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Finnish-NLP/mc4_fi_cleaned | Finnish-NLP | 2022-10-21T16:57:34Z | 282 | 3 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|mc4",
"language:fi",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators: []
language_creators: []
language:
- fi
license: []
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- extended|mc4
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
pretty_name: mC4 Finnish Cleaned
---
# Dataset Card for mC4 Finnish Cleaned
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
mC4 Finnish cleaned is cleaned version of the original mC4 Finnish split.
### Supported Tasks and Leaderboards
mC4 Finnish is mainly intended to pretrain Finnish language models and word representations.
### Languages
Finnish
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
The data have several fields:
- url: url of the source as a string
- text: text content as a string
- timestamp: timestamp as a string
- perplexity_kenlm_full: perplexity of the text calculated by KenLM model
### Data Splits
Train Validation
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | [
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|
GroNLP/ik-nlp-22_transqe | GroNLP | 2022-10-21T08:06:50Z | 282 | 0 | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:extended|esnli",
"language:en",
"language:nl",
"license:apache-2.0",
"quality-estimation",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- machine-generated
language:
- en
- nl
license:
- apache-2.0
multilinguality:
- translation
size_categories:
- unknown
source_datasets:
- extended|esnli
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: iknlp22-transqe
tags:
- quality-estimation
---
# Dataset Card for IK-NLP-22 Project 3: Translation Quality-driven Data Selection for Natural Language Inference
## Table of Contents
- [Dataset Card for IK-NLP-22 Project 3: Translation Quality-driven Data Selection for Natural Language Inference](#dataset-card-for-ik-nlp-22-project-3-translation-quality-driven-data-selection-for-natural-language-inference)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Splits](#data-splits)
- [Data Example](#data-example)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Source:** [Github](https://github.com/OanaMariaCamburu/e-SNLI)
- **Point of Contact:** [Gabriele Sarti](mailto:[email protected])
### Dataset Summary
This dataset contains the full [e-SNLI](https://huggingface.co/datasets/esnli) dataset, automatically translated to Dutch using the [Helsinki-NLP/opus-mt-en-nl](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl) neural machine translation model. The translation of each field has been anotated with two quality estimation scores using the referenceless version of the [COMET](https://github.com/Unbabel/COMET/) metric by Unbabel.
The intended usage of this corpus is restricted to the scope of final project for the 2022 edition of the Natural Language Processing course at the Information Science Master's Degree (IK) at the University of Groningen, taught by [Arianna Bisazza](https://research.rug.nl/en/persons/arianna-bisazza) and [Gabriele Sarti](https://research.rug.nl/en/persons/gabriele-sarti), with the assistance of [Anjali Nair](https://nl.linkedin.com/in/anjalinair012).
*The e-SNLI corpus was made freely available by the authors on Github. The present dataset was created for educational purposes, and is based on the original e-SNLI dataset by Camburu et al..All rights of the present contents are attributed to the original authors.*
### Languages
The language data of this corpus is in English (BCP-47 `en`) and Dutch (BCP-47 `nl`).
## Dataset Structure
### Data Instances
The dataset contains a single condiguration by default, named `plain_text`, with the three original splits `train`, `validation` and `test`. Every split contains the following fields:
| **Field** | **Description** |
|------------|-----------------------------|
|`premise_en`| The original English premise.|
|`premise_nl`| The premise automatically translated to Dutch.|
|`hypothesis_en`| The original English hypothesis.|
|`hypothesis_nl`| The hypothesis automatically translated to Dutch.|
|`label`| The label of the data instance (0 for entailment, 1 for neutral, 2 for contradiction).|
|`explanation_1_en`| The first explanation for the assigned label in English.|
|`explanation_1_nl`| The first explanation automatically translated to Dutch.|
|`explanation_2_en`| The second explanation for the assigned label in English.|
|`explanation_2_nl`| The second explanation automatically translated to Dutch.|
|`explanation_3_en`| The third explanation for the assigned label in English.|
|`explanation_3_nl`| The third explanation automatically translated to Dutch.|
|`da_premise`| The quality estimation produced by the `wmt20-comet-qe-da` model for the premise translation.|
|`da_hypothesis`| The quality estimation produced by the `wmt20-comet-qe-da` model for the hypothesis translation.|
|`da_explanation_1`| The quality estimation produced by the `wmt20-comet-qe-da` model for the first explanation translation.|
|`da_explanation_2`| The quality estimation produced by the `wmt20-comet-qe-da` model for the second explanation translation.|
|`da_explanation_3`| The quality estimation produced by the `wmt20-comet-qe-da` model for the third explanation translation.|
|`mqm_premise`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the premise translation.|
|`mqm_hypothesis`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the hypothesis translation.|
|`mqm_explanation_1`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the first explanation translation.|
|`mqm_explanation_2`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the second explanation translation.|
|`mqm_explanation_3`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the third explanation translation.|
Explanation 2 and 3 and related quality estimation scores are only present in the `validation` and `test` splits.
### Data Splits
| config| train | validation | test |
|------------:|---------|------------|------|
|`plain_text` | 549'367 | 9842 | 9824 |
For your analyses, use the amount of data that is the most reasonable for your computational setup. The more, the better.
### Data Example
The following is an example of entry 2000 taken from the `test` split:
```json
{
"premise_en": "A young woman wearing a yellow sweater and black pants is ice skating outdoors.",
"premise_nl": "Een jonge vrouw met een gele trui en zwarte broek schaatst buiten.",
"hypothesis_en": "a woman is practicing for the olympics",
"hypothesis_nl": "een vrouw oefent voor de Olympische Spelen",
"label": 1,
"explanation_1_en": "You can not infer it's for the Olympics.",
"explanation_1_nl": "Het is niet voor de Olympische Spelen.",
"explanation_2_en": "Just because a girl is skating outdoors does not mean she is practicing for the Olympics.",
"explanation_2_nl": "Alleen omdat een meisje buiten schaatst betekent niet dat ze oefent voor de Olympische Spelen.",
"explanation_3_en": "Ice skating doesn't imply practicing for the olympics.",
"explanation_3_nl": "Schaatsen betekent niet oefenen voor de Olympische Spelen.",
"da_premise": "0.6099",
"mqm_premise": "0.1298",
"da_hypothesis": "0.8504",
"mqm_hypothesis": "0.1521",
"da_explanation_1": "0.0001",
"mqm_explanation_1": "0.1237",
"da_explanation_2": "0.4017",
"mqm_explanation_2": "0.1467",
"da_explanation_3": "0.6069",
"mqm_explanation_3": "0.1389"
}
```
### Dataset Creation
The dataset was created through the following steps:
- Translating every field of the original e-SNLI corpus to Dutch using the [Helsinki-NLP/opus-mt-en-nl](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl) neural machine translation model.
- Annotating the quality estimation of the translations with two referenceless versions of the [COMET](https://github.com/Unbabel/COMET/) metric by Unbabel.
## Additional Information
### Dataset Curators
For problems on this 🤗 Datasets version, please contact us at [[email protected]](mailto:[email protected]).
### Licensing Information
The dataset is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.html).
### Citation Information
Please cite the authors if you use these corpora in your work:
```bibtex
@incollection{NIPS2018_8163,
title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {9539--9549},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}
}
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|
open-llm-leaderboard/details_georgesung__llama2_7b_chat_uncensored | open-llm-leaderboard | 2023-09-17T06:01:46Z | 282 | 0 | [
"region:us"
]
| null | 2023-08-18T11:08:31Z | ---
pretty_name: Evaluation run of georgesung/llama2_7b_chat_uncensored
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [georgesung/llama2_7b_chat_uncensored](https://huggingface.co/georgesung/llama2_7b_chat_uncensored)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_georgesung__llama2_7b_chat_uncensored\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T06:01:34.534802](https://huggingface.co/datasets/open-llm-leaderboard/details_georgesung__llama2_7b_chat_uncensored/blob/main/results_2023-09-17T06-01-34.534802.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0018875838926174498,\n\
\ \"em_stderr\": 0.0004445109990558761,\n \"f1\": 0.05687290268456382,\n\
\ \"f1_stderr\": 0.0013311620250832507,\n \"acc\": 0.3997491582259886,\n\
\ \"acc_stderr\": 0.009384299684412923\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0018875838926174498,\n \"em_stderr\": 0.0004445109990558761,\n\
\ \"f1\": 0.05687290268456382,\n \"f1_stderr\": 0.0013311620250832507\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.058377558756633814,\n \
\ \"acc_stderr\": 0.0064580835578324685\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7411207576953434,\n \"acc_stderr\": 0.012310515810993376\n\
\ }\n}\n```"
repo_url: https://huggingface.co/georgesung/llama2_7b_chat_uncensored
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|arc:challenge|25_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T06_01_34.534802
path:
- '**/details_harness|drop|3_2023-09-17T06-01-34.534802.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T06-01-34.534802.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T06_01_34.534802
path:
- '**/details_harness|gsm8k|5_2023-09-17T06-01-34.534802.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T06-01-34.534802.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hellaswag|10_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:17:24.189192.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T11:17:24.189192.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T11:17:24.189192.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T06_01_34.534802
path:
- '**/details_harness|winogrande|5_2023-09-17T06-01-34.534802.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T06-01-34.534802.parquet'
- config_name: results
data_files:
- split: 2023_07_24T11_17_24.189192
path:
- results_2023-07-24T11:17:24.189192.parquet
- split: 2023_09_17T06_01_34.534802
path:
- results_2023-09-17T06-01-34.534802.parquet
- split: latest
path:
- results_2023-09-17T06-01-34.534802.parquet
---
# Dataset Card for Evaluation run of georgesung/llama2_7b_chat_uncensored
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/georgesung/llama2_7b_chat_uncensored
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [georgesung/llama2_7b_chat_uncensored](https://huggingface.co/georgesung/llama2_7b_chat_uncensored) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_georgesung__llama2_7b_chat_uncensored",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T06:01:34.534802](https://huggingface.co/datasets/open-llm-leaderboard/details_georgesung__llama2_7b_chat_uncensored/blob/main/results_2023-09-17T06-01-34.534802.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0018875838926174498,
"em_stderr": 0.0004445109990558761,
"f1": 0.05687290268456382,
"f1_stderr": 0.0013311620250832507,
"acc": 0.3997491582259886,
"acc_stderr": 0.009384299684412923
},
"harness|drop|3": {
"em": 0.0018875838926174498,
"em_stderr": 0.0004445109990558761,
"f1": 0.05687290268456382,
"f1_stderr": 0.0013311620250832507
},
"harness|gsm8k|5": {
"acc": 0.058377558756633814,
"acc_stderr": 0.0064580835578324685
},
"harness|winogrande|5": {
"acc": 0.7411207576953434,
"acc_stderr": 0.012310515810993376
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
gilkeyio/AudioMNIST | gilkeyio | 2023-11-22T15:28:13Z | 282 | 0 | [
"task_categories:audio-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"arxiv:1807.03418",
"region:us"
]
| [
"audio-classification"
]
| 2023-11-09T19:04:43Z | ---
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- audio-classification
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: speaker_id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: digit
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
- name: gender
dtype:
class_label:
names:
'0': male
'1': female
- name: accent
dtype: string
- name: age
dtype: int64
- name: native_speaker
dtype: bool
- name: origin
dtype: string
splits:
- name: train
num_bytes: 1493209727.0
num_examples: 24000
- name: test
num_bytes: 360966680.0
num_examples: 6000
download_size: 1483680961
dataset_size: 1854176407.0
---
# Dataset Card for "AudioMNIST"
The [audioMNIST](https://github.com/soerenab/AudioMNIST) dataset has 50 English recordings per digit (0-9) of 60 speakers.
There are 60 participants in total, with 12 being women and 48 being men, all featuring a diverse range of accents and country of origin. Their ages vary from 22 to 61 years old. This is a great dataset to explore a simple audio classification problem: either the digit or the gender.
## Bias, Risks, and Limitations
* The genders represented in the dataset are unbalanced, with around 80% being men.
* The majority of the speakers, around 70%, have a German accent
### Citation Information
The original creators of the dataset ask you to cite [their paper](https://arxiv.org/abs/1807.03418) if you use this data:
```
@ARTICLE{becker2018interpreting,
author = {Becker, S\"oren and Ackermann, Marcel and Lapuschkin, Sebastian and M\"uller, Klaus-Robert and Samek, Wojciech},
title = {Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals},
journal = {CoRR},
volume = {abs/1807.03418},
year = {2018},
archivePrefix = {arXiv},
eprint = {1807.03418},
}
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|
nguyenphuthien/vietnamese_ultrachat_200k | nguyenphuthien | 2023-11-14T10:46:50Z | 282 | 0 | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:vi",
"license:mit",
"arxiv:2305.14233",
"region:us"
]
| [
"conversational",
"text-generation"
]
| 2023-11-14T09:27:48Z | ---
language:
- vi
license: mit
size_categories:
- 100K<n<1M
task_categories:
- conversational
- text-generation
pretty_name: Vietnamese UltraChat 200k
---
# Dataset Card for Vietnamese UltraChat 200k
## Dataset Description
This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model.
The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic:
- Selection of a subset of data for faster supervised fine tuning.
- Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?"
- Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either.
The Dataset has been translated to Vietnamese by Google translate
## Dataset Structure
The dataset has two splits, suitable for:
* Supervised fine-tuning (`sft`).
The number of examples per split is shown as follows:
| train_sft | test_sft |
|:-------:|:-----------:|
| 207834 | 23107 |
The dataset is stored in parquet format with each entry using the following schema:
```
{
"prompt": "Có thể kết hợp ngăn kéo, tủ đựng chén và giá đựng rượu trong cùng một tủ búp phê không?: ...",
"messages":[
{
"role": "user",
"content": "Có thể kết hợp ngăn kéo, tủ đựng chén và giá đựng rượu trong cùng một tủ búp phê không?: ...",
},
{
"role": "assistant",
"content": "Có, có thể kết hợp ngăn kéo, tủ đựng chén và giá để rượu trong cùng một chiếc tủ. Tủ búp phê Hand Made ...",
},
{
"role": "user",
"content": "Bạn có thể cung cấp cho tôi thông tin liên hệ của người bán tủ búp phê Hand Made được không? ...",
},
{
"role": "assistant",
"content": "Tôi không có quyền truy cập vào thông tin cụ thể về người bán hoặc thông tin liên hệ của họ. ...",
},
{
"role": "user",
"content": "Bạn có thể cung cấp cho tôi một số ví dụ về các loại bàn làm việc khác nhau có sẵn cho tủ búp phê Hand Made không?",
},
{
"role": "assistant",
"content": "Chắc chắn, đây là một số ví dụ về các mặt bàn làm việc khác nhau có sẵn cho tủ búp phê Hand Made: ...",
},
],
"prompt_id": "0ee8332c26405af5457b3c33398052b86723c33639f472dd6bfec7417af38692"
}
```
## Citation
If you find this dataset is useful in your work, please cite the original UltraChat dataset:
```
@misc{ding2023enhancing,
title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations},
author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou},
year={2023},
eprint={2305.14233},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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|
mutual_friends | null | 2022-11-18T21:31:53Z | 281 | 2 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1704.07130",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: mutualfriends
pretty_name: MutualFriends
dataset_info:
features:
- name: uuid
dtype: string
- name: scenario_uuid
dtype: string
- name: scenario_alphas
sequence: float32
- name: scenario_attributes
sequence:
- name: unique
dtype: bool_
- name: value_type
dtype: string
- name: name
dtype: string
- name: scenario_kbs
sequence:
sequence:
sequence:
sequence: string
- name: agents
struct:
- name: '1'
dtype: string
- name: '0'
dtype: string
- name: outcome_reward
dtype: int32
- name: events
struct:
- name: actions
sequence: string
- name: start_times
sequence: float32
- name: data_messages
sequence: string
- name: data_selects
sequence:
- name: attributes
sequence: string
- name: values
sequence: string
- name: agents
sequence: int32
- name: times
sequence: float32
config_name: plain_text
splits:
- name: train
num_bytes: 26979472
num_examples: 8967
- name: test
num_bytes: 3327158
num_examples: 1107
- name: validation
num_bytes: 3267881
num_examples: 1083
download_size: 41274578
dataset_size: 33574511
---
# Dataset Card for MutualFriends
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [COCOA](https://stanfordnlp.github.io/cocoa/)
- **Repository:** [Github repository](https://github.com/stanfordnlp/cocoa)
- **Paper:** [Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings (ACL 2017)](https://arxiv.org/abs/1704.07130)
- **Codalab**: [Codalab](https://worksheets.codalab.org/worksheets/0xc757f29f5c794e5eb7bfa8ca9c945573/)
### Dataset Summary
Our goal is to build systems that collaborate with people by exchanging information through natural language and reasoning over structured knowledge base. In the MutualFriend task, two agents, A and B, each have a private knowledge base, which contains a list of friends with multiple attributes (e.g., name, school, major, etc.). The agents must chat with each other to find their unique mutual friend.
### Supported Tasks and Leaderboards
We consider two agents, each with a private knowledge base of items, who must communicate their knowledge to achieve a common goal. Specifically, we designed the MutualFriends task (see the figure below). Each agent has a list of friends with attributes like school, major etc. They must chat with each other to find the unique mutual friend.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
An example looks like this.
```
{
'uuid': 'C_423324a5fff045d78bef75a6f295a3f4'
'scenario_uuid': 'S_hvmRM4YNJd55ecT5',
'scenario_alphas': [0.30000001192092896, 1.0, 1.0],
'scenario_attributes': {
'name': ['School', 'Company', 'Location Preference'],
'unique': [False, False, False],
'value_type': ['school', 'company', 'loc_pref']
},
'scenario_kbs': [
[
[['School', 'Company', 'Location Preference'], ['Longwood College', 'Alton Steel', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Salisbury State University', 'Leonard Green & Partners', 'indoor']],
[['School', 'Company', 'Location Preference'], ['New Mexico Highlands University', 'Crazy Eddie', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Rhodes College', "Tully's Coffee", 'indoor']],
[['School', 'Company', 'Location Preference'], ['Sacred Heart University', 'AMR Corporation', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Salisbury State University', 'Molycorp', 'indoor']],
[['School', 'Company', 'Location Preference'], ['New Mexico Highlands University', 'The Hartford Financial Services Group', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Sacred Heart University', 'Molycorp', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Babson College', 'The Hartford Financial Services Group', 'indoor']]
],
[
[['School', 'Company', 'Location Preference'], ['National Technological University', 'Molycorp', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Fairmont State College', 'Leonard Green & Partners', 'outdoor']],
[['School', 'Company', 'Location Preference'], ['Johnson C. Smith University', 'Data Resources Inc.', 'outdoor']],
[['School', 'Company', 'Location Preference'], ['Salisbury State University', 'Molycorp', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Fairmont State College', 'Molycorp', 'outdoor']],
[['School', 'Company', 'Location Preference'], ['University of South Carolina - Aiken', 'Molycorp', 'indoor']],
[['School', 'Company', 'Location Preference'], ['University of South Carolina - Aiken', 'STX', 'outdoor']],
[['School', 'Company', 'Location Preference'], ['National Technological University', 'STX', 'outdoor']],
[['School', 'Company', 'Location Preference'], ['Johnson C. Smith University', 'Rockstar Games', 'indoor']]
]
],
'agents': {
'0': 'human',
'1': 'human'
},
'outcome_reward': 1,
'events': {
'actions': ['message', 'message', 'message', 'message', 'select', 'select'],
'agents': [1, 1, 0, 0, 1, 0],
'data_messages': ['Hello', 'Do you know anyone who works at Molycorp?', 'Hi. All of my friends like the indoors.', 'Ihave two friends that work at Molycorp. They went to Salisbury and Sacred Heart.', '', ''],
'data_selects': {
'attributes': [
[], [], [], [], ['School', 'Company', 'Location Preference'], ['School', 'Company', 'Location Preference']
],
'values': [
[], [], [], [], ['Salisbury State University', 'Molycorp', 'indoor'], ['Salisbury State University', 'Molycorp', 'indoor']
]
},
'start_times': [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0],
'times': [1480737280.0, 1480737280.0, 1480737280.0, 1480737280.0, 1480737280.0, 1480737280.0]
},
}
```
### Data Fields
- `uuid`: example id.
- `scenario_uuid`: scenario id.
- `scenario_alphas`: scenario alphas.
- `scenario_attributes`: all the attributes considered in the scenario. The dictionaries are liniearized: to reconstruct the dictionary of attribute i-th, one should extract the i-th elements of `unique`, `value_type` and `name`.
- `unique`: bool.
- `value_type`: code/type of the attribute.
- `name`: name of the attribute.
- `scenario_kbs`: descriptions of the persons present in the two users' databases. List of two (one for each user in the dialogue). `scenario_kbs[i]` is a list of persons. Each person is represented as two lists (one for attribute names and the other for attribute values). The j-th element of attribute names corresponds to the j-th element of attribute values (linearized dictionary).
- `agents`: the two users engaged in the dialogue.
- `outcome_reward`: reward of the present dialogue.
- `events`: dictionary describing the dialogue. The j-th element of each sub-element of the dictionary describes the turn along the axis of the sub-element.
- `actions`: type of turn (either `message` or `select`).
- `agents`: who is talking? Agent 1 or 0?
- `data_messages`: the string exchanged if `action==message`. Otherwise, empty string.
- `data_selects`: selection of the user if `action==select`. Otherwise, empty selection/dictionary.
- `start_times`: always -1 in these data.
- `times`: sending time.
### Data Splits
There are 8967 dialogues for training, 1083 for validation and 1107 for testing.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{he-etal-2017-learning,
title = "Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings",
author = "He, He and
Balakrishnan, Anusha and
Eric, Mihail and
Liang, Percy",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P17-1162",
doi = "10.18653/v1/P17-1162",
pages = "1766--1776",
abstract = "We study a \textit{symmetric collaborative dialogue} setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.",
}
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. | [
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|
opus_finlex | null | 2022-11-03T16:08:11Z | 281 | 1 | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:fi",
"language:sv",
"license:unknown",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- fi
- sv
license:
- unknown
multilinguality:
- translation
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusFinlex
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- fi
- sv
config_name: fi-sv
splits:
- name: train
num_bytes: 610550215
num_examples: 3114141
download_size: 153886554
dataset_size: 610550215
---
# Dataset Card for [opus_finlex]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[Finlex](http://opus.nlpl.eu/Finlex.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Finlex Data Base is a comprehensive collection of legislative and other judicial information of Finland, which is available in Finnish, Swedish and partially in English. This corpus is taken from the Semantic Finlex serice that provides the Finnish and Swedish data as linked open data and also raw XML files.
### Supported Tasks and Leaderboards
The underlying task is machine translation for language pair Swedish and Finnish.
### Languages
Swedish and Finnish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. | [
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|
times_of_india_news_headlines | null | 2022-11-03T16:15:42Z | 281 | 0 | [
"task_categories:text2text-generation",
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"task_ids:fact-checking-retrieval",
"task_ids:text-simplification",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"region:us"
]
| [
"text2text-generation",
"text-retrieval"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text2text-generation
- text-retrieval
task_ids:
- document-retrieval
- fact-checking-retrieval
- text-simplification
paperswithcode_id: null
pretty_name: Times of India News Headlines
dataset_info:
features:
- name: publish_date
dtype: string
- name: headline_category
dtype: string
- name: headline_text
dtype: string
splits:
- name: train
num_bytes: 260939306
num_examples: 3297173
download_size: 0
dataset_size: 260939306
---
# Dataset Card for Times of India News Headlines
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/J7BYRX
- **Repository:** [More Information Needed]
- **Paper:** [More Information Needed]
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
This news dataset is a persistent historical archive of noteable events in the Indian subcontinent from start-2001 to mid-2020, recorded in realtime by the journalists of India. It contains approximately 3.3 million events published by Times of India. Times Group as a news agency, reaches out a very wide audience across Asia and drawfs every other agency in the quantity of english articles published per day. Due to the heavy daily volume over multiple years, this data offers a deep insight into Indian society, its priorities, events, issues and talking points and how they have unfolded over time. It is possible to chop this dataset into a smaller piece for a more focused analysis, based on one or more facets.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
```
{
'publish_date': '20010530',
'headline_category': city.kolkata,
'headline_text': "Malda fake notes"
}
```
### Data Fields
- `publish_date`: Date of publishing in yyyyMMdd format
- `headline_category`: Category of event in ascii, dot-delimited values
- `headline_text`: Headline of article en la Engrezi (2020-07-10)
### Data Splits
This dataset has no splits.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was created by Rohit Kulkarni.
### Licensing Information
The data is under the [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
```
@data{DVN/DPQMQH_2020,
author = {Kulkarni, Rohit},
publisher = {Harvard Dataverse},
title = {{Times of India News Headlines}},
year = {2020},
version = {V1},
doi = {10.7910/DVN/DPQMQH},
url = {https://doi.org/10.7910/DVN/DPQMQH}
}
```
### Contributions
Thanks to [@tanmoyio](https://github.com/tanmoyio) for adding this dataset. | [
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|
turkish_movie_sentiment | null | 2022-11-03T16:07:48Z | 281 | 3 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:tr",
"license:unknown",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- tr
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- sentiment-scoring
paperswithcode_id: null
pretty_name: 'TurkishMovieSentiment: This dataset contains turkish movie reviews.'
dataset_info:
features:
- name: point
dtype: float32
- name: comment
dtype: string
- name: film_name
dtype: string
config_name: turkishmoviesentiment
splits:
- name: train
num_bytes: 33954560
num_examples: 83227
download_size: 0
dataset_size: 33954560
---
# Dataset Card for TurkishMovieSentiment: This dataset contains turkish movie reviews.
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.kaggle.com/mustfkeskin/turkish-movie-sentiment-analysis-dataset/tasks](https://www.kaggle.com/mustfkeskin/turkish-movie-sentiment-analysis-dataset/tasks)
- **Point of Contact:** [Mustafa Keskin](https://www.linkedin.com/in/mustfkeskin/)
### Dataset Summary
This data set is a dataset from kaggle consisting of Turkish movie reviews and scored between 0-5.
### Languages
The dataset is based on Turkish.
## Dataset Structure
### Data Instances
**Example 1:**
**Comment:** Jean Reno denince zaten leon filmi gelir akla izlemeyen kalmamıştır ama kaldıysada ee ne duruyorsun hemen izle :),
**Film_name:** Sevginin Gücü,
**Point:** 5,0
**Example 2:**
**Comment:** Bence güzel bi film olmush.İzlenmeli.İnsana şükretmek gerektini hatırlatıyor.Ama cok da poh pohlanacak bi sey yapmamıslar,
**Film_name:** Cinderella Man,
**Point:** 2,5
### Data Fields
- **comment**(string) : Contatins turkish movie review
- **film_name**(string) : Film name in Turkish.
- **point**(float) : [0-5] floating point
### Data Splits
It is not divided into Train set and Test set.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Discussion of Social Impact and Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was created by [Mustafa Keskin](https://www.linkedin.com/in/mustfkeskin/).
### Licensing Information
The data is under the [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@yavuzKomecoglu](https://github.com/yavuzKomecoglu) for adding this dataset. | [
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|
DanL/scientific-challenges-and-directions-dataset | DanL | 2022-10-25T08:56:00Z | 281 | 3 | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:CORD-19",
"language:en",
"arxiv:2108.13751",
"arxiv:2004.10706",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
YAML tags:
annotations_creators:
- expert-generated
language_creators: []
language:
- en
license: []
multilinguality:
- monolingual
pretty_name: DanL/scientific-challenges-and-directions-dataset
source_datasets:
- CORD-19
task_categories:
- text-classification
task_ids:
- multi-label-classification
---
# Dataset Card for scientific-challenges-and-directions
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository: [repo](https://github.com/Dan-La/scientific-challenges-and-directions)**
- **Paper: [A Search Engine for Discovery of Scientific Challenges and Directions](https://arxiv.org/abs/2108.13751)**
- **Point of Contact: [email protected],[email protected]**
### Dataset Summary
The scientific challenges and directions dataset is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the [CORD-19](https://arxiv.org/abs/2004.10706) corpus, labeled for classification of _challenges_ and _directions_ by expert annotators with biomedical and bioNLP backgrounds.
At a high level, our labels are defined as follows:
* **Challenge**: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap.
* **Research direction**: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration.
The dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature.
### Languages
The language in the dataset is English as written by authors of the scientific papers in the CORD-19 corpus.
## Dataset Structure
### Data Instances
For each instance, there is a unique id, a string for the text sentence, a string for the previous sentence, a string for the next sentence, and a list for the challenge and direction labels.
```
{'id': 'PMC7152165_152',
'label': [0.0, 0.0],
'next_sent': 'The railways brought a new technology and vast engineering and architectural structures into Britain’s rural and urban landscapes.',
'prev_sent': 'In Britain, improvements in coaching technologies and roads helped to increase stage coach speeds in the late eighteenth and early nineteenth centuries, while the railway construction boom of the 1830s and 1840s led to a massive reduction in journey times, and the emergence of distinctly new experiences and geographies.',
'text': 'Britain’s railway companies were among the nation’s largest employers in the nineteenth century, and they facilitated the mobility of passengers and important commodities.'}
```
### Data Fields
* id: A string as a unique id for the instance. The id is composed of the unique PMC id of the paper, an underscore, and the index of the sentence within the paper.
* next_sent_: A string of a sentence that is following the _text_ of the instance. If the text is the first in its paragraph the string is saved as '|'.
* prev_sent_: A string of a sentence that is preceding the _text_ of the instance. If the text is the first in its paragraph the string is saved as '|'.
* text: A string of the sentence we seek to classify.
* label: A list of 2 values - the first is the label for _challenge_ and the last of _direction_. Each value may be either 0, indicating that the _text_ is **not** _challenge_ or _direction_, or 1, indicating that the the _text_ is _challenge_ or _direction_. Each instance can be a _challenge_, a _direction_, both, or neither.
### Data Splits
The scientific-challenges-and-directions dataset has 3 splits: _train_, _dev_, and _test_. Each instances shows up in only one split. The splits are stratified with no overlap in papers.
| Labels | Train | Dev | Test | All |
|:----------------------------:|:------:|:-----:|:----:|:----:|
| Not Challenge, Not Direction | 602 | 146 | 745 | 1493 |
| Not Challenge, Direction | 106 | 25 | 122 | 253 |
| Challenge, Not Direction | 288 | 73 | 382 | 743 |
| Challenge, Direction | 155 | 40 | 210 | 405 |
## Dataset Creation
### Curation Rationale
The resource was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research.
### Source Data
#### Initial Data Collection and Normalization
See section 3.1 in our [paper](https://arxiv.org/abs/2108.13751).
#### Who are the source language producers?
The authors of the subset of full-text papers in the [CORD-19 dataset](https://arxiv.org/abs/2004.10706), which at the time of creating our dataset included roughly 180K documents.
### Annotations
#### Annotation process
See section 3.1 in our [paper](https://arxiv.org/abs/2108.13751).
#### Who are the annotators?
Four expert annotators with biomedical and bioNLP backgrounds. For more details see section 3.1 in our [paper](https://arxiv.org/abs/2108.13751).
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or annotators.
## Considerations for Using the Data
### Social Impact of Dataset
As mentioned, the dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research.
Studies were conducted to evaluate the utility of the dataset for researchers and medical professionals, in which a prototype based on the dataset was found to outperform other biomedical search tools. For more details see section 4 in our [paper](https://arxiv.org/abs/2108.13751).
This dataset was also developed for evaluating representational systems for scientific text classification and can be used as such.
### Discussion of Biases
The source of the dataset is the full-text papers in the [CORD-19 dataset](https://arxiv.org/abs/2004.10706), so biases in CORD-19 may be replicated to our dataset.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
The dataset was developed by Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld and Tom Hope as part of _Tel Aviv University_, the _Allen Institute for AI_, _University of Washington_, _Georgia Institute of Technology_, _Microsoft_ and _Swedish Medical Group_.
It was supported by the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University, ONR grant N00014-18-1-2193, NSF RAPID grant 2040196, the WR-F/Cable Professorship, and AI2.
### Licensing Information
[More Information Needed]
### Citation Information
If using our dataset and models, please cite:
```
@misc{lahav2021search,
title={A Search Engine for Discovery of Scientific Challenges and Directions},
author={Dan Lahav and Jon Saad Falcon and Bailey Kuehl and Sophie Johnson and Sravanthi Parasa and Noam Shomron and Duen Horng Chau and Diyi Yang and Eric Horvitz and Daniel S. Weld and Tom Hope},
year={2021},
eprint={2108.13751},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@Dan-La](https://github.com/Dan-La) and [@tomhoper](https://github.com/tomhoper) for adding this dataset.
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|
huggingartists/metallica | huggingartists | 2022-10-25T09:38:20Z | 281 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/metallica"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.6616 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f2d983ad882fc80979d95ef031e82bc5.999x999x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/metallica">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Metallica</div>
<a href="https://genius.com/artists/metallica">
<div style="text-align: center; font-size: 14px;">@metallica</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/metallica).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/metallica")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|469| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/metallica")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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|
laion/laion400m | laion | 2023-04-04T06:35:23Z | 281 | 18 | [
"license:cc-by-4.0",
"region:us"
]
| null | 2023-03-28T21:36:09Z | ---
license: cc-by-4.0
---
# LAION-400m_new
This datasets has two improvements compared to original LAION_400m dataset:
1. It uses a multilingual text filter to filter out malicious content
2. The better open_clip VitH model was used to detect potential harmful content in the images
All in all, we filtered out around 6 million additional image-text pairs - probably with a high false positive rate - in order to improve dataset safety. | [
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|
open-llm-leaderboard/details_Locutusque__gpt2-conversational-or-qa | open-llm-leaderboard | 2023-09-17T06:39:50Z | 281 | 0 | [
"region:us"
]
| null | 2023-08-18T00:20:27Z | ---
pretty_name: Evaluation run of Locutusque/gpt2-conversational-or-qa
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Locutusque/gpt2-conversational-or-qa](https://huggingface.co/Locutusque/gpt2-conversational-or-qa)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Locutusque__gpt2-conversational-or-qa\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T06:39:40.166876](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__gpt2-conversational-or-qa/blob/main/results_2023-09-17T06-39-40.166876.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.00041946308724832214,\n\
\ \"em_stderr\": 0.00020969854707829385,\n \"f1\": 0.015460360738255055,\n\
\ \"f1_stderr\": 0.0006333702020804492,\n \"acc\": 0.25610125343097334,\n\
\ \"acc_stderr\": 0.007403477156790923\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.00041946308724832214,\n \"em_stderr\": 0.00020969854707829385,\n\
\ \"f1\": 0.015460360738255055,\n \"f1_stderr\": 0.0006333702020804492\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \
\ \"acc_stderr\": 0.0007581501137225174\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5114443567482242,\n \"acc_stderr\": 0.014048804199859329\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Locutusque/gpt2-conversational-or-qa
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|arc:challenge|25_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T06_39_40.166876
path:
- '**/details_harness|drop|3_2023-09-17T06-39-40.166876.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T06-39-40.166876.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T06_39_40.166876
path:
- '**/details_harness|gsm8k|5_2023-09-17T06-39-40.166876.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T06-39-40.166876.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hellaswag|10_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T16:08:01.149355.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T16:08:01.149355.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T16:08:01.149355.parquet'
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- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T16:08:01.149355.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-18T16:08:01.149355.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-18T16:08:01.149355.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T06_39_40.166876
path:
- '**/details_harness|winogrande|5_2023-09-17T06-39-40.166876.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T06-39-40.166876.parquet'
- config_name: results
data_files:
- split: 2023_07_18T16_08_01.149355
path:
- results_2023-07-18T16:08:01.149355.parquet
- split: 2023_09_17T06_39_40.166876
path:
- results_2023-09-17T06-39-40.166876.parquet
- split: latest
path:
- results_2023-09-17T06-39-40.166876.parquet
---
# Dataset Card for Evaluation run of Locutusque/gpt2-conversational-or-qa
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Locutusque/gpt2-conversational-or-qa
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Locutusque/gpt2-conversational-or-qa](https://huggingface.co/Locutusque/gpt2-conversational-or-qa) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Locutusque__gpt2-conversational-or-qa",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T06:39:40.166876](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__gpt2-conversational-or-qa/blob/main/results_2023-09-17T06-39-40.166876.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.00041946308724832214,
"em_stderr": 0.00020969854707829385,
"f1": 0.015460360738255055,
"f1_stderr": 0.0006333702020804492,
"acc": 0.25610125343097334,
"acc_stderr": 0.007403477156790923
},
"harness|drop|3": {
"em": 0.00041946308724832214,
"em_stderr": 0.00020969854707829385,
"f1": 0.015460360738255055,
"f1_stderr": 0.0006333702020804492
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225174
},
"harness|winogrande|5": {
"acc": 0.5114443567482242,
"acc_stderr": 0.014048804199859329
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_nthngdy__pythia-owt2-70m-100k | open-llm-leaderboard | 2023-09-16T16:58:29Z | 281 | 0 | [
"region:us"
]
| null | 2023-08-18T11:06:21Z | ---
pretty_name: Evaluation run of nthngdy/pythia-owt2-70m-100k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [nthngdy/pythia-owt2-70m-100k](https://huggingface.co/nthngdy/pythia-owt2-70m-100k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_nthngdy__pythia-owt2-70m-100k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-16T16:58:18.157087](https://huggingface.co/datasets/open-llm-leaderboard/details_nthngdy__pythia-owt2-70m-100k/blob/main/results_2023-09-16T16-58-18.157087.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.01960989932885906,\n\
\ \"em_stderr\": 0.00141996222824606,\n \"f1\": 0.0546665268456376,\n\
\ \"f1_stderr\": 0.0018294405855806455,\n \"acc\": 0.26637726913970006,\n\
\ \"acc_stderr\": 0.007011150285217067\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.01960989932885906,\n \"em_stderr\": 0.00141996222824606,\n\
\ \"f1\": 0.0546665268456376,\n \"f1_stderr\": 0.0018294405855806455\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5327545382794001,\n\
\ \"acc_stderr\": 0.014022300570434134\n }\n}\n```"
repo_url: https://huggingface.co/nthngdy/pythia-owt2-70m-100k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|arc:challenge|25_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_16T16_58_18.157087
path:
- '**/details_harness|drop|3_2023-09-16T16-58-18.157087.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-16T16-58-18.157087.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_16T16_58_18.157087
path:
- '**/details_harness|gsm8k|5_2023-09-16T16-58-18.157087.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-16T16-58-18.157087.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hellaswag|10_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:34:55.847761.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_philosophy_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T13_34_55.847761
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:34:55.847761.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:34:55.847761.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_security_studies_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_sociology_5
data_files:
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data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_virology_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_world_religions_5
data_files:
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path:
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path:
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data_files:
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path:
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path:
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- config_name: harness_winogrande_5
data_files:
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path:
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path:
- '**/details_harness|winogrande|5_2023-09-16T16-58-18.157087.parquet'
- config_name: results
data_files:
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path:
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- split: 2023_09_16T16_58_18.157087
path:
- results_2023-09-16T16-58-18.157087.parquet
- split: latest
path:
- results_2023-09-16T16-58-18.157087.parquet
---
# Dataset Card for Evaluation run of nthngdy/pythia-owt2-70m-100k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/nthngdy/pythia-owt2-70m-100k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [nthngdy/pythia-owt2-70m-100k](https://huggingface.co/nthngdy/pythia-owt2-70m-100k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_nthngdy__pythia-owt2-70m-100k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-16T16:58:18.157087](https://huggingface.co/datasets/open-llm-leaderboard/details_nthngdy__pythia-owt2-70m-100k/blob/main/results_2023-09-16T16-58-18.157087.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.01960989932885906,
"em_stderr": 0.00141996222824606,
"f1": 0.0546665268456376,
"f1_stderr": 0.0018294405855806455,
"acc": 0.26637726913970006,
"acc_stderr": 0.007011150285217067
},
"harness|drop|3": {
"em": 0.01960989932885906,
"em_stderr": 0.00141996222824606,
"f1": 0.0546665268456376,
"f1_stderr": 0.0018294405855806455
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5327545382794001,
"acc_stderr": 0.014022300570434134
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_frank098__orca_mini_3b_juniper | open-llm-leaderboard | 2023-09-17T00:19:56Z | 281 | 0 | [
"region:us"
]
| null | 2023-08-18T12:01:02Z | ---
pretty_name: Evaluation run of frank098/orca_mini_3b_juniper
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [frank098/orca_mini_3b_juniper](https://huggingface.co/frank098/orca_mini_3b_juniper)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_frank098__orca_mini_3b_juniper\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T00:19:44.475095](https://huggingface.co/datasets/open-llm-leaderboard/details_frank098__orca_mini_3b_juniper/blob/main/results_2023-09-17T00-19-44.475095.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0007340604026845638,\n\
\ \"em_stderr\": 0.000277361445733574,\n \"f1\": 0.04966652684563771,\n\
\ \"f1_stderr\": 0.001261898789421576,\n \"acc\": 0.3041531307650375,\n\
\ \"acc_stderr\": 0.007876199120377373\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0007340604026845638,\n \"em_stderr\": 0.000277361445733574,\n\
\ \"f1\": 0.04966652684563771,\n \"f1_stderr\": 0.001261898789421576\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.00530705079605762,\n \
\ \"acc_stderr\": 0.002001305720948044\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6029992107340174,\n \"acc_stderr\": 0.013751092519806702\n\
\ }\n}\n```"
repo_url: https://huggingface.co/frank098/orca_mini_3b_juniper
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|arc:challenge|25_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T00_19_44.475095
path:
- '**/details_harness|drop|3_2023-09-17T00-19-44.475095.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T00-19-44.475095.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T00_19_44.475095
path:
- '**/details_harness|gsm8k|5_2023-09-17T00-19-44.475095.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T00-19-44.475095.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hellaswag|10_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T10:27:47.193085.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T10:27:47.193085.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T10:27:47.193085.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T00_19_44.475095
path:
- '**/details_harness|winogrande|5_2023-09-17T00-19-44.475095.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T00-19-44.475095.parquet'
- config_name: results
data_files:
- split: 2023_07_24T10_27_47.193085
path:
- results_2023-07-24T10:27:47.193085.parquet
- split: 2023_09_17T00_19_44.475095
path:
- results_2023-09-17T00-19-44.475095.parquet
- split: latest
path:
- results_2023-09-17T00-19-44.475095.parquet
---
# Dataset Card for Evaluation run of frank098/orca_mini_3b_juniper
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/frank098/orca_mini_3b_juniper
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [frank098/orca_mini_3b_juniper](https://huggingface.co/frank098/orca_mini_3b_juniper) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_frank098__orca_mini_3b_juniper",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T00:19:44.475095](https://huggingface.co/datasets/open-llm-leaderboard/details_frank098__orca_mini_3b_juniper/blob/main/results_2023-09-17T00-19-44.475095.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0007340604026845638,
"em_stderr": 0.000277361445733574,
"f1": 0.04966652684563771,
"f1_stderr": 0.001261898789421576,
"acc": 0.3041531307650375,
"acc_stderr": 0.007876199120377373
},
"harness|drop|3": {
"em": 0.0007340604026845638,
"em_stderr": 0.000277361445733574,
"f1": 0.04966652684563771,
"f1_stderr": 0.001261898789421576
},
"harness|gsm8k|5": {
"acc": 0.00530705079605762,
"acc_stderr": 0.002001305720948044
},
"harness|winogrande|5": {
"acc": 0.6029992107340174,
"acc_stderr": 0.013751092519806702
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
deadbits/vigil-instruction-bypass-ada-002 | deadbits | 2023-09-09T18:29:12Z | 281 | 0 | [
"embeddings",
"text",
"security",
"region:us"
]
| null | 2023-09-09T16:49:16Z | ---
tags:
- embeddings
- text
- security
pretty_name: 'Vigil: LLM Instruction Bypass text-embedding-ada-002 '
---
# Vigil: LLM Instruction Bypass all-MiniLM-L6-v2
- **Repo:** [github.com/deadbits/vigil-llm](https://github.com/deadbits/vigil-llm)
`Vigil` is a Python framework and REST API for assessing Large Language Model (LLM) prompts against a set of scanners to detect prompt injections, jailbreaks, and other potentially risky inputs.
This repository contains `text-embedding-ada-002` embeddings for all Instruction Bypass style prompts ("Ignore instructions ...") used by [Vigil](https://github.com/deadbits/prompt-injection-defense).
You can use the [parquet2vdb.py](https://github.com/deadbits/prompt-injection-defense/blob/main/vigil/utils/parquet2vdb.py) utility to load the embeddings in the Vigil chromadb instance, or use them in your own application.
## Format
```json
[
{
"text": str,
"embedding": [],
"model": "text-embedding-ada-002"
}
]
```
Instruction bypass prompts generated with: https://gist.github.com/deadbits/e93a90aa36c9aa7b5ce1179597a6fe3d#file-generate-phrases-py | [
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|
atmallen/qm_grader_last_1.0e | atmallen | 2023-11-16T18:22:24Z | 281 | 0 | [
"region:us"
]
| null | 2023-11-16T03:25:10Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 29940088
num_examples: 400000
- name: validation
num_bytes: 3002836
num_examples: 40000
- name: test
num_bytes: 3004340
num_examples: 40000
download_size: 0
dataset_size: 35947264
---
# Dataset Card for "qm_grader_last_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
bnl_newspapers | null | 2023-01-25T14:27:26Z | 280 | 1 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"language:da",
"language:de",
"language:fi",
"language:fr",
"language:lb",
"language:nl",
"language:pt",
"license:cc0-1.0",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
- da
- de
- fi
- fr
- lb
- nl
- pt
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
pretty_name: BnL Historical Newspapers
dataset_info:
features:
- name: id
dtype: string
- name: source
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: ispartof
dtype: string
- name: text
dtype: string
- name: pub_date
dtype: timestamp[s]
- name: publisher
dtype: string
- name: language
dtype: string
- name: article_type
dtype:
class_label:
names:
'0': ADVERTISEMENT_SECTION
'1': BIBLIOGRAPHY
'2': CHAPTER
'3': INDEX
'4': CONTRIBUTION
'5': TABLE_OF_CONTENTS
'6': WEATHER
'7': SHIPPING
'8': SECTION
'9': ARTICLE
'10': TITLE_SECTION
'11': DEATH_NOTICE
'12': SUPPLEMENT
'13': TABLE
'14': ADVERTISEMENT
'15': CHART_DIAGRAM
'16': ILLUSTRATION
'17': ISSUE
- name: extent
dtype: int32
config_name: processed
splits:
- name: train
num_bytes: 1611620156
num_examples: 537558
download_size: 1224029060
dataset_size: 1611620156
---
# Dataset Card for BnL Historical Newspapers
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://data.bnl.lu/data/historical-newspapers/
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
The BnL has digitised over 800.000 pages of Luxembourg newspapers. This dataset currently has one configuration covering a subset of these newspapers, which sit under the "Processed Datasets" collection. The BNL:
> processed all newspapers and monographs that are in the public domain and extracted the full text and associated meta data of every single article, section, advertisement… The result is a large number of small, easy to use XML files formatted using Dublin Core.
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
The dataset currently contains a single configuration.
### Data Instances
An example instance from the datasets:
``` python
{'id': 'https://persist.lu/ark:/70795/wx8r4c/articles/DTL47',
'article_type': 8,
'extent': 49,
'ispartof': 'Luxemburger Wort',
'pub_date': datetime.datetime(1853, 3, 23, 0, 0),
'publisher': 'Verl. der St-Paulus-Druckerei',
'source': 'newspaper/luxwort/1853-03-23',
'text': 'Asien. Eine neue Nedcrland-Post ist angekommen mil Nachrichten aus Calcutta bis zum 5. Febr.; Vom» vay, 12. Febr. ; Nangun und HongKong, 13. Jan. Die durch die letzte Post gebrachle Nachricht, der König von Ava sei durch seinen Bruder enlhronl worden, wird bestätigt. (K. Z.) Verantwortl. Herausgeber, F. Schümann.',
'title': 'Asien.',
'url': 'http://www.eluxemburgensia.lu/webclient/DeliveryManager?pid=209701#panel:pp|issue:209701|article:DTL47',
'language': 'de'
}
```
### Data Fields
- 'id': This is a unique and persistent identifier using ARK.
- 'article_type': The type of the exported data, possible values ('ADVERTISEMENT_SECTION',
'BIBLIOGRAPHY',
'CHAPTER',
'INDEX',
'CONTRIBUTION',
'TABLE_OF_CONTENTS',
'WEATHER',
'SHIPPING',
'SECTION',
'ARTICLE',
'TITLE_SECTION',
'DEATH_NOTICE',
'SUPPLEMENT',
'TABLE',
'ADVERTISEMENT',
'CHART_DIAGRAM',
'ILLUSTRATION',
'ISSUE')
- 'extent': The number of words in the text field
- 'ispartof: The complete title of the source document e.g. “Luxemburger Wort”.
- 'pub_date': The publishing date of the document e.g “1848-12-15”
- 'publisher':The publisher of the document e.g. “Verl. der St-Paulus-Druckerei”.
- 'source': Describes the source of the document. For example
<dc:source>newspaper/luxwort/1848-12-15</dc:source> means that this article comes from the newspaper “luxwort” (ID for Luxemburger Wort) issued on 15.12.1848.
- 'text': The full text of the entire article, section, advertisement etc. It includes any titles and subtitles as well. The content does not contain layout information, such as headings, paragraphs or lines.
- 'title': The main title of the article, section, advertisement, etc.
- 'url': The link to the BnLViewer on eluxemburgensia.lu to view the resource online.
- 'language': The language of the text, possible values ('ar', 'da', 'de', 'fi', 'fr', 'lb', 'nl', 'pt')
### Data Splits
This dataset contains a single split `train`.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{bnl_newspapers,
title={Historical Newspapers},
url={https://data.bnl.lu/data/historical-newspapers/},
author={ Bibliothèque nationale du Luxembourg},
```
### Contributions
Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset. | [
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|
roman_urdu | null | 2023-01-25T14:43:17Z | 280 | 2 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ur",
"license:unknown",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ur
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: roman-urdu-data-set
pretty_name: Roman Urdu Dataset
dataset_info:
features:
- name: sentence
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': Positive
'1': Negative
'2': Neutral
splits:
- name: train
num_bytes: 1633423
num_examples: 20229
download_size: 1628349
dataset_size: 1633423
---
# Dataset Card for Roman Urdu Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Roman+Urdu+Data+Set)
- **Point of Contact:** [Zareen Sharf](mailto:[email protected])
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Urdu
## Dataset Structure
[More Information Needed]
### Data Instances
```
Wah je wah,Positive,
```
### Data Fields
Each row consists of a short Urdu text, followed by a sentiment label. The labels are one of `Positive`, `Negative`, and `Neutral`. Note that the original source file is a comma-separated values file.
* `sentence`: A short Urdu text
* `label`: One of `Positive`, `Negative`, and `Neutral`, indicating the polarity of the sentiment expressed in the sentence
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@InProceedings{Sharf:2018,
title = "Performing Natural Language Processing on Roman Urdu Datasets",
authors = "Zareen Sharf and Saif Ur Rahman",
booktitle = "International Journal of Computer Science and Network Security",
volume = "18",
number = "1",
pages = "141-148",
year = "2018"
}
@misc{Dua:2019,
author = "Dua, Dheeru and Graff, Casey",
year = "2017",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
institution = "University of California, Irvine, School of Information and Computer Sciences"
}
```
### Contributions
Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset. | [
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|
yoruba_gv_ner | null | 2023-01-25T15:03:39Z | 280 | 0 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:yo",
"license:cc-by-3.0",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- yo
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Yoruba GV NER Corpus
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-DATE
'8': I-DATE
config_name: yoruba_gv_ner
splits:
- name: train
num_bytes: 358885
num_examples: 817
- name: validation
num_bytes: 50161
num_examples: 117
- name: test
num_bytes: 96518
num_examples: 237
download_size: 254347
dataset_size: 505564
---
# Dataset Card for Yoruba GV NER Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [Yoruba GV NER](https://github.com/ajesujoba/YorubaTwi-Embedding/tree/master/Yoruba/Yoruba-NER)
- **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335/
- **Leaderboard:**
- **Point of Contact:** [David Adelani](mailto:[email protected])
### Dataset Summary
The Yoruba GV NER is a named entity recognition (NER) dataset for Yorùbá language based on the [Global Voices news](https://yo.globalvoices.org/) corpus. Global Voices (GV) is a multilingual news platform with articles contributed by journalists, translators, bloggers, and human rights activists from around the world with a coverage of over 50 languages. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Yorùbá.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [B-LOC, 0, 0, 0, 0],
'tokens': ['Tanzania', 'fi', 'Ajìjàgbara', 'Ọmọ', 'Orílẹ̀-èdèe']
}
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & times (DATE). (O) is used for tokens not considered part of any named entity.
### Data Splits
Training (19,421 tokens), validation (2,695 tokens) and test split (5,235 tokens)
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Yorùbá.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset is based on the news domain and was crawled from [Global Voices Yorùbá news](https://yo.globalvoices.org/).
[More Information Needed]
#### Who are the source language producers?
The dataset contributed by journalists, translators, bloggers, and human rights activists from around the world. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated by Jesujoba Alabi and David Adelani for the paper:
[Massive vs. Curated Embeddings for Low-Resourced Languages: the case of Yorùbá and Twi](https://www.aclweb.org/anthology/2020.lrec-1.335/).
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The annotated data sets were developed by students of Saarland University, Saarbrücken, Germany .
### Licensing Information
The data is under the [Creative Commons Attribution 3.0 ](https://creativecommons.org/licenses/by/3.0/)
### Citation Information
```
@inproceedings{alabi-etal-2020-massive,
title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
author = "Alabi, Jesujoba and
Amponsah-Kaakyire, Kwabena and
Adelani, David and
Espa{\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
pages = "2754--2762",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
### Contributions
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset. | [
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|
yoruba_text_c3 | null | 2023-06-16T15:06:58Z | 280 | 1 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:yo",
"license:cc-by-nc-4.0",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- yo
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: Yorùbá Text C3
dataset_info:
- config_name: plain_text
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 77094396
num_examples: 562238
download_size: 75407454
dataset_size: 77094396
- config_name: yoruba_text_c3
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 77094396
num_examples: 562238
download_size: 75407454
dataset_size: 77094396
---
# Dataset Card for Yorùbá Text C3
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/ajesujoba/YorubaTwi-Embedding/
- **Paper:** https://aclanthology.org/2020.lrec-1.335/
- **Leaderboard:**
- **Point of Contact:** [Jesujoba Alabi](mailto:[email protected])
### Dataset Summary
Yorùbá Text C3 was collected from various sources from the web (Bible, JW300, books, news articles, wikipedia, etc)
to compare pre-trained word embeddings (Fasttext and BERT) and embeddings and embeddings trained on curated Yorùbá Texts.
The dataset consists of clean texts (i.e texts with proper Yorùbá diacritics) like the Bible & JW300 and noisy texts (
with incorrect or absent diacritics)
from other online sources like Wikipedia, BBC Yorùbá, and VON Yorùbá
### Supported Tasks and Leaderboards
For training word embeddings and language models on Yoruba texts.
### Languages
The language supported is Yorùbá.
## Dataset Structure
### Data Instances
A data point is a sentence in each line.
{
'text': 'lílo àkàbà — ǹjẹ́ o máa ń ṣe àyẹ̀wò wọ̀nyí tó lè dáàbò bò ẹ́'
}
### Data Fields
- `text`: a `string` feature.
a sentence text per line
### Data Splits
Contains only the training split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Yorùbá.
### Source Data
#### Initial Data Collection and Normalization
The dataset comes from various sources of the web like Bible, JW300, books, news articles, wikipedia, etc.
See Table 1 in the [paper](https://www.aclweb.org/anthology/2020.lrec-1.335/) for the summary of the dataset and statistics
#### Who are the source language producers?
[Jehovah Witness](https://www.jw.org/yo/) (JW300)
[Yorùbá Bible](http://www.bible.com/)
[Yorùbá Wikipedia](dumps.wikimedia.org/yowiki)
[BBC Yorùbá](bbc.com/yoruba)
[VON Yorùbá](https://von.gov.ng/)
[Global Voices Yorùbá]( yo.globalvoices.org)
And other sources, see https://www.aclweb.org/anthology/2020.lrec-1.335/
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
The dataset is biased to the religion domain (Christianity) because of the inclusion of JW300 and the Bible.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The data sets were curated by Jesujoba Alabi and David Adelani, students of Saarland University, Saarbrücken, Germany .
### Licensing Information
The data is under the [Creative Commons Attribution-NonCommercial 4.0 ](https://creativecommons.org/licenses/by-nc/4.0/legalcode)
### Citation Information
```
@inproceedings{alabi-etal-2020-massive,
title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
author = "Alabi, Jesujoba and
Amponsah-Kaakyire, Kwabena and
Adelani, David and
Espa{\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
pages = "2754--2762",
abstract = "The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yor{\`u}b{\'a} and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yor{\`u}b{\'a} and Twi. We extend the analysis to contextual word embeddings and evaluate multilingual BERT on a named entity recognition task. For this, we annotate with named entities the Global Voices corpus for Yor{\`u}b{\'a}. As output of the work, we provide corpora, embeddings and the test suits for both languages.",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
### Contributions
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset. | [
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|
adorkin/extended_tweet_emojis | adorkin | 2023-02-07T12:18:57Z | 280 | 1 | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
task_categories:
- text-classification
language:
- en
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is comprised of `emoji` and `emotion` subsets of [tweet_eval](https://huggingface.co/datasets/tweet_eval). The motivation
is that the original `emoji` subset essentially contains only positive/neutral emojis, while `emotion` subset contains a varied array
of emotions. So, the idea was to replace emotion labels with corresponding emojis (sad, angry) in the `emotion` subset and mix it together
with the `emoji` subset.
### Supported Tasks and Leaderboards
Similar to tweet eval the expected usage is text classification.
### Languages
Only English is present in the dataset.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
Refer to [tweet_eval](https://huggingface.co/datasets/tweet_eval). No additional data was added.
#### Annotation process
Same as tweet eval.
#### Who are the annotators?
Same as tweet eval.
### Personal and Sensitive Information
Same as tweet eval.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
Nexdata/mandarin_chinese | Nexdata | 2023-11-22T09:52:56Z | 280 | 7 | [
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for mandarin_chinese
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://nexdata.ai/?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset contains 15,000 hours of Mandarin Chinese speech data. It's collected from local Mandarin speakers in 33 provinces of China, covering mutiple scenes and enviroments. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 97%.
For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Mandarin
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commercial License
### Citation Information
[More Information Needed]
### Contributions
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|
echarlaix/vqa | echarlaix | 2022-02-01T10:45:13Z | 280 | 1 | [
"license:apache-2.0",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
license: apache-2.0
---
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|
open-llm-leaderboard/details_bigcode__gpt_bigcode-santacoder | open-llm-leaderboard | 2023-09-17T12:23:31Z | 280 | 0 | [
"region:us"
]
| null | 2023-08-17T23:54:07Z | ---
pretty_name: Evaluation run of bigcode/gpt_bigcode-santacoder
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bigcode/gpt_bigcode-santacoder](https://huggingface.co/bigcode/gpt_bigcode-santacoder)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_bigcode__gpt_bigcode-santacoder\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T12:23:19.324032](https://huggingface.co/datasets/open-llm-leaderboard/details_bigcode__gpt_bigcode-santacoder/blob/main/results_2023-09-17T12-23-19.324032.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0009437919463087249,\n\
\ \"em_stderr\": 0.0003144653119413059,\n \"f1\": 0.03720532718120814,\n\
\ \"f1_stderr\": 0.0010858123513473891,\n \"acc\": 0.2418011181367818,\n\
\ \"acc_stderr\": 0.008020272468716342\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0009437919463087249,\n \"em_stderr\": 0.0003144653119413059,\n\
\ \"f1\": 0.03720532718120814,\n \"f1_stderr\": 0.0010858123513473891\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.00530705079605762,\n \
\ \"acc_stderr\": 0.0020013057209480557\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.47829518547750594,\n \"acc_stderr\": 0.014039239216484629\n\
\ }\n}\n```"
repo_url: https://huggingface.co/bigcode/gpt_bigcode-santacoder
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|arc:challenge|25_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T12_23_19.324032
path:
- '**/details_harness|drop|3_2023-09-17T12-23-19.324032.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T12-23-19.324032.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T12_23_19.324032
path:
- '**/details_harness|gsm8k|5_2023-09-17T12-23-19.324032.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T12-23-19.324032.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hellaswag|10_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:05:43.434285.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T19:05:43.434285.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T19:05:43.434285.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T12_23_19.324032
path:
- '**/details_harness|winogrande|5_2023-09-17T12-23-19.324032.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T12-23-19.324032.parquet'
- config_name: results
data_files:
- split: 2023_07_19T19_05_43.434285
path:
- results_2023-07-19T19:05:43.434285.parquet
- split: 2023_09_17T12_23_19.324032
path:
- results_2023-09-17T12-23-19.324032.parquet
- split: latest
path:
- results_2023-09-17T12-23-19.324032.parquet
---
# Dataset Card for Evaluation run of bigcode/gpt_bigcode-santacoder
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bigcode/gpt_bigcode-santacoder
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [bigcode/gpt_bigcode-santacoder](https://huggingface.co/bigcode/gpt_bigcode-santacoder) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_bigcode__gpt_bigcode-santacoder",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T12:23:19.324032](https://huggingface.co/datasets/open-llm-leaderboard/details_bigcode__gpt_bigcode-santacoder/blob/main/results_2023-09-17T12-23-19.324032.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0009437919463087249,
"em_stderr": 0.0003144653119413059,
"f1": 0.03720532718120814,
"f1_stderr": 0.0010858123513473891,
"acc": 0.2418011181367818,
"acc_stderr": 0.008020272468716342
},
"harness|drop|3": {
"em": 0.0009437919463087249,
"em_stderr": 0.0003144653119413059,
"f1": 0.03720532718120814,
"f1_stderr": 0.0010858123513473891
},
"harness|gsm8k|5": {
"acc": 0.00530705079605762,
"acc_stderr": 0.0020013057209480557
},
"harness|winogrande|5": {
"acc": 0.47829518547750594,
"acc_stderr": 0.014039239216484629
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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0.24413453042507172,
0.20458504557609558,
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|
open-llm-leaderboard/details_Kunhao__pile-7b-250b-tokens | open-llm-leaderboard | 2023-09-17T06:32:07Z | 280 | 0 | [
"region:us"
]
| null | 2023-08-18T18:39:56Z | ---
pretty_name: Evaluation run of Kunhao/pile-7b-250b-tokens
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Kunhao/pile-7b-250b-tokens](https://huggingface.co/Kunhao/pile-7b-250b-tokens)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Kunhao__pile-7b-250b-tokens\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T06:31:55.680940](https://huggingface.co/datasets/open-llm-leaderboard/details_Kunhao__pile-7b-250b-tokens/blob/main/results_2023-09-17T06-31-55.680940.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0016778523489932886,\n\
\ \"em_stderr\": 0.00041913301788268413,\n \"f1\": 0.02978817114093967,\n\
\ \"f1_stderr\": 0.0010045845151481873,\n \"acc\": 0.26666299658982046,\n\
\ \"acc_stderr\": 0.008015854967176925\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788268413,\n\
\ \"f1\": 0.02978817114093967,\n \"f1_stderr\": 0.0010045845151481873\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.00530705079605762,\n \
\ \"acc_stderr\": 0.002001305720948061\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5280189423835833,\n \"acc_stderr\": 0.014030404213405788\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Kunhao/pile-7b-250b-tokens
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|arc:challenge|25_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T06_31_55.680940
path:
- '**/details_harness|drop|3_2023-09-17T06-31-55.680940.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T06-31-55.680940.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T06_31_55.680940
path:
- '**/details_harness|gsm8k|5_2023-09-17T06-31-55.680940.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T06-31-55.680940.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hellaswag|10_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T19:43:31.029227.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T19:43:31.029227.parquet'
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- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T19:43:31.029227.parquet'
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- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T19:43:31.029227.parquet'
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- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T19:43:31.029227.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T19:43:31.029227.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T19:43:31.029227.parquet'
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- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T19:43:31.029227.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T19:43:31.029227.parquet'
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- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T19:43:31.029227.parquet'
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- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T19:43:31.029227.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T19:43:31.029227.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T19:43:31.029227.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T19:43:31.029227.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T06_31_55.680940
path:
- '**/details_harness|winogrande|5_2023-09-17T06-31-55.680940.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T06-31-55.680940.parquet'
- config_name: results
data_files:
- split: 2023_08_17T19_43_31.029227
path:
- results_2023-08-17T19:43:31.029227.parquet
- split: 2023_09_17T06_31_55.680940
path:
- results_2023-09-17T06-31-55.680940.parquet
- split: latest
path:
- results_2023-09-17T06-31-55.680940.parquet
---
# Dataset Card for Evaluation run of Kunhao/pile-7b-250b-tokens
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Kunhao/pile-7b-250b-tokens
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Kunhao/pile-7b-250b-tokens](https://huggingface.co/Kunhao/pile-7b-250b-tokens) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Kunhao__pile-7b-250b-tokens",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T06:31:55.680940](https://huggingface.co/datasets/open-llm-leaderboard/details_Kunhao__pile-7b-250b-tokens/blob/main/results_2023-09-17T06-31-55.680940.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788268413,
"f1": 0.02978817114093967,
"f1_stderr": 0.0010045845151481873,
"acc": 0.26666299658982046,
"acc_stderr": 0.008015854967176925
},
"harness|drop|3": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788268413,
"f1": 0.02978817114093967,
"f1_stderr": 0.0010045845151481873
},
"harness|gsm8k|5": {
"acc": 0.00530705079605762,
"acc_stderr": 0.002001305720948061
},
"harness|winogrande|5": {
"acc": 0.5280189423835833,
"acc_stderr": 0.014030404213405788
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
aditijha/instruct_v1_5k | aditijha | 2023-09-22T21:16:56Z | 280 | 0 | [
"region:us"
]
| null | 2023-09-22T21:16:55Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 3688239.8415107066
num_examples: 5000
download_size: 1942992
dataset_size: 3688239.8415107066
---
# Dataset Card for "instruct_v1_5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
open-llm-leaderboard/details_Envoid__Yousei-22B | open-llm-leaderboard | 2023-10-26T01:22:32Z | 280 | 0 | [
"region:us"
]
| null | 2023-10-10T13:10:05Z | ---
pretty_name: Evaluation run of Envoid/Yousei-22B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Envoid/Yousei-22B](https://huggingface.co/Envoid/Yousei-22B) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Envoid__Yousei-22B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-26T01:22:19.156649](https://huggingface.co/datasets/open-llm-leaderboard/details_Envoid__Yousei-22B/blob/main/results_2023-10-26T01-22-19.156649.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.15604026845637584,\n\
\ \"em_stderr\": 0.003716369253387427,\n \"f1\": 0.23708158557046954,\n\
\ \"f1_stderr\": 0.003820843210859161,\n \"acc\": 0.3598119404753428,\n\
\ \"acc_stderr\": 0.007269770584572424\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.15604026845637584,\n \"em_stderr\": 0.003716369253387427,\n\
\ \"f1\": 0.23708158557046954,\n \"f1_stderr\": 0.003820843210859161\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.004548900682335102,\n \
\ \"acc_stderr\": 0.0018535550440036204\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7150749802683505,\n \"acc_stderr\": 0.012685986125141229\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Envoid/Yousei-22B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|arc:challenge|25_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_26T01_22_19.156649
path:
- '**/details_harness|drop|3_2023-10-26T01-22-19.156649.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-26T01-22-19.156649.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_26T01_22_19.156649
path:
- '**/details_harness|gsm8k|5_2023-10-26T01-22-19.156649.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-26T01-22-19.156649.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hellaswag|10_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T13-09-41.852615.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T13-09-41.852615.parquet'
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- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T13-09-41.852615.parquet'
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- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T13-09-41.852615.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T13-09-41.852615.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T13-09-41.852615.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T13-09-41.852615.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_26T01_22_19.156649
path:
- '**/details_harness|winogrande|5_2023-10-26T01-22-19.156649.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-26T01-22-19.156649.parquet'
- config_name: results
data_files:
- split: 2023_10_10T13_09_41.852615
path:
- results_2023-10-10T13-09-41.852615.parquet
- split: 2023_10_26T01_22_19.156649
path:
- results_2023-10-26T01-22-19.156649.parquet
- split: latest
path:
- results_2023-10-26T01-22-19.156649.parquet
---
# Dataset Card for Evaluation run of Envoid/Yousei-22B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Envoid/Yousei-22B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Envoid/Yousei-22B](https://huggingface.co/Envoid/Yousei-22B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Envoid__Yousei-22B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T01:22:19.156649](https://huggingface.co/datasets/open-llm-leaderboard/details_Envoid__Yousei-22B/blob/main/results_2023-10-26T01-22-19.156649.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.15604026845637584,
"em_stderr": 0.003716369253387427,
"f1": 0.23708158557046954,
"f1_stderr": 0.003820843210859161,
"acc": 0.3598119404753428,
"acc_stderr": 0.007269770584572424
},
"harness|drop|3": {
"em": 0.15604026845637584,
"em_stderr": 0.003716369253387427,
"f1": 0.23708158557046954,
"f1_stderr": 0.003820843210859161
},
"harness|gsm8k|5": {
"acc": 0.004548900682335102,
"acc_stderr": 0.0018535550440036204
},
"harness|winogrande|5": {
"acc": 0.7150749802683505,
"acc_stderr": 0.012685986125141229
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_pszemraj__pythia-6.9b-HC3 | open-llm-leaderboard | 2023-10-26T13:43:48Z | 280 | 0 | [
"region:us"
]
| null | 2023-10-26T13:43:39Z | ---
pretty_name: Evaluation run of pszemraj/pythia-6.9b-HC3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [pszemraj/pythia-6.9b-HC3](https://huggingface.co/pszemraj/pythia-6.9b-HC3) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_pszemraj__pythia-6.9b-HC3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-26T13:43:34.818170](https://huggingface.co/datasets/open-llm-leaderboard/details_pszemraj__pythia-6.9b-HC3/blob/main/results_2023-10-26T13-43-34.818170.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.00010486577181208053,\n\
\ \"em_stderr\": 0.00010486577181208242,\n \"f1\": 0.022344798657718254,\n\
\ \"f1_stderr\": 0.0006975774134342648,\n \"acc\": 0.30386740331491713,\n\
\ \"acc_stderr\": 0.006861200231000444\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.00010486577181208053,\n \"em_stderr\": 0.00010486577181208242,\n\
\ \"f1\": 0.022344798657718254,\n \"f1_stderr\": 0.0006975774134342648\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6077348066298343,\n\
\ \"acc_stderr\": 0.013722400462000888\n }\n}\n```"
repo_url: https://huggingface.co/pszemraj/pythia-6.9b-HC3
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_26T13_43_34.818170
path:
- '**/details_harness|drop|3_2023-10-26T13-43-34.818170.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-26T13-43-34.818170.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_26T13_43_34.818170
path:
- '**/details_harness|gsm8k|5_2023-10-26T13-43-34.818170.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-26T13-43-34.818170.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_26T13_43_34.818170
path:
- '**/details_harness|winogrande|5_2023-10-26T13-43-34.818170.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-26T13-43-34.818170.parquet'
- config_name: results
data_files:
- split: 2023_10_26T13_43_34.818170
path:
- results_2023-10-26T13-43-34.818170.parquet
- split: latest
path:
- results_2023-10-26T13-43-34.818170.parquet
---
# Dataset Card for Evaluation run of pszemraj/pythia-6.9b-HC3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/pszemraj/pythia-6.9b-HC3
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [pszemraj/pythia-6.9b-HC3](https://huggingface.co/pszemraj/pythia-6.9b-HC3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_pszemraj__pythia-6.9b-HC3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T13:43:34.818170](https://huggingface.co/datasets/open-llm-leaderboard/details_pszemraj__pythia-6.9b-HC3/blob/main/results_2023-10-26T13-43-34.818170.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.00010486577181208053,
"em_stderr": 0.00010486577181208242,
"f1": 0.022344798657718254,
"f1_stderr": 0.0006975774134342648,
"acc": 0.30386740331491713,
"acc_stderr": 0.006861200231000444
},
"harness|drop|3": {
"em": 0.00010486577181208053,
"em_stderr": 0.00010486577181208242,
"f1": 0.022344798657718254,
"f1_stderr": 0.0006975774134342648
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.6077348066298343,
"acc_stderr": 0.013722400462000888
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
kannada_news | null | 2023-01-25T14:33:33Z | 279 | 1 | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:kn",
"license:cc-by-sa-4.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- other
language_creators:
- other
language:
- kn
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: KannadaNews Dataset
dataset_info:
features:
- name: headline
dtype: string
- name: label
dtype:
class_label:
names:
'0': sports
'1': tech
'2': entertainment
splits:
- name: train
num_bytes: 969216
num_examples: 5167
- name: validation
num_bytes: 236817
num_examples: 1293
download_size: 0
dataset_size: 1206033
---
# Dataset Card for kannada_news dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Kaggle link](https://www.kaggle.com/disisbig/kannada-news-dataset) for kannada news headlines dataset
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** More information about the dataset and the models can be found [here](https://github.com/goru001/nlp-for-kannada)
### Dataset Summary
The Kannada news dataset contains only the headlines of news article in three categories:
Entertainment, Tech, and Sports.
The data set contains around 6300 news article headlines which are collected from Kannada news websites.
The data set has been cleaned and contains train and test set using which can be used to benchmark topic classification models in Kannada.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Kannada (kn)
## Dataset Structure
### Data Instances
The data has two files. A train.csv and valid.csv. An example row of the dataset is as below:
```
{
'headline': 'ಫಿಫಾ ವಿಶ್ವಕಪ್ ಫೈನಲ್: ಅತಿರೇಕಕ್ಕೇರಿದ ಸಂಭ್ರಮಾಚರಣೆ; ಅಭಿಮಾನಿಗಳ ಹುಚ್ಚು ವರ್ತನೆಗೆ ವ್ಯಾಪಕ ಖಂಡನೆ',
'label':'sports'
}
```
NOTE: The data has very few examples on the technology (class label: 'tech') topic. [More Information Needed]
### Data Fields
Data has two fields:
- headline: text headline in kannada (string)
- label : corresponding class label which the headlines pertains to in english (string)
### Data Splits
The dataset is divided into two splits. All the headlines are scraped from news websites on the internet.
| | train | validation |
|-----------------|--------:|-----------:|
| Input Sentences | 5167 | 1293 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
There are starkingly less amount of data for South Indian languages, especially Kannada, available in digital format which can be used for NLP purposes.
Though having roughly 38 million native speakers, it is a little under-represented language and will benefit from active contribution from the community.
This dataset, however, can just help people get exposed to Kannada and help proceed further active participation for enabling continuous progress and development.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[Gaurav Arora] (https://github.com/goru001/nlp-for-kannada). Has also got some starter models an embeddings to help get started.
### Licensing Information
cc-by-sa-4.0
### Citation Information
https://www.kaggle.com/disisbig/kannada-news-dataset
### Contributions
Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset. | [
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|
swahili | null | 2022-11-18T21:49:35Z | 279 | 7 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:sw",
"license:cc-by-4.0",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- sw
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: swahili
dataset_info:
features:
- name: text
dtype: string
config_name: swahili
splits:
- name: train
num_bytes: 7700136
num_examples: 42069
- name: test
num_bytes: 695092
num_examples: 3371
- name: validation
num_bytes: 663520
num_examples: 3372
download_size: 2783330
dataset_size: 9058748
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339006/
- **Repository:**
- **Paper:** https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339006/
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
The Swahili dataset developed specifically for language modeling task.
The dataset contains 28,000 unique words with 6.84M, 970k, and 2M words for the train,
valid and test partitions respectively which represent the ratio 80:10:10.
The entire dataset is lowercased, has no punctuation marks and,
the start and end of sentence markers have been incorporated to facilitate easy tokenization during language modeling.
### Supported Tasks and Leaderboards
Language Modeling
### Languages
Swahili (sw)
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- text : A line of text in Swahili
### Data Splits
train = 80%, valid = 10%, test = 10%
## Dataset Creation
### Curation Rationale
Enhancing African low-resource languages
### Source Data
#### Initial Data Collection and Normalization
The dataset contains 28,000 unique words with 6.84 M, 970k, and 2 M words for the train, valid and test partitions respectively which represent the ratio 80:10:10.
The entire dataset is lowercased, has no punctuation marks and, the start and end of sentence markers have been incorporated to facilitate easy tokenization during language modelling.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
Unannotated data
#### Who are the annotators?
NA
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
Enhancing African low-resource languages
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Creative Commons Attribution 4.0 International
### Citation Information
"""\
@InProceedings{huggingface:dataset,
title = Language modeling data for Swahili (Version 1),
authors={Shivachi Casper Shikali, & Mokhosi Refuoe.
},
year={2019},
link = http://doi.org/10.5281/zenodo.3553423
}
"""
### Contributions
Thanks to [@akshayb7](https://github.com/akshayb7) for adding this dataset. | [
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|
GEM/wiki_auto_asset_turk | GEM | 2022-10-24T15:31:10Z | 279 | 3 | [
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:1910.02677",
"arxiv:2005.00352",
"region:us"
]
| [
"text2text-generation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- en
license:
- other
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
pretty_name: wiki_auto_asset_turk
---
# Dataset Card for GEM/wiki_auto_asset_turk
## Dataset Description
- **Homepage:** n/a
- **Repository:** https://github.com/chaojiang06/wiki-auto, [ASSET repository
- **Paper:** https://aclanthology.org/2020.acl-main.709/, [ASSET
- **Leaderboard:** N/A
- **Point of Contact:** WikiAuto: Chao Jiang; ASSET: Fernando Alva-Manchego and Louis Martin; TURK: Wei Xu
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/wiki_auto_asset_turk).
### Dataset Summary
WikiAuto is an English simplification dataset that we paired with ASSET and TURK, two very high-quality evaluation datasets, as test sets. The input is an English sentence taken from Wikipedia and the target a simplified sentence. ASSET and TURK contain the same test examples but have references that are simplified in different ways (splitting sentences vs. rewriting and splitting).
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/wiki_auto_asset_turk')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/wiki_auto_asset_turk).
#### website
n/a
#### paper
[WikiAuto](https://aclanthology.org/2020.acl-main.709/), [ASSET](https://aclanthology.org/2020.acl-main.424/), [TURK](https://aclanthology.org/Q16-1029/)
#### authors
WikiAuto: Chao Jiang, Mounica Maddela, Wuwei Lan, Yang Zhong, Wei Xu; ASSET: Fernando Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton, and Benoîıt Sagot, and Lucia Specia; TURK: Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch
## Dataset Overview
### Where to find the Data and its Documentation
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Wiki-Auto repository](https://github.com/chaojiang06/wiki-auto), [ASSET repository](https://github.com/facebookresearch/asset), [TURKCorpus](https://github.com/cocoxu/simplification)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[WikiAuto](https://aclanthology.org/2020.acl-main.709/), [ASSET](https://aclanthology.org/2020.acl-main.424/), [TURK](https://aclanthology.org/Q16-1029/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
WikiAuto:
```
@inproceedings{jiang-etal-2020-neural,
title = "Neural {CRF} Model for Sentence Alignment in Text Simplification",
author = "Jiang, Chao and
Maddela, Mounica and
Lan, Wuwei and
Zhong, Yang and
Xu, Wei",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.709",
doi = "10.18653/v1/2020.acl-main.709",
pages = "7943--7960",
}
```
ASSET:
```
@inproceedings{alva-manchego-etal-2020-asset,
title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations",
author = "Alva-Manchego, Fernando and
Martin, Louis and
Bordes, Antoine and
Scarton, Carolina and
Sagot, Beno{\^\i}t and
Specia, Lucia",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.424",
pages = "4668--4679",
}
```
TURK:
```
@article{Xu-EtAl:2016:TACL,
author = {Wei Xu and Courtney Napoles and Ellie Pavlick and Quanze Chen and Chris Callison-Burch},
title = {Optimizing Statistical Machine Translation for Text Simplification},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year = {2016},
url = {https://cocoxu.github.io/publications/tacl2016-smt-simplification.pdf},
pages = {401--415}
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
WikiAuto: Chao Jiang; ASSET: Fernando Alva-Manchego and Louis Martin; TURK: Wei Xu
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
[email protected], [email protected], [email protected], [email protected]
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
Wiki-Auto contains English text only (BCP-47: `en`). It is presented as a translation task where Wikipedia Simple English is treated as its own idiom. For a statement of what is intended (but not always observed) to constitute Simple English on this platform, see [Simple English in Wikipedia](https://simple.wikipedia.org/wiki/Wikipedia:About#Simple_English).
Both ASSET and TURK use crowdsourcing to change references, and their language is thus a combination of the WikiAuto data and the language of the demographic on mechanical Turk
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
other: Other license
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
WikiAuto provides a set of aligned sentences from English Wikipedia and Simple English Wikipedia as a resource to train sentence simplification systems.
The authors first crowd-sourced a set of manual alignments between sentences in a subset of the Simple English Wikipedia and their corresponding versions in English Wikipedia (this corresponds to the `manual` config in this version of the dataset), then trained a neural CRF system to predict these alignments.
The trained alignment prediction model was then applied to the other articles in Simple English Wikipedia with an English counterpart to create a larger corpus of aligned sentences (corresponding to the `auto` and `auto_acl` configs here).
[ASSET](https://github.com/facebookresearch/asset) [(Alva-Manchego et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.424.pdf) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from [TurkCorpus](https://github.com/cocoxu/simplification/) [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence
splitting in [HSplit](https://www.aclweb.org/anthology/D18-1081.pdf)), the simplifications in ASSET encompass a variety of rewriting transformations.
TURKCorpus is a high quality simplification dataset where each source (not simple) sentence is associated with 8 human-written simplifications that focus on lexical paraphrasing. It is one of the two evaluation datasets for the text simplification task in GEM. It acts as the validation and test set for paraphrasing-based simplification that does not involve sentence splitting and deletion.
#### Add. License Info
<!-- info: What is the 'other' license of the dataset? -->
<!-- scope: periscope -->
WikiAuto: `CC BY-NC 3.0`, ASSET: `CC BY-NC 4.0`, TURK: `GNU General Public License v3.0`
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Simplification
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
The goal is to communicate the main ideas of source sentence in a way that is easier to understand by non-native speakers of English.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`, `industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Ohio State University, University of Sheffield, Inria, Facebook AI Research, Imperial College London, University of Pennsylvania, John Hopkins University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
WikiAuto: Chao Jiang, Mounica Maddela, Wuwei Lan, Yang Zhong, Wei Xu; ASSET: Fernando Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton, and Benoîıt Sagot, and Lucia Specia; TURK: Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
WikiAuto: NSF, ODNI, IARPA, Figure Eight AI, and Criteo. ASSET: PRAIRIE Institute, ANR. TURK: NSF
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
GEM v1 had separate data cards for WikiAuto, ASSET, and TURK. They were contributed by Dhruv Kumar and Mounica Maddela. The initial data loader was written by Yacine Jernite. Sebastian Gehrmann merged and extended the data cards and migrated the loader to the v2 infrastructure.
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `source`: A source sentence from one of the datasets
- `target`: A single simplified sentence corresponding to `source`
- `references`: In the case of ASSET/TURK, references is a list of strings corresponding to the different references.
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The underlying datasets have extensive secondary annotations that can be used in conjunction with the GEM version. We omit those annotations to simplify the format into one that can be used by seq2seq models.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
'source': 'In early work, Rutherford discovered the concept of radioactive half-life , the radioactive element radon, and differentiated and named alpha and beta radiation .',
'target': 'Rutherford discovered the radioactive half-life, and the three parts of radiation which he named Alpha, Beta, and Gamma.'
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
In WikiAuto, which is used as training and validation set, the following splits are provided:
| | Tain | Dev | Test |
| ----- | ------ | ----- | ---- |
| Total sentence pairs | 373801 | 73249 | 118074 |
| Aligned sentence pairs | 1889 | 346 | 677 |
ASSET does not contain a training set; many models use [WikiLarge](https://github.com/XingxingZhang/dress) (Zhang and Lapata, 2017) for training. For GEM, [Wiki-Auto](https://github.com/chaojiang06/wiki-auto) will be used for training the model.
Each input sentence has 10 associated reference simplified sentences. The statistics of ASSET are given below.
| | Dev | Test | Total |
| ----- | ------ | ---- | ----- |
| Input Sentences | 2000 | 359 | 2359 |
| Reference Simplifications | 20000 | 3590 | 23590 |
The test and validation sets are the same as those of [TurkCorpus](https://github.com/cocoxu/simplification/). The split was random.
There are 19.04 tokens per reference on average (lower than 21.29 and 25.49 for TurkCorpus and HSplit, respectively). Most (17,245) of the referece sentences do not involve sentence splitting.
TURKCorpus does not contain a training set; many models use [WikiLarge](https://github.com/XingxingZhang/dress) (Zhang and Lapata, 2017) or [Wiki-Auto](https://github.com/chaojiang06/wiki-auto) (Jiang et. al 2020) for training.
Each input sentence has 8 associated reference simplified sentences. 2,359 input sentences are randomly split into 2,000 validation and 359 test sentences.
| | Dev | Test | Total |
| ----- | ------ | ---- | ----- |
| Input Sentences | 2000 | 359 | 2359 |
| Reference Simplifications | 16000 | 2872 | 18872 |
There are 21.29 tokens per reference on average.
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
In our setup, we use WikiAuto as training/validation corpus and ASSET and TURK as test corpora. ASSET and TURK have the same inputs but differ in their reference style. Researchers can thus conduct targeted evaluations based on the strategies that a model should learn.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
WikiAuto is the largest open text simplification dataset currently available. ASSET and TURK are high quality test sets that are compatible with WikiAuto.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
It's unique setup with multiple test sets makes the task interesting since it allows for evaluation of multiple generations and systems that simplify in different ways.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
simplification
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
We removed secondary annotations and focus on the simple `input->output` format, but combine the different sub-datasets.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
yes
#### Split Information
<!-- info: Describe how the new splits were created -->
<!-- scope: periscope -->
we split the original test set according to syntactic complexity of the source sentences. To characterize sentence syntactic complexity, we use the 8-level developmental level (d-level) scale proposed by [Covington et al. (2006)](https://www.researchgate.net/publication/254033869_How_complex_is_that_sentence_A_proposed_revision_of_the_Rosenberg_and_Abbeduto_D-Level_Scale) and the implementation of [Lu, Xiaofei (2010)](https://www.jbe-platform.com/content/journals/10.1075/ijcl.15.4.02lu).
We thus split the original test set into 8 subsets corresponding to the 8 d-levels assigned to source sentences. We obtain the following number of instances per level and average d-level of the dataset:
| Total nb. sentences | L0 | L1 | L2 | L3 | L4 | L5 | L6 | L7 | Mean Level |
|-------------------- | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ---------- |
| 359 | 166 | 0 | 58 | 32 | 5 | 28 | 7 | 63 | 2.38 |
#### Split Motivation
<!-- info: What aspects of the model's generation capacities were the splits created to test? -->
<!-- scope: periscope -->
The goal was to assess performance when simplifying source sentences with different syntactic structure and complexity.
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
There are recent supervised ([Martin et al., 2019](https://arxiv.org/abs/1910.02677), [Kriz et al., 2019](https://www.aclweb.org/anthology/N19-1317/), [Dong et al., 2019](https://www.aclweb.org/anthology/P19-1331/), [Zhang and Lapata, 2017](https://www.aclweb.org/anthology/D17-1062/)) and unsupervised ([Martin et al., 2020](https://arxiv.org/abs/2005.00352v1), [Kumar et al., 2020](https://www.aclweb.org/anthology/2020.acl-main.707/), [Surya et al., 2019](https://www.aclweb.org/anthology/P19-1198/)) text simplification models that can be used as baselines.
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
The common metric used for automatic evaluation is SARI [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029/).
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Simplification
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`, `BLEU`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
SARI: A simplification metric that considers both input and references to measure the "goodness" of words that are added, deleted, and kept.
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
The original authors of WikiAuto and ASSET used human evaluation to assess the fluency, adequacy, and simplicity (details provided in the paper). For TURK, the authors measured grammaticality, meaning-preservation, and simplicity gain (details in the paper).
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Wiki-Auto provides a new version of the Wikipedia corpus that is larger, contains 75% less defective pairs and has more complex rewrites than the previous WIKILARGE dataset.
ASSET was created in order to improve the evaluation of sentence simplification. It uses the same input sentences as the [TurkCorpus](https://github.com/cocoxu/simplification/) dataset from [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). The 2,359 input sentences of TurkCorpus are a sample of "standard" (not simple) sentences from the [Parallel Wikipedia Simplification (PWKP)](https://www.informatik.tu-darmstadt.de/ukp/research_6/data/sentence_simplification/simple_complex_sentence_pairs/index.en.jsp) dataset [(Zhu et al., 2010)](https://www.aclweb.org/anthology/C10-1152.pdf), which come from the August 22, 2009 version of Wikipedia. The sentences of TurkCorpus were chosen to be of similar length [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). No further information is provided on the sampling strategy.
The TurkCorpus dataset was developed in order to overcome some of the problems with sentence pairs from Standard and Simple Wikipedia: a large fraction of sentences were misaligned, or not actually simpler [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). However, TurkCorpus mainly focused on *lexical paraphrasing*, and so cannot be used to evaluate simplifications involving *compression* (deletion) or *sentence splitting*. HSplit [(Sulem et al., 2018)](https://www.aclweb.org/anthology/D18-1081.pdf), on the other hand, can only be used to evaluate sentence splitting. The reference sentences in ASSET include a wider variety of sentence rewriting strategies, combining splitting, compression and paraphrasing. Annotators were given examples of each kind of transformation individually, as well as all three transformations used at once, but were allowed to decide which transformations to use for any given sentence.
An example illustrating the differences between TurkCorpus, HSplit and ASSET is given below:
> **Original:** He settled in London, devoting himself chiefly to practical teaching.
>
> **TurkCorpus:** He rooted in London, devoting himself mainly to practical teaching.
>
> **HSplit:** He settled in London. He devoted himself chiefly to practical teaching.
>
> **ASSET:** He lived in London. He was a teacher.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
The goal is to communicate the same information as the source sentence using simpler words and grammar.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
Wikipedia
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The dataset uses language from Wikipedia: some demographic information is provided [here](https://en.wikipedia.org/wiki/Wikipedia:Who_writes_Wikipedia%3F).
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
The authors mention that they "extracted 138,095 article pairs from the 2019/09 Wikipedia dump using an improved version of the [WikiExtractor](https://github.com/attardi/wikiextractor) library". The [SpaCy](https://spacy.io/) library is used for sentence splitting.
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
crowd-sourced
#### Number of Raters
<!-- info: What is the number of raters -->
<!-- scope: telescope -->
11<n<50
#### Rater Qualifications
<!-- info: Describe the qualifications required of an annotator. -->
<!-- scope: periscope -->
WikiAuto (Figure Eight): No information provided.
ASSET (MTurk):
- Having a HIT approval rate over 95%, and over 1000 HITs approved. No other demographic or compensation information is provided.
- Passing a Qualification Test (appropriately simplifying sentences). Out of 100 workers, 42 passed the test.
- Being a resident of the United States, United Kingdom or Canada.
TURK (MTurk):
- Reference sentences were written by workers with HIT approval rate over 95%. No other demographic or compensation information is provided.
#### Raters per Training Example
<!-- info: How many annotators saw each training example? -->
<!-- scope: periscope -->
1
#### Raters per Test Example
<!-- info: How many annotators saw each test example? -->
<!-- scope: periscope -->
>5
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
yes
#### Which Annotation Service
<!-- info: Which annotation services were used? -->
<!-- scope: periscope -->
`Amazon Mechanical Turk`, `Appen`
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
WikiAuto: Sentence alignment labels were crowdsourced for 500 randomly sampled document pairs (10,123 sentence pairs total). The authors pre-selected several alignment candidates from English Wikipedia for each Simple Wikipedia sentence based on various similarity metrics, then asked the crowd-workers to annotate these pairs. Finally, they trained their alignment model on this manually annotated dataset to obtain automatically aligned sentences (138,095 document pairs, 488,332 sentence pairs).
No demographic annotation is provided for the crowd workers. The [Figure Eight](https://www.figure-eight.com/) platform now part of Appen) was used for the annotation process.
ASSET: The instructions given to the annotators are available [here](https://github.com/facebookresearch/asset/blob/master/crowdsourcing/AMT_AnnotationInstructions.pdf).
TURK: The references are crowdsourced from Amazon Mechanical Turk. The annotators were asked to provide simplifications without losing any information or splitting the input sentence. No other demographic or compensation information is provided in the TURKCorpus paper. The instructions given to the annotators are available in the paper.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
none
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
Both Figure Eight and Amazon Mechanical Turk raters forfeit the right to their data as part of their agreements.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
Since the dataset is created from Wikipedia/Simple Wikipedia, all the information contained in the dataset is already in the public domain.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
yes
#### Links and Summaries of Analysis Work
<!-- info: Provide links to and summaries of works analyzing these biases. -->
<!-- scope: microscope -->
The dataset may contain some social biases, as the input sentences are based on Wikipedia. Studies have shown that the English Wikipedia contains both gender biases [(Schmahl et al., 2020)](https://research.tudelft.nl/en/publications/is-wikipedia-succeeding-in-reducing-gender-bias-assessing-changes) and racial biases [(Adams et al., 2019)](https://journals.sagepub.com/doi/pdf/10.1177/2378023118823946).
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
All the data is in the public domain.
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
The dataset may contain some social biases, as the input sentences are based on Wikipedia. Studies have shown that the English Wikipedia contains both gender biases [(Schmahl et al., 2020)](https://research.tudelft.nl/en/publications/is-wikipedia-succeeding-in-reducing-gender-bias-assessing-changes) and racial biases [(Adams et al., 2019)](https://journals.sagepub.com/doi/pdf/10.1177/2378023118823946).
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Since the test datasets contains only 2,359 sentences that are derived from Wikipedia, they are limited to a small subset of topics present on Wikipedia.
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|
abdusah/masc_dev | abdusah | 2022-07-01T15:28:05Z | 279 | 0 | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:ar",
"license:cc-by-nc-4.0",
"region:us"
]
| []
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
license:
- cc-by-nc-4.0
multilinguality: []
paperswithcode_id: []
pretty_name: 'MASC'
size_categories:
source_datasets: []
task_categories: []
task_ids: []
---
# Dataset Card for MASC: MASSIVE ARABIC SPEECH CORPUS
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus
- **Repository:**
- **Paper:** https://dx.doi.org/10.21227/e1qb-jv46
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This corpus is a dataset that contains 1,000 hours of speech sampled at 16~kHz and crawled from over 700 YouTube channels. MASC is multi-regional, multi-genre, and multi-dialect dataset that is intended to advance the research and development of Arabic speech technology with the special emphasis on Arabic speech recognition
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Multi-dialect Arabic
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
#### masc_dev
- speech
- sampling_rate
- target_text (label)
### Data Splits
#### masc_dev
- train: 100
- test: 40
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
Note: this is a small development set for testing.
### Dataset Curators
[More Information Needed]
### Licensing Information
CC 4.0
### Citation Information
[More Information Needed]
### Contributions
Mohammad Al-Fetyani, Muhammad Al-Barham, Gheith Abandah, Adham Alsharkawi, Maha Dawas, August 18, 2021, "MASC: Massive Arabic Speech Corpus", IEEE Dataport, doi: https://dx.doi.org/10.21227/e1qb-jv46.
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|
huggingartists/ariana-grande | huggingartists | 2022-10-25T09:23:36Z | 279 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ariana-grande"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.997954 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/d36a47955ac0ddb12748c5e7c2bd4b4b.640x640x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/ariana-grande">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ariana Grande</div>
<a href="https://genius.com/artists/ariana-grande">
<div style="text-align: center; font-size: 14px;">@ariana-grande</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/ariana-grande).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/ariana-grande")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|596| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/ariana-grande")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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|
EMBO/BLURB | EMBO | 2022-12-09T07:57:37Z | 279 | 3 | [
"task_categories:question-answering",
"task_categories:token-classification",
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_ids:closed-domain-qa",
"task_ids:named-entity-recognition",
"task_ids:parsing",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2007.15779",
"arxiv:1909.06146",
"region:us"
]
| [
"question-answering",
"token-classification",
"sentence-similarity",
"text-classification"
]
| 2022-03-14T10:29:16Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- token-classification
- sentence-similarity
- text-classification
task_ids:
- closed-domain-qa
- named-entity-recognition
- parsing
- semantic-similarity-scoring
- text-scoring
- topic-classification
pretty_name: BLURB (Biomedical Language Understanding and Reasoning Benchmark.)
---
# Dataset Card for BLURB
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://microsoft.github.io/BLURB/index.html
- **Paper:** [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://arxiv.org/pdf/2007.15779.pdf)
- **Leaderboard:** https://microsoft.github.io/BLURB/leaderboard.html
- **Point of Contact:**
### Dataset Summary
BLURB is a collection of resources for biomedical natural language processing. In general domains, such as newswire and the Web, comprehensive benchmarks and leaderboards such as GLUE have greatly accelerated progress in open-domain NLP. In biomedicine, however, such resources are ostensibly scarce. In the past, there have been a plethora of shared tasks in biomedical NLP, such as BioCreative, BioNLP Shared Tasks, SemEval, and BioASQ, to name just a few. These efforts have played a significant role in fueling interest and progress by the research community, but they typically focus on individual tasks. The advent of neural language models, such as BERT provides a unifying foundation to leverage transfer learning from unlabeled text to support a wide range of NLP applications. To accelerate progress in biomedical pretraining strategies and task-specific methods, it is thus imperative to create a broad-coverage benchmark encompassing diverse biomedical tasks.
Inspired by prior efforts toward this direction (e.g., BLUE), we have created BLURB (short for Biomedical Language Understanding and Reasoning Benchmark). BLURB comprises of a comprehensive benchmark for PubMed-based biomedical NLP applications, as well as a leaderboard for tracking progress by the community. BLURB includes thirteen publicly available datasets in six diverse tasks. To avoid placing undue emphasis on tasks with many available datasets, such as named entity recognition (NER), BLURB reports the macro average across all tasks as the main score. The BLURB leaderboard is model-agnostic. Any system capable of producing the test predictions using the same training and development data can participate. The main goal of BLURB is to lower the entry barrier in biomedical NLP and help accelerate progress in this vitally important field for positive societal and human impact.
#### BC5-chem
The corpus consists of three separate sets of
articles with diseases, chemicals and their relations annotated.
The training (500 articles) and development (500 articles) sets
were released to task participants in advance to support text-mining
method development. The test set (500 articles) was used for final
system performance evaluation.
- **Homepage:** https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper:** [BioCreative V CDR task corpus: a resource for chemical disease relation extraction](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/)
#### BC5-disease
The corpus consists of three separate sets of
articles with diseases, chemicals and their relations annotated.
The training (500 articles) and development (500 articles) sets
were released to task participants in advance to support text-mining
method development. The test set (500 articles) was used for final
system performance evaluation.
- **Homepage:** https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper:** [BioCreative V CDR task corpus: a resource for chemical disease relation extraction](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/)
#### BC2GM
The BioCreative II Gene Mention task.
The training corpus for the current task consists mainly of
the training and testing corpora (text collections) from the
BCI task, and the testing corpus for the current task
consists of an additional 5,000 sentences that were held
'in reserve' from the previous task.
In the current corpus, tokenization is not provided;
instead participants are asked to identify a gene mention
in a sentence by giving its start and end characters.
As before, the training set consists of a set of sentences,
and for each sentence a set of gene mentions
(GENE annotations).
- **Homepage:** https://biocreative.bioinformatics.udel.edu/tasks/biocreative-ii/task-1a-gene-mention-tagging/
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper:** [verview of BioCreative II gene mention recognition](https://link.springer.com/article/10.1186/gb-2008-9-s2-s2)
#### NCBI Disease
The NCBI disease corpus is fully annotated at the mention
and concept level to serve as a research resource for the biomedical natural
language processing community.
Corpus Characteristics
----------------------
* 793 PubMed abstracts
* 6,892 disease mentions
* 790 unique disease concepts
* Medical Subject Headings (MeSH®)
* Online Mendelian Inheritance in Man (OMIM®)
* 91% of the mentions map to a single disease concept
**divided into training, developing and testing sets.
Corpus Annotation
* Fourteen annotators
* Two-annotators per document (randomly paired)
* Three annotation phases
* Checked for corpus-wide consistency of annotations
- **Homepage:** https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper:** [NCBI disease corpus: a resource for disease name recognition and concept normalization](https://pubmed.ncbi.nlm.nih.gov/24393765/)
#### JNLPBA
The BioNLP / JNLPBA Shared Task 2004 involves the identification
and classification of technical terms referring to concepts of interest to
biologists in the domain of molecular biology. The task was organized by GENIA
Project based on the annotations of the GENIA Term corpus (version 3.02).
Corpus format: The JNLPBA corpus is distributed in IOB format, with each line
containing a single token and its tag, separated by a tab character.
Sentences are separated by blank lines.
- **Homepage: ** http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper: ** [Introduction to the Bio-entity Recognition Task at JNLPBA](https://aclanthology.org/W04-1213)
#### EBM PICO
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
#### ChemProt
- **Homepage:**
- **Repository:**
- **Paper:**
#### DDI
- **Homepage:**
- **Repository:**
- **Paper:**
#### GAD
- **Homepage:**
- **Repository:**
- **Paper:**
#### BIOSSES
BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/) containing articles from the biomedical domain. The sentence pairs in BIOSSES were selected from citing sentences, i.e. sentences that have a citation to a reference article.
The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). In the original paper the mean of the scores assigned by the five human annotators was taken as the gold standard. The Pearson correlation between the gold standard scores and the scores estimated by the models was used as the evaluation metric. The strength of correlation can be assessed by the general guideline proposed by Evans (1996) as follows:
- very strong: 0.80–1.00
- strong: 0.60–0.79
- moderate: 0.40–0.59
- weak: 0.20–0.39
- very weak: 0.00–0.19
- **Homepage:** https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html
- **Repository:** https://github.com/gizemsogancioglu/biosses
- **Paper:** [BIOSSES: a semantic sentence similarity estimation system for the biomedical domain](https://academic.oup.com/bioinformatics/article/33/14/i49/3953954)
- **Point of Contact:** [Gizem Soğancıoğlu]([email protected]) and [Arzucan Özgür]([email protected])
#### HoC
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
#### PubMedQA
We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts. PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances. Each PubMedQA instance is composed of (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. Our best performing model, multi-phase fine-tuning of BioBERT with long answer bag-of-word statistics as additional supervision, achieves 68.1% accuracy, compared to single human performance of 78.0% accuracy and majority-baseline of 55.2% accuracy, leaving much room for improvement. PubMedQA is publicly available at this https URL.
- **Homepage:** https://pubmedqa.github.io/
- **Repository:** https://github.com/pubmedqa/pubmedqa
- **Paper:** [PubMedQA: A Dataset for Biomedical Research Question Answering](https://arxiv.org/pdf/1909.06146.pdf)
- **Leaderboard:** [Question answering](https://pubmedqa.github.io/)
- **Point of Contact:**
#### BioASQ
Task 7b will use benchmark datasets containing training and test biomedical questions, in English, along with gold standard (reference) answers. The participants will have to respond to each test question with relevant concepts (from designated terminologies and ontologies), relevant articles (in English, from designated article repositories), relevant snippets (from the relevant articles), relevant RDF triples (from designated ontologies), exact answers (e.g., named entities in the case of factoid questions) and 'ideal' answers (English paragraph-sized summaries). 2747 training questions (that were used as dry-run or test questions in previous year) are already available, along with their gold standard answers (relevant concepts, articles, snippets, exact answers, summaries).
- **Homepage:** http://bioasq.org/
- **Repository:** http://participants-area.bioasq.org/datasets/
- **Paper:** [Automatic semantic classification of scientific literature according to the hallmarks of cancer](https://academic.oup.com/bioinformatics/article/32/3/432/1743783?login=false)
### Supported Tasks and Leaderboards
| **Dataset** | **Task** | **Train** | **Dev** | **Test** | **Evaluation Metrics** | **Added** |
|:------------:|:-----------------------:|:---------:|:-------:|:--------:|:----------------------:|-----------|
| BC5-chem | NER | 5203 | 5347 | 5385 | F1 entity-level | **Yes** |
| BC5-disease | NER | 4182 | 4244 | 4424 | F1 entity-level | **Yes** |
| NCBI-disease | NER | 5134 | 787 | 960 | F1 entity-level | **Yes** |
| BC2GM | NER | 15197 | 3061 | 6325 | F1 entity-level | **Yes** |
| JNLPBA | NER | 46750 | 4551 | 8662 | F1 entity-level | **Yes** |
| EBM PICO | PICO | 339167 | 85321 | 16364 | Macro F1 word-level | No |
| ChemProt | Relation Extraction | 18035 | 11268 | 15745 | Micro F1 | No |
| DDI | Relation Extraction | 25296 | 2496 | 5716 | Micro F1 | No |
| GAD | Relation Extraction | 4261 | 535 | 534 | Micro F1 | No |
| BIOSSES | Sentence Similarity | 64 | 16 | 20 | Pearson | **Yes** |
| HoC | Document Classification | 1295 | 186 | 371 | Average Micro F1 | No |
| PubMedQA | Question Answering | 450 | 50 | 500 | Accuracy | **Yes** |
| BioASQ | Question Answering | 670 | 75 | 140 | Accuracy | No |
Datasets used in the BLURB biomedical NLP benchmark. The Train, Dev, and test splits might not be exactly identical to those proposed in BLURB.
This is something to be checked.
### Languages
English from biomedical texts
## Dataset Structure
### Data Instances
* **NER**
```json
{
'id': 0,
'tokens': [ "DPP6", "as", "a", "candidate", "gene", "for", "neuroleptic", "-", "induced", "tardive", "dyskinesia", "." ]
'ner_tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
}
```
* **PICO**
```json
{
'TBD'
}
```
* **Relation Extraction**
```json
{
'TBD'
}
```
* **Sentence Similarity**
```json
{'sentence 1': 'Here, looking for agents that could specifically kill KRAS mutant cells, they found that knockdown of GATA2 was synthetically lethal with KRAS mutation'
'sentence 2': 'Not surprisingly, GATA2 knockdown in KRAS mutant cells resulted in a striking reduction of active GTP-bound RHO proteins, including the downstream ROCK kinase'
'score': 2.2}
```
* **Document Classification**
```json
{
'TBD'
}
```
* **Question Answering**
* PubMedQA
```json
{'context': {'contexts': ['Programmed cell death (PCD) is the regulated death of cells within an organism. The lace plant (Aponogeton madagascariensis) produces perforations in its leaves through PCD. The leaves of the plant consist of a latticework of longitudinal and transverse veins enclosing areoles. PCD occurs in the cells at the center of these areoles and progresses outwards, stopping approximately five cells from the vasculature. The role of mitochondria during PCD has been recognized in animals; however, it has been less studied during PCD in plants.',
'The following paper elucidates the role of mitochondrial dynamics during developmentally regulated PCD in vivo in A. madagascariensis. A single areole within a window stage leaf (PCD is occurring) was divided into three areas based on the progression of PCD; cells that will not undergo PCD (NPCD), cells in early stages of PCD (EPCD), and cells in late stages of PCD (LPCD). Window stage leaves were stained with the mitochondrial dye MitoTracker Red CMXRos and examined. Mitochondrial dynamics were delineated into four categories (M1-M4) based on characteristics including distribution, motility, and membrane potential (ΔΨm). A TUNEL assay showed fragmented nDNA in a gradient over these mitochondrial stages. Chloroplasts and transvacuolar strands were also examined using live cell imaging. The possible importance of mitochondrial permeability transition pore (PTP) formation during PCD was indirectly examined via in vivo cyclosporine A (CsA) treatment. This treatment resulted in lace plant leaves with a significantly lower number of perforations compared to controls, and that displayed mitochondrial dynamics similar to that of non-PCD cells.'],
'labels': ['BACKGROUND', 'RESULTS'],
'meshes': ['Alismataceae',
'Apoptosis',
'Cell Differentiation',
'Mitochondria',
'Plant Leaves'],
'reasoning_free_pred': ['y', 'e', 's'],
'reasoning_required_pred': ['y', 'e', 's']},
'final_decision': 'yes',
'long_answer': 'Results depicted mitochondrial dynamics in vivo as PCD progresses within the lace plant, and highlight the correlation of this organelle with other organelles during developmental PCD. To the best of our knowledge, this is the first report of mitochondria and chloroplasts moving on transvacuolar strands to form a ring structure surrounding the nucleus during developmental PCD. Also, for the first time, we have shown the feasibility for the use of CsA in a whole plant system. Overall, our findings implicate the mitochondria as playing a critical and early role in developmentally regulated PCD in the lace plant.',
'pubid': 21645374,
'question': 'Do mitochondria play a role in remodelling lace plant leaves during programmed cell death?'}
```
### Data Fields
* **NER**
* `id`: string
* `ner_tags`: Sequence[ClassLabel]
* `tokens`: Sequence[String]
* **PICO**
* To be added
* **Relation Extraction**
* To be added
* **Sentence Similarity**
* `sentence 1`: string
* `sentence 2`: string
* `score`: float ranging from 0 (no relation) to 4 (equivalent)
* **Document Classification**
* To be added
* **Question Answering**
* PubMedQA
* `pubid`: integer
* `question`: string
* `context`: sequence of strings [`contexts`, `labels`, `meshes`, `reasoning_required_pred`, `reasoning_free_pred`]
* `long_answer`: string
* `final_decision`: string
### Data Splits
Shown in the table of supported tasks.
## Dataset Creation
### Curation Rationale
* BC5-chem
* BC5-disease
* BC2GM
* JNLPBA
* EBM PICO
* ChemProt
* DDI
* GAD
* BIOSSES
* HoC
* PubMedQA
* BioASQ
### Source Data
[More Information Needed]
### Annotations
All the datasets have been obtained and annotated by experts in the biomedical domain. Check the different citations for further details.
#### Annotation process
* BC5-chem
* BC5-disease
* BC2GM
* JNLPBA
* EBM PICO
* ChemProt
* DDI
* GAD
* BIOSSES - The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). The score range was described based on the guidelines of SemEval 2012 Task 6 on STS (Agirre et al., 2012). Besides the annotation instructions, example sentences from the biomedical literature were provided to the annotators for each of the similarity degrees.
* HoC
* PubMedQA
* BioASQ
### Dataset Curators
All the datasets have been obtained and annotated by experts in thebiomedical domain. Check the different citations for further details.
### Licensing Information
* BC5-chem
* BC5-disease
* BC2GM
* JNLPBA
* EBM PICO
* ChemProt
* DDI
* GAD
* BIOSSES - BIOSSES is made available under the terms of [The GNU Common Public License v.3.0](https://www.gnu.org/licenses/gpl-3.0.en.html).
* HoC
* PubMedQA - MIT License Copyright (c) 2019 pubmedqa
* BioASQ
### Citation Information
* BC5-chem & BC5-disease
```latex
@article{article,
author = {Li, Jiao and Sun, Yueping and Johnson, Robin and Sciaky, Daniela and Wei, Chih-Hsuan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn and Wiegers, Thomas and lu, Zhiyong},
year = {2016},
month = {05},
pages = {baw068},
title = {BioCreative V CDR task corpus: a resource for chemical disease relation extraction},
volume = {2016},
journal = {Database},
doi = {10.1093/database/baw068}
}
```
* BC2GM
```latex
@article{article,
author = {Smith, Larry and Tanabe, Lorraine and Ando, Rie and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph and Ganchev, Kuzman and Torii, Manabu and Liu, Hongfang and Haddow, Barry and Struble, Craig and Povinelli, Richard and Vlachos, Andreas and Baumgartner Jr, William and Hunter, Lawrence and Carpenter, Bob and Wilbur, W.},
year = {2008},
month = {09},
pages = {S2},
title = {Overview of BioCreative II gene mention recognition},
volume = {9 Suppl 2},
journal = {Genome biology},
doi = {10.1186/gb-2008-9-s2-s2}
}
```
* JNLPBA
```latex
@inproceedings{collier-kim-2004-introduction,
title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
author = "Collier, Nigel and
Kim, Jin-Dong",
booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})",
month = aug # " 28th and 29th",
year = "2004",
address = "Geneva, Switzerland",
publisher = "COLING",
url = "https://aclanthology.org/W04-1213",
pages = "73--78",
}
```
* NCBI Disiease
```latex
@article{10.5555/2772763.2772800,
author = {Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
title = {NCBI Disease Corpus},
year = {2014},
issue_date = {February 2014},
publisher = {Elsevier Science},
address = {San Diego, CA, USA},
volume = {47},
number = {C},
issn = {1532-0464},
abstract = {Graphical abstractDisplay Omitted NCBI disease corpus is built as a gold-standard resource for disease recognition.793 PubMed abstracts are annotated with disease mentions and concepts (MeSH/OMIM).14 Annotators produced high consistency level and inter-annotator agreement.Normalization benchmark results demonstrate the utility of the corpus.The corpus is publicly available to the community. Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information, however, the development of powerful, highly effective tools to automatically detect central biomedical concepts such as diseases is conditional on the availability of annotated corpora.This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH ) or Online Mendelian Inheritance in Man (OMIM ). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency.The public release of the NCBI disease corpus contains 6892 disease mentions, which are mapped to 790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the rest contain an OMIM identifier. We were able to link 91% of the mentions to a single disease concept, while the rest are described as a combination of concepts. In order to help researchers use the corpus to design and test disease identification methods, we have prepared the corpus as training, testing and development sets. To demonstrate its utility, we conducted a benchmarking experiment where we compared three different knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/.},
journal = {J. of Biomedical Informatics},
month = {feb},
pages = {1–10},
numpages = {10}}
```
* EBM PICO
* ChemProt
* DDI
* GAD
* BIOSSES
```latex
@article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={So{\u{g}}anc{\i}o{\u{g}}lu, Gizem and {\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
year={2017},
publisher={Oxford University Press}
}
```
* HoC
* PubMedQA
```latex
@inproceedings{jin2019pubmedqa,
title={PubMedQA: A Dataset for Biomedical Research Question Answering},
author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
pages={2567--2577},
year={2019}
}
```
* BioASQ
```latex
@article{10.1093/bioinformatics/btv585,
author = {Baker, Simon and Silins, Ilona and Guo, Yufan and Ali, Imran and Högberg, Johan and Stenius, Ulla and Korhonen, Anna},
title = "{Automatic semantic classification of scientific literature according to the hallmarks of cancer}",
journal = {Bioinformatics},
volume = {32},
number = {3},
pages = {432-440},
year = {2015},
month = {10},
abstract = "{Motivation: The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research.Results: We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future.Availability and implementation: The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html .Contact:[email protected]}",
issn = {1367-4803},
doi = {10.1093/bioinformatics/btv585},
url = {https://doi.org/10.1093/bioinformatics/btv585},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/32/3/432/19568147/btv585.pdf},
}
```
### Contributions
* This dataset has been uploaded and generated by Dr. Jorge Abreu Vicente.
* Thanks to [@GamalC](https://github.com/GamalC) for uploading the NER datasets to GitHub, from where I got them.
* I am not part of the team that generated BLURB. This dataset is intended to help researchers to usethe BLURB benchmarking for NLP in Biomedical NLP.
* Thanks to [@bwang482](https://github.com/bwang482) for uploading the [BIOSSES dataset](https://github.com/bwang482/datasets/tree/master/datasets/biosses). We forked the [BIOSSES 🤗 dataset](https://huggingface.co/datasets/biosses) to add it to this BLURB benchmark.
* Thank you to [@tuner007](https://github.com/tuner007) for adding this dataset to the 🤗 hub | [
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|
open-llm-leaderboard/details_notstoic__PygmalionCoT-7b | open-llm-leaderboard | 2023-09-22T15:06:51Z | 279 | 0 | [
"region:us"
]
| null | 2023-08-17T23:56:26Z | ---
pretty_name: Evaluation run of notstoic/PygmalionCoT-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [notstoic/PygmalionCoT-7b](https://huggingface.co/notstoic/PygmalionCoT-7b) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_notstoic__PygmalionCoT-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T15:06:38.792335](https://huggingface.co/datasets/open-llm-leaderboard/details_notstoic__PygmalionCoT-7b/blob/main/results_2023-09-22T15-06-38.792335.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.12111996644295302,\n\
\ \"em_stderr\": 0.0033412757702121106,\n \"f1\": 0.17514471476510068,\n\
\ \"f1_stderr\": 0.0034689450739406216,\n \"acc\": 0.36081482886571287,\n\
\ \"acc_stderr\": 0.00895060187911282\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.12111996644295302,\n \"em_stderr\": 0.0033412757702121106,\n\
\ \"f1\": 0.17514471476510068,\n \"f1_stderr\": 0.0034689450739406216\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.032600454890068235,\n \
\ \"acc_stderr\": 0.004891669021939579\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6890292028413575,\n \"acc_stderr\": 0.01300953473628606\n\
\ }\n}\n```"
repo_url: https://huggingface.co/notstoic/PygmalionCoT-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|arc:challenge|25_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T15_06_38.792335
path:
- '**/details_harness|drop|3_2023-09-22T15-06-38.792335.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T15-06-38.792335.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T15_06_38.792335
path:
- '**/details_harness|gsm8k|5_2023-09-22T15-06-38.792335.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T15-06-38.792335.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hellaswag|10_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:24:33.017908.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:24:33.017908.parquet'
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- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:24:33.017908.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-18T12:24:33.017908.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-18T12:24:33.017908.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T15_06_38.792335
path:
- '**/details_harness|winogrande|5_2023-09-22T15-06-38.792335.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T15-06-38.792335.parquet'
- config_name: results
data_files:
- split: 2023_07_18T12_24_33.017908
path:
- results_2023-07-18T12:24:33.017908.parquet
- split: 2023_09_22T15_06_38.792335
path:
- results_2023-09-22T15-06-38.792335.parquet
- split: latest
path:
- results_2023-09-22T15-06-38.792335.parquet
---
# Dataset Card for Evaluation run of notstoic/PygmalionCoT-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/notstoic/PygmalionCoT-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [notstoic/PygmalionCoT-7b](https://huggingface.co/notstoic/PygmalionCoT-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_notstoic__PygmalionCoT-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T15:06:38.792335](https://huggingface.co/datasets/open-llm-leaderboard/details_notstoic__PygmalionCoT-7b/blob/main/results_2023-09-22T15-06-38.792335.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.12111996644295302,
"em_stderr": 0.0033412757702121106,
"f1": 0.17514471476510068,
"f1_stderr": 0.0034689450739406216,
"acc": 0.36081482886571287,
"acc_stderr": 0.00895060187911282
},
"harness|drop|3": {
"em": 0.12111996644295302,
"em_stderr": 0.0033412757702121106,
"f1": 0.17514471476510068,
"f1_stderr": 0.0034689450739406216
},
"harness|gsm8k|5": {
"acc": 0.032600454890068235,
"acc_stderr": 0.004891669021939579
},
"harness|winogrande|5": {
"acc": 0.6890292028413575,
"acc_stderr": 0.01300953473628606
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_concedo__Vicuzard-30B-Uncensored | open-llm-leaderboard | 2023-09-23T02:47:48Z | 279 | 0 | [
"region:us"
]
| null | 2023-08-18T11:52:24Z | ---
pretty_name: Evaluation run of concedo/Vicuzard-30B-Uncensored
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [concedo/Vicuzard-30B-Uncensored](https://huggingface.co/concedo/Vicuzard-30B-Uncensored)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_concedo__Vicuzard-30B-Uncensored\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T02:47:37.236097](https://huggingface.co/datasets/open-llm-leaderboard/details_concedo__Vicuzard-30B-Uncensored/blob/main/results_2023-09-23T02-47-37.236097.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.17365771812080538,\n\
\ \"em_stderr\": 0.003879418958892462,\n \"f1\": 0.2676352768456391,\n\
\ \"f1_stderr\": 0.003979938331768844,\n \"acc\": 0.46250866906059396,\n\
\ \"acc_stderr\": 0.010873579764037198\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.17365771812080538,\n \"em_stderr\": 0.003879418958892462,\n\
\ \"f1\": 0.2676352768456391,\n \"f1_stderr\": 0.003979938331768844\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.15390447308567096,\n \
\ \"acc_stderr\": 0.009939799304049\n },\n \"harness|winogrande|5\": {\n\
\ \"acc\": 0.771112865035517,\n \"acc_stderr\": 0.011807360224025395\n\
\ }\n}\n```"
repo_url: https://huggingface.co/concedo/Vicuzard-30B-Uncensored
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|arc:challenge|25_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T02_47_37.236097
path:
- '**/details_harness|drop|3_2023-09-23T02-47-37.236097.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T02-47-37.236097.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T02_47_37.236097
path:
- '**/details_harness|gsm8k|5_2023-09-23T02-47-37.236097.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T02-47-37.236097.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hellaswag|10_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:20:40.681862.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:20:40.681862.parquet'
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- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:20:40.681862.parquet'
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path:
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- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:20:40.681862.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T22:20:40.681862.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T22:20:40.681862.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T02_47_37.236097
path:
- '**/details_harness|winogrande|5_2023-09-23T02-47-37.236097.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T02-47-37.236097.parquet'
- config_name: results
data_files:
- split: 2023_07_19T22_20_40.681862
path:
- results_2023-07-19T22:20:40.681862.parquet
- split: 2023_09_23T02_47_37.236097
path:
- results_2023-09-23T02-47-37.236097.parquet
- split: latest
path:
- results_2023-09-23T02-47-37.236097.parquet
---
# Dataset Card for Evaluation run of concedo/Vicuzard-30B-Uncensored
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/concedo/Vicuzard-30B-Uncensored
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [concedo/Vicuzard-30B-Uncensored](https://huggingface.co/concedo/Vicuzard-30B-Uncensored) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_concedo__Vicuzard-30B-Uncensored",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T02:47:37.236097](https://huggingface.co/datasets/open-llm-leaderboard/details_concedo__Vicuzard-30B-Uncensored/blob/main/results_2023-09-23T02-47-37.236097.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.17365771812080538,
"em_stderr": 0.003879418958892462,
"f1": 0.2676352768456391,
"f1_stderr": 0.003979938331768844,
"acc": 0.46250866906059396,
"acc_stderr": 0.010873579764037198
},
"harness|drop|3": {
"em": 0.17365771812080538,
"em_stderr": 0.003879418958892462,
"f1": 0.2676352768456391,
"f1_stderr": 0.003979938331768844
},
"harness|gsm8k|5": {
"acc": 0.15390447308567096,
"acc_stderr": 0.009939799304049
},
"harness|winogrande|5": {
"acc": 0.771112865035517,
"acc_stderr": 0.011807360224025395
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_codellama__CodeLlama-34b-hf | open-llm-leaderboard | 2023-09-17T13:13:30Z | 279 | 0 | [
"region:us"
]
| null | 2023-08-26T05:34:05Z | ---
pretty_name: Evaluation run of codellama/CodeLlama-34b-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_codellama__CodeLlama-34b-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T13:13:18.038521](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-34b-hf/blob/main/results_2023-09-17T13-13-18.038521.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0017827181208053692,\n\
\ \"em_stderr\": 0.0004320097346038763,\n \"f1\": 0.049458892617449755,\n\
\ \"f1_stderr\": 0.0012523570997250966,\n \"acc\": 0.43630162765913527,\n\
\ \"acc_stderr\": 0.011053010908838208\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0017827181208053692,\n \"em_stderr\": 0.0004320097346038763,\n\
\ \"f1\": 0.049458892617449755,\n \"f1_stderr\": 0.0012523570997250966\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1425322213798332,\n \
\ \"acc_stderr\": 0.009629588445673819\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7300710339384373,\n \"acc_stderr\": 0.012476433372002597\n\
\ }\n}\n```"
repo_url: https://huggingface.co/codellama/CodeLlama-34b-hf
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|arc:challenge|25_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T13_13_18.038521
path:
- '**/details_harness|drop|3_2023-09-17T13-13-18.038521.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T13-13-18.038521.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T13_13_18.038521
path:
- '**/details_harness|gsm8k|5_2023-09-17T13-13-18.038521.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T13-13-18.038521.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hellaswag|10_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T05:33:43.008439.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T05:33:43.008439.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T05:33:43.008439.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T05:33:43.008439.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T13_13_18.038521
path:
- '**/details_harness|winogrande|5_2023-09-17T13-13-18.038521.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T13-13-18.038521.parquet'
- config_name: results
data_files:
- split: 2023_08_26T05_33_43.008439
path:
- results_2023-08-26T05:33:43.008439.parquet
- split: 2023_09_17T13_13_18.038521
path:
- results_2023-09-17T13-13-18.038521.parquet
- split: latest
path:
- results_2023-09-17T13-13-18.038521.parquet
---
# Dataset Card for Evaluation run of codellama/CodeLlama-34b-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/codellama/CodeLlama-34b-hf
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_codellama__CodeLlama-34b-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T13:13:18.038521](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-34b-hf/blob/main/results_2023-09-17T13-13-18.038521.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0017827181208053692,
"em_stderr": 0.0004320097346038763,
"f1": 0.049458892617449755,
"f1_stderr": 0.0012523570997250966,
"acc": 0.43630162765913527,
"acc_stderr": 0.011053010908838208
},
"harness|drop|3": {
"em": 0.0017827181208053692,
"em_stderr": 0.0004320097346038763,
"f1": 0.049458892617449755,
"f1_stderr": 0.0012523570997250966
},
"harness|gsm8k|5": {
"acc": 0.1425322213798332,
"acc_stderr": 0.009629588445673819
},
"harness|winogrande|5": {
"acc": 0.7300710339384373,
"acc_stderr": 0.012476433372002597
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_yeontaek__llama-2-70B-ensemble-v3 | open-llm-leaderboard | 2023-09-01T14:03:22Z | 279 | 0 | [
"region:us"
]
| null | 2023-09-01T14:02:23Z | ---
pretty_name: Evaluation run of yeontaek/llama-2-70B-ensemble-v3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [yeontaek/llama-2-70B-ensemble-v3](https://huggingface.co/yeontaek/llama-2-70B-ensemble-v3)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_yeontaek__llama-2-70B-ensemble-v3\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-01T14:01:58.848407](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-70B-ensemble-v3/blob/main/results_2023-09-01T14%3A01%3A58.848407.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6813782482106774,\n\
\ \"acc_stderr\": 0.03171011741691581,\n \"acc_norm\": 0.6847848607826429,\n\
\ \"acc_norm_stderr\": 0.031684498624315015,\n \"mc1\": 0.45532435740514077,\n\
\ \"mc1_stderr\": 0.01743349010253877,\n \"mc2\": 0.6421820394674438,\n\
\ \"mc2_stderr\": 0.015085186356964665\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6621160409556314,\n \"acc_stderr\": 0.013822047922283504,\n\
\ \"acc_norm\": 0.6851535836177475,\n \"acc_norm_stderr\": 0.013572657703084948\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6936865166301533,\n\
\ \"acc_stderr\": 0.004600194559865542,\n \"acc_norm\": 0.8716391157140012,\n\
\ \"acc_norm_stderr\": 0.003338076015617253\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\
\ \"acc_stderr\": 0.042446332383532286,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.042446332383532286\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7828947368421053,\n \"acc_stderr\": 0.03355045304882924,\n\
\ \"acc_norm\": 0.7828947368421053,\n \"acc_norm_stderr\": 0.03355045304882924\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.69,\n\
\ \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \
\ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7622641509433963,\n \"acc_stderr\": 0.02619980880756192,\n\
\ \"acc_norm\": 0.7622641509433963,\n \"acc_norm_stderr\": 0.02619980880756192\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.031164899666948617,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.031164899666948617\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \
\ \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.049999999999999996\n \
\ },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\"\
: 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n\
\ \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.653179190751445,\n \"acc_stderr\": 0.036291466701596636,\n\
\ \"acc_norm\": 0.653179190751445,\n \"acc_norm_stderr\": 0.036291466701596636\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4215686274509804,\n\
\ \"acc_stderr\": 0.04913595201274498,\n \"acc_norm\": 0.4215686274509804,\n\
\ \"acc_norm_stderr\": 0.04913595201274498\n },\n \"harness|hendrycksTest-computer_security|5\"\
: {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \
\ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n \
\ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6340425531914894,\n\
\ \"acc_stderr\": 0.0314895582974553,\n \"acc_norm\": 0.6340425531914894,\n\
\ \"acc_norm_stderr\": 0.0314895582974553\n },\n \"harness|hendrycksTest-econometrics|5\"\
: {\n \"acc\": 0.42105263157894735,\n \"acc_stderr\": 0.04644602091222318,\n\
\ \"acc_norm\": 0.42105263157894735,\n \"acc_norm_stderr\": 0.04644602091222318\n\
\ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\
: 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n \"\
acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.48412698412698413,\n \"acc_stderr\": 0.025738330639412152,\n \"\
acc_norm\": 0.48412698412698413,\n \"acc_norm_stderr\": 0.025738330639412152\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677173,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677173\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8225806451612904,\n \"acc_stderr\": 0.021732540689329286,\n \"\
acc_norm\": 0.8225806451612904,\n \"acc_norm_stderr\": 0.021732540689329286\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n \"\
acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\
: 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781678,\n\
\ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781678\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8535353535353535,\n \"acc_stderr\": 0.025190921114603918,\n \"\
acc_norm\": 0.8535353535353535,\n \"acc_norm_stderr\": 0.025190921114603918\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9430051813471503,\n \"acc_stderr\": 0.01673108529360755,\n\
\ \"acc_norm\": 0.9430051813471503,\n \"acc_norm_stderr\": 0.01673108529360755\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6923076923076923,\n \"acc_stderr\": 0.02340092891831049,\n \
\ \"acc_norm\": 0.6923076923076923,\n \"acc_norm_stderr\": 0.02340092891831049\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.02866120111652459,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.02866120111652459\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.773109243697479,\n \"acc_stderr\": 0.027205371538279476,\n \
\ \"acc_norm\": 0.773109243697479,\n \"acc_norm_stderr\": 0.027205371538279476\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8807339449541285,\n \"acc_stderr\": 0.013895729292588949,\n \"\
acc_norm\": 0.8807339449541285,\n \"acc_norm_stderr\": 0.013895729292588949\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538272,\n \"\
acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538272\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9068627450980392,\n \"acc_stderr\": 0.020397853969426998,\n \"\
acc_norm\": 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969426998\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.890295358649789,\n \"acc_stderr\": 0.02034340073486884,\n \
\ \"acc_norm\": 0.890295358649789,\n \"acc_norm_stderr\": 0.02034340073486884\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7802690582959642,\n\
\ \"acc_stderr\": 0.027790177064383602,\n \"acc_norm\": 0.7802690582959642,\n\
\ \"acc_norm_stderr\": 0.027790177064383602\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\
\ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8347107438016529,\n \"acc_stderr\": 0.03390780612972776,\n \"\
acc_norm\": 0.8347107438016529,\n \"acc_norm_stderr\": 0.03390780612972776\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\
\ \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.7592592592592593,\n\
\ \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8343558282208589,\n \"acc_stderr\": 0.029208296231259104,\n\
\ \"acc_norm\": 0.8343558282208589,\n \"acc_norm_stderr\": 0.029208296231259104\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5625,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.5625,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8931623931623932,\n\
\ \"acc_stderr\": 0.020237149008990915,\n \"acc_norm\": 0.8931623931623932,\n\
\ \"acc_norm_stderr\": 0.020237149008990915\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8607918263090677,\n\
\ \"acc_stderr\": 0.012378786101885145,\n \"acc_norm\": 0.8607918263090677,\n\
\ \"acc_norm_stderr\": 0.012378786101885145\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577605,\n\
\ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577605\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5787709497206703,\n\
\ \"acc_stderr\": 0.016513676031179595,\n \"acc_norm\": 0.5787709497206703,\n\
\ \"acc_norm_stderr\": 0.016513676031179595\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\
\ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.752411575562701,\n\
\ \"acc_stderr\": 0.024513879973621967,\n \"acc_norm\": 0.752411575562701,\n\
\ \"acc_norm_stderr\": 0.024513879973621967\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7993827160493827,\n \"acc_stderr\": 0.02228231394977488,\n\
\ \"acc_norm\": 0.7993827160493827,\n \"acc_norm_stderr\": 0.02228231394977488\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5709219858156028,\n \"acc_stderr\": 0.02952591430255856,\n \
\ \"acc_norm\": 0.5709219858156028,\n \"acc_norm_stderr\": 0.02952591430255856\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5645371577574967,\n\
\ \"acc_stderr\": 0.012663412101248349,\n \"acc_norm\": 0.5645371577574967,\n\
\ \"acc_norm_stderr\": 0.012663412101248349\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \
\ \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.02815637344037142\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7336601307189542,\n \"acc_stderr\": 0.017883188134667206,\n \
\ \"acc_norm\": 0.7336601307189542,\n \"acc_norm_stderr\": 0.017883188134667206\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8656716417910447,\n\
\ \"acc_stderr\": 0.024112678240900794,\n \"acc_norm\": 0.8656716417910447,\n\
\ \"acc_norm_stderr\": 0.024112678240900794\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\
\ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\
\ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.45532435740514077,\n\
\ \"mc1_stderr\": 0.01743349010253877,\n \"mc2\": 0.6421820394674438,\n\
\ \"mc2_stderr\": 0.015085186356964665\n }\n}\n```"
repo_url: https://huggingface.co/yeontaek/llama-2-70B-ensemble-v3
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|arc:challenge|25_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hellaswag|10_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T14:01:58.848407.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T14:01:58.848407.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T14:01:58.848407.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T14:01:58.848407.parquet'
- config_name: results
data_files:
- split: 2023_09_01T14_01_58.848407
path:
- results_2023-09-01T14:01:58.848407.parquet
- split: latest
path:
- results_2023-09-01T14:01:58.848407.parquet
---
# Dataset Card for Evaluation run of yeontaek/llama-2-70B-ensemble-v3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeontaek/llama-2-70B-ensemble-v3
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [yeontaek/llama-2-70B-ensemble-v3](https://huggingface.co/yeontaek/llama-2-70B-ensemble-v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_yeontaek__llama-2-70B-ensemble-v3",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-01T14:01:58.848407](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-70B-ensemble-v3/blob/main/results_2023-09-01T14%3A01%3A58.848407.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6813782482106774,
"acc_stderr": 0.03171011741691581,
"acc_norm": 0.6847848607826429,
"acc_norm_stderr": 0.031684498624315015,
"mc1": 0.45532435740514077,
"mc1_stderr": 0.01743349010253877,
"mc2": 0.6421820394674438,
"mc2_stderr": 0.015085186356964665
},
"harness|arc:challenge|25": {
"acc": 0.6621160409556314,
"acc_stderr": 0.013822047922283504,
"acc_norm": 0.6851535836177475,
"acc_norm_stderr": 0.013572657703084948
},
"harness|hellaswag|10": {
"acc": 0.6936865166301533,
"acc_stderr": 0.004600194559865542,
"acc_norm": 0.8716391157140012,
"acc_norm_stderr": 0.003338076015617253
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5925925925925926,
"acc_stderr": 0.042446332383532286,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.042446332383532286
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7828947368421053,
"acc_stderr": 0.03355045304882924,
"acc_norm": 0.7828947368421053,
"acc_norm_stderr": 0.03355045304882924
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.69,
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"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7622641509433963,
"acc_stderr": 0.02619980880756192,
"acc_norm": 0.7622641509433963,
"acc_norm_stderr": 0.02619980880756192
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.031164899666948617
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.55,
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"acc_norm": 0.55,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.653179190751445,
"acc_stderr": 0.036291466701596636,
"acc_norm": 0.653179190751445,
"acc_norm_stderr": 0.036291466701596636
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
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"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-conceptual_physics|5": {
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"acc_stderr": 0.0314895582974553,
"acc_norm": 0.6340425531914894,
"acc_norm_stderr": 0.0314895582974553
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.42105263157894735,
"acc_stderr": 0.04644602091222318,
"acc_norm": 0.42105263157894735,
"acc_norm_stderr": 0.04644602091222318
},
"harness|hendrycksTest-electrical_engineering|5": {
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"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555498
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.48412698412698413,
"acc_norm_stderr": 0.025738330639412152
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677173
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.8225806451612904,
"acc_norm_stderr": 0.021732540689329286
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm_stderr": 0.03512819077876106
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.75,
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},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.8484848484848485,
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_norm": 0.6923076923076923,
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},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm_stderr": 0.02866120111652459
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.773109243697479,
"acc_stderr": 0.027205371538279476,
"acc_norm": 0.773109243697479,
"acc_norm_stderr": 0.027205371538279476
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
"acc_stderr": 0.0395802723112157,
"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.0395802723112157
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8807339449541285,
"acc_stderr": 0.013895729292588949,
"acc_norm": 0.8807339449541285,
"acc_norm_stderr": 0.013895729292588949
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5370370370370371,
"acc_stderr": 0.03400603625538272,
"acc_norm": 0.5370370370370371,
"acc_norm_stderr": 0.03400603625538272
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9068627450980392,
"acc_stderr": 0.020397853969426998,
"acc_norm": 0.9068627450980392,
"acc_norm_stderr": 0.020397853969426998
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.890295358649789,
"acc_stderr": 0.02034340073486884,
"acc_norm": 0.890295358649789,
"acc_norm_stderr": 0.02034340073486884
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7802690582959642,
"acc_stderr": 0.027790177064383602,
"acc_norm": 0.7802690582959642,
"acc_norm_stderr": 0.027790177064383602
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.816793893129771,
"acc_stderr": 0.03392770926494733,
"acc_norm": 0.816793893129771,
"acc_norm_stderr": 0.03392770926494733
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8347107438016529,
"acc_stderr": 0.03390780612972776,
"acc_norm": 0.8347107438016529,
"acc_norm_stderr": 0.03390780612972776
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7592592592592593,
"acc_stderr": 0.04133119440243839,
"acc_norm": 0.7592592592592593,
"acc_norm_stderr": 0.04133119440243839
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8343558282208589,
"acc_stderr": 0.029208296231259104,
"acc_norm": 0.8343558282208589,
"acc_norm_stderr": 0.029208296231259104
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5625,
"acc_stderr": 0.04708567521880525,
"acc_norm": 0.5625,
"acc_norm_stderr": 0.04708567521880525
},
"harness|hendrycksTest-management|5": {
"acc": 0.8155339805825242,
"acc_stderr": 0.03840423627288276,
"acc_norm": 0.8155339805825242,
"acc_norm_stderr": 0.03840423627288276
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8931623931623932,
"acc_stderr": 0.020237149008990915,
"acc_norm": 0.8931623931623932,
"acc_norm_stderr": 0.020237149008990915
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8607918263090677,
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"acc_norm": 0.8607918263090677,
"acc_norm_stderr": 0.012378786101885145
},
"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm": 0.7196531791907514,
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.5787709497206703,
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"acc_norm": 0.5787709497206703,
"acc_norm_stderr": 0.016513676031179595
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.738562091503268,
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"acc_norm": 0.738562091503268,
"acc_norm_stderr": 0.025160998214292456
},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.752411575562701,
"acc_norm_stderr": 0.024513879973621967
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"harness|hendrycksTest-prehistory|5": {
"acc": 0.7993827160493827,
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"acc_norm": 0.7993827160493827,
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"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5709219858156028,
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"acc_norm_stderr": 0.02952591430255856
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5645371577574967,
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"acc_norm": 0.5645371577574967,
"acc_norm_stderr": 0.012663412101248349
},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm": 0.6875,
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},
"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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"acc_norm_stderr": 0.04350271442923243
},
"harness|hendrycksTest-security_studies|5": {
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"acc_norm": 0.7306122448979592,
"acc_norm_stderr": 0.02840125202902294
},
"harness|hendrycksTest-sociology|5": {
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"acc_norm_stderr": 0.024112678240900794
},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.83,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5301204819277109,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.5301204819277109,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.45532435740514077,
"mc1_stderr": 0.01743349010253877,
"mc2": 0.6421820394674438,
"mc2_stderr": 0.015085186356964665
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_Sao10K__Euryale-1.3-L2-70B | open-llm-leaderboard | 2023-10-26T00:12:02Z | 279 | 0 | [
"region:us"
]
| null | 2023-10-12T17:36:47Z | ---
pretty_name: Evaluation run of Sao10K/Euryale-1.3-L2-70B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Sao10K/Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Sao10K__Euryale-1.3-L2-70B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-26T00:11:50.324232](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Euryale-1.3-L2-70B/blob/main/results_2023-10-26T00-11-50.324232.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.5388003355704698,\n\
\ \"em_stderr\": 0.005105027329360947,\n \"f1\": 0.6009920302013437,\n\
\ \"f1_stderr\": 0.004740248039821831,\n \"acc\": 0.5849328585370874,\n\
\ \"acc_stderr\": 0.011836910620214903\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.5388003355704698,\n \"em_stderr\": 0.005105027329360947,\n\
\ \"f1\": 0.6009920302013437,\n \"f1_stderr\": 0.004740248039821831\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3419257012888552,\n \
\ \"acc_stderr\": 0.013066089625182799\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8279400157853196,\n \"acc_stderr\": 0.010607731615247007\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Sao10K/Euryale-1.3-L2-70B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|arc:challenge|25_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_26T00_11_50.324232
path:
- '**/details_harness|drop|3_2023-10-26T00-11-50.324232.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-26T00-11-50.324232.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_26T00_11_50.324232
path:
- '**/details_harness|gsm8k|5_2023-10-26T00-11-50.324232.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-26T00-11-50.324232.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hellaswag|10_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_5
data_files:
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path:
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path:
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- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T17-36-24.431746.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T17-36-24.431746.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T17-36-24.431746.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T17-36-24.431746.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T17-36-24.431746.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T17-36-24.431746.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-12T17-36-24.431746.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-12T17-36-24.431746.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-12T17-36-24.431746.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-12T17-36-24.431746.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_26T00_11_50.324232
path:
- '**/details_harness|winogrande|5_2023-10-26T00-11-50.324232.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-26T00-11-50.324232.parquet'
- config_name: results
data_files:
- split: 2023_10_12T17_36_24.431746
path:
- results_2023-10-12T17-36-24.431746.parquet
- split: 2023_10_26T00_11_50.324232
path:
- results_2023-10-26T00-11-50.324232.parquet
- split: latest
path:
- results_2023-10-26T00-11-50.324232.parquet
---
# Dataset Card for Evaluation run of Sao10K/Euryale-1.3-L2-70B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Sao10K/Euryale-1.3-L2-70B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Sao10K/Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Sao10K__Euryale-1.3-L2-70B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T00:11:50.324232](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Euryale-1.3-L2-70B/blob/main/results_2023-10-26T00-11-50.324232.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.5388003355704698,
"em_stderr": 0.005105027329360947,
"f1": 0.6009920302013437,
"f1_stderr": 0.004740248039821831,
"acc": 0.5849328585370874,
"acc_stderr": 0.011836910620214903
},
"harness|drop|3": {
"em": 0.5388003355704698,
"em_stderr": 0.005105027329360947,
"f1": 0.6009920302013437,
"f1_stderr": 0.004740248039821831
},
"harness|gsm8k|5": {
"acc": 0.3419257012888552,
"acc_stderr": 0.013066089625182799
},
"harness|winogrande|5": {
"acc": 0.8279400157853196,
"acc_stderr": 0.010607731615247007
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
diplomacy_detection | null | 2023-01-25T14:29:25Z | 278 | 0 | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
pretty_name: HateOffensive
dataset_info:
features:
- name: messages
sequence: string
- name: sender_labels
sequence:
class_label:
names:
'0': 'false'
'1': 'true'
- name: receiver_labels
sequence:
class_label:
names:
'0': 'false'
'1': 'true'
'2': noannotation
- name: speakers
sequence:
class_label:
names:
'0': italy
'1': turkey
'2': russia
'3': england
'4': austria
'5': germany
'6': france
- name: receivers
sequence:
class_label:
names:
'0': italy
'1': turkey
'2': russia
'3': england
'4': austria
'5': germany
'6': france
- name: absolute_message_index
sequence: int64
- name: relative_message_index
sequence: int64
- name: seasons
sequence:
class_label:
names:
'0': spring
'1': fall
'2': winter
'3': Spring
'4': Fall
'5': Winter
- name: years
sequence:
class_label:
names:
'0': '1901'
'1': '1902'
'2': '1903'
'3': '1904'
'4': '1905'
'5': '1906'
'6': '1907'
'7': '1908'
'8': '1909'
'9': '1910'
'10': '1911'
'11': '1912'
'12': '1913'
'13': '1914'
'14': '1915'
'15': '1916'
'16': '1917'
'17': '1918'
- name: game_score
sequence:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
'10': '10'
'11': '11'
'12': '12'
'13': '13'
'14': '14'
'15': '15'
'16': '16'
'17': '17'
'18': '18'
- name: game_score_delta
sequence:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
'10': '10'
'11': '11'
'12': '12'
'13': '13'
'14': '14'
'15': '15'
'16': '16'
'17': '17'
'18': '18'
'19': '-1'
'20': '-2'
'21': '-3'
'22': '-4'
'23': '-5'
'24': '-6'
'25': '-7'
'26': '-8'
'27': '-9'
'28': '-10'
'29': '-11'
'30': '-12'
'31': '-13'
'32': '-14'
'33': '-15'
'34': '-16'
'35': '-17'
'36': '-18'
- name: players
sequence:
class_label:
names:
'0': italy
'1': turkey
'2': russia
'3': england
'4': austria
'5': germany
'6': france
- name: game_id
dtype: int64
splits:
- name: validation
num_bytes: 254344
num_examples: 21
- name: train
num_bytes: 2539778
num_examples: 189
- name: test
num_bytes: 506191
num_examples: 42
download_size: 3208706
dataset_size: 3300313
---
# Dataset Card for HateOffensive
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage** : https://sites.google.com/view/qanta/projects/diplomacy
- **Repository** : https://github.com/DenisPeskov/2020_acl_diplomacy
- **Paper** : http://users.umiacs.umd.edu/~jbg/docs/2020_acl_diplomacy.pdf
- **Leaderboard** :
- **Point of Contact** :
### Dataset Summary
This dataset contains pairwise conversations annotated by the sender and the receiver for deception (and conversely truthfulness). The 17,289 messages are gathered from 12 games.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
```
{
"messages":
["Greetings Sultan!\n\nAs your neighbor I would like to propose an alliance! What are your views on the board so far?", "I think an alliance would be great! Perhaps a dmz in the Black Sea would be a good idea to solidify this alliance?\n\nAs for my views on the board, my first moves will be Western into the Balkans and Mediterranean Sea.", "Sounds good lets call a dmz in the black sea", "What's our move this year?", "I've been away from the game for a while", "Not sure yet, what are your thoughts?", "Well I'm pretty worried about Germany attacking me (and Austria to a lesser extent) so im headed west. It looks like Italy's landing a army in Syr this fall unless you can stop it", "That sounds good to me. I'll move to defend against Italy while you move west. If it's not too much too ask, I'd like to request that you withdraw your fleet from bla.", "Oh sorry missed the msg to move out of bl sea ill do that this turn. I did bring my army down into Armenia, To help you expel the Italian. It looks like Austria and Italy are working together. If we have a chance in the region you should probably use smy to protect con. We can't afford to lose con.", "I'll defend con from both ank and smy.", "Hey sorry for stabbing you earlier, it was an especially hard choice since Turkey is usually my country of choice. It's cool we got to do this study huh?"],
"sender_labels": [false, true, false, true, true, true, true, true, true, true, true],
"receiver_labels": [true, true, true, true, true, true, true, true, true, true, "NOANNOTATION"],
"speakers": ["russia", "turkey", "russia", "russia", "russia", "turkey", "russia", "turkey", "russia", "turkey", "russia"],
"receivers": ["turkey", "russia", "turkey", "turkey", "turkey", "russia", "turkey", "russia", "turkey", "russia", "turkey"],
"absolute_message_index": [78, 107, 145, 370, 371, 374, 415, 420, 495, 497, 717],
"relative_message_index": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
"seasons": ["Spring", "Spring", "Spring", "Spring", "Spring", "Spring", "Fall", "Fall", "Spring", "Spring", "Fall"],
"years": ["1901", "1901", "1901", "1902", "1902", "1902", "1902", "1902", "1903", "1903", "1905"],
"game_score": ["4", "3", "4", "5", "5", "4", "5", "4", "5", "3", "7"],
"game_score_delta": ["1", "-1", "1", "1", "1", "-1", "1", "-1", "2", "-2", "7"],
"players": ["russia", "turkey"],
"game_id": 10
}
```
### Data Fields
- speakers: the sender of the message (string format. Seven possible values: russia, turkey, england, austria, germany, france, italy)
- receivers: the receiver of the message (string format. Seven possible values: russia, turkey, england, austria, germany, france, italy)
- messages: the raw message string (string format. ranges in length from one word to paragraphs in length)
- sender_labels: indicates if the sender of the message selected that the message is truthful, true, or deceptive, false. This is used for our ACTUAL_LIE calculation (true/false which can be bool or string format)
- receiver_labels: indicates if the receiver of the message selected that the message is perceived as truthful, true, or deceptive, false. In <10% of the cases, no annotation was received. This is used for our SUSPECTED_LIE calculation (string format. true/false/"NOANNOTATION" )
- game_score: the current game score---supply centers---of the sender (string format that ranges can range from 0 to 18)
- game_score_delta: the current game score---supply centers---of the sender minus the game score of the recipient (string format that ranges from -18 to 18)
- absolute_message_index: the index the message is in the entire game, across all dialogs (int format)
- relative_message_index: the index of the message in the current dialog (int format)
- seasons: the season in Diplomacy, associated with the year (string format. Spring, Fall, Winter)
- years: the year in Diplomacy, associated with the season (string format. 1901 through 1918)
- game_id: which of the 12 games the dialog comes from (int format ranging from 1 to 12)
### Data Splits
Train, Test and Validation splits
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Unknown
### Citation Information
@inproceedings{Peskov:Cheng:Elgohary:Barrow:Danescu-Niculescu-Mizil:Boyd-Graber-2020,
Title = {It Takes Two to Lie: One to Lie and One to Listen},
Author = {Denis Peskov and Benny Cheng and Ahmed Elgohary and Joe Barrow and Cristian Danescu-Niculescu-Mizil and Jordan Boyd-Graber},
Booktitle = {Association for Computational Linguistics},
Year = {2020},
Location = {Seattle},
}
### Contributions
Thanks to [@MisbahKhan789](https://github.com/MisbahKhan789) for adding this dataset. | [
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|
hippocorpus | null | 2022-11-03T16:15:25Z | 278 | 3 | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:other",
"narrative-flow",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: hippocorpus
tags:
- narrative-flow
dataset_info:
features:
- name: AssignmentId
dtype: string
- name: WorkTimeInSeconds
dtype: string
- name: WorkerId
dtype: string
- name: annotatorAge
dtype: float32
- name: annotatorGender
dtype: string
- name: annotatorRace
dtype: string
- name: distracted
dtype: float32
- name: draining
dtype: float32
- name: frequency
dtype: float32
- name: importance
dtype: float32
- name: logTimeSinceEvent
dtype: string
- name: mainEvent
dtype: string
- name: memType
dtype: string
- name: mostSurprising
dtype: string
- name: openness
dtype: string
- name: recAgnPairId
dtype: string
- name: recImgPairId
dtype: string
- name: similarity
dtype: string
- name: similarityReason
dtype: string
- name: story
dtype: string
- name: stressful
dtype: string
- name: summary
dtype: string
- name: timeSinceEvent
dtype: string
splits:
- name: train
num_bytes: 7229795
num_examples: 6854
download_size: 0
dataset_size: 7229795
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Hippocorpus](https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318)
- **Repository:** [Hippocorpus](https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318)
- **Paper:** [Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models](http://erichorvitz.com/cognitive_studies_narrative.pdf)
- **Point of Contact:** [Eric Horvitz](mailto:[email protected])
### Dataset Summary
To examine the cognitive processes of remembering and imagining and their traces in language, we introduce Hippocorpus, a dataset of 6,854 English diary-like short stories about recalled and imagined events. Using a crowdsourcing framework, we first collect recalled stories and summaries from workers, then provide these summaries to other workers who write imagined stories. Finally, months later, we collect a retold version of the recalled stories from a subset of recalled authors. Our dataset comes paired with author demographics (age, gender, race), their openness to experience, as well as some variables regarding the author's relationship to the event (e.g., how personal the event is, how often they tell its story, etc.).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset can be found in English
## Dataset Structure
[More Information Needed]
### Data Instances
[More Information Needed]
### Data Fields
This CSV file contains all the stories in Hippcorpus v2 (6854 stories)
These are the columns in the file:
- `AssignmentId`: Unique ID of this story
- `WorkTimeInSeconds`: Time in seconds that it took the worker to do the entire HIT (reading instructions, storywriting, questions)
- `WorkerId`: Unique ID of the worker (random string, not MTurk worker ID)
- `annotatorAge`: Lower limit of the age bucket of the worker. Buckets are: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55+
- `annotatorGender`: Gender of the worker
- `annotatorRace`: Race/ethnicity of the worker
- `distracted`: How distracted were you while writing your story? (5-point Likert)
- `draining`: How taxing/draining was writing for you emotionally? (5-point Likert)
- `frequency`: How often do you think about or talk about this event? (5-point Likert)
- `importance`: How impactful, important, or personal is this story/this event to you? (5-point Likert)
- `logTimeSinceEvent`: Log of time (days) since the recalled event happened
- `mainEvent`: Short phrase describing the main event described
- `memType`: Type of story (recalled, imagined, retold)
- `mostSurprising`: Short phrase describing what the most surpring aspect of the story was
- `openness`: Continuous variable representing the openness to experience of the worker
- `recAgnPairId`: ID of the recalled story that corresponds to this retold story (null for imagined stories). Group on this variable to get the recalled-retold pairs.
- `recImgPairId`: ID of the recalled story that corresponds to this imagined story (null for retold stories). Group on this variable to get the recalled-imagined pairs.
- `similarity`: How similar to your life does this event/story feel to you? (5-point Likert)
- `similarityReason`: Free text annotation of similarity
- `story`: Story about the imagined or recalled event (15-25 sentences)
- `stressful`: How stressful was this writing task? (5-point Likert)
- `summary`: Summary of the events in the story (1-3 sentences)
- `timeSinceEvent`: Time (num. days) since the recalled event happened
### Data Splits
[More Information Needed]
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
[More Information Needed]
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
[More Information Needed]
### Dataset Curators
The dataset was initially created by Maarten Sap, Eric Horvitz, Yejin Choi, Noah A. Smith, James W. Pennebaker, during work done at Microsoft Research.
### Licensing Information
Hippocorpus is distributed under the [Open Use of Data Agreement v1.0](https://msropendata-web-api.azurewebsites.net/licenses/f1f352a6-243f-4905-8e00-389edbca9e83/view).
### Citation Information
```
@inproceedings{sap-etal-2020-recollection,
title = "Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models",
author = "Sap, Maarten and
Horvitz, Eric and
Choi, Yejin and
Smith, Noah A. and
Pennebaker, James",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.178",
doi = "10.18653/v1/2020.acl-main.178",
pages = "1970--1978",
abstract = "We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release Hippocorpus, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events. Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language.",
}
```
### Contributions
Thanks to [@manandey](https://github.com/manandey) for adding this dataset. | [
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|
msr_zhen_translation_parity | null | 2022-11-03T16:08:10Z | 278 | 0 | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|other-newstest2017",
"language:en",
"license:ms-pl",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
- machine-generated
language:
- en
license:
- ms-pl
multilinguality:
- monolingual
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-newstest2017
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: MsrZhenTranslationParity
dataset_info:
features:
- name: Reference-HT
dtype: string
- name: Reference-PE
dtype: string
- name: Combo-4
dtype: string
- name: Combo-5
dtype: string
- name: Combo-6
dtype: string
- name: Online-A-1710
dtype: string
splits:
- name: train
num_bytes: 1797033
num_examples: 2001
download_size: 0
dataset_size: 1797033
---
# Dataset Card for msr_zhen_translation_parity
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
[Translator Human Parity Data](https://msropendata.com/datasets/93f9aa87-9491-45ac-81c1-6498b6be0d0b)
- **Repository:**
- **Paper:**
[Achieving Human Parity on Automatic Chinese to English News Translation](https://www.microsoft.com/en-us/research/publication/achieving-human-parity-on-automatic-chinese-to-english-news-translation/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
> Human evaluation results and translation output for the Translator Human Parity Data release,
> as described in https://blogs.microsoft.com/ai/machine-translation-news-test-set-human-parity/
> The Translator Human Parity Data release contains all human evaluation results and translations
> related to our paper "Achieving Human Parity on Automatic Chinese to English News Translation",
> published on March 14, 2018. We have released this data to
> 1) allow external validation of our claim of having achieved human parity
> 2) to foster future research by releasing two additional human references
> for the Reference-WMT test set.
>
The dataset includes:
1) two new references for Chinese-English language pair of WMT17,
one based on human translation from scratch (Reference-HT),
the other based on human post-editing (Reference-PE);
2) human parity translations generated by our research systems Combo-4, Combo-5, and Combo-6,
as well as translation output from online machine translation service Online-A-1710,
collected on October 16, 2017;
The data package provided with the study also includes (but not parsed and provided as
workable features of this dataset) all data points collected in human evaluation campaigns.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
This dataset contains 6 extra English translations to Chinese-English language pair of WMT17.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
As mentioned in the summary, this dataset provides 6 extra English translations of
Chinese-English language pair of WMT17.
Data fields are named exactly like the associated paper for easier cross-referenceing.
- `Reference-HT`: human translation from scrach.
- `Reference-PE`: human post-editing.
- `Combo-4`, `Combo-5`, `Combo-6`: three translations by research systems.
- `Online-A-1710`: a translation from an anonymous online machine translation service.
All data fields of a record are translations for the same Chinese source sentence.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
Citation information is available at this link [Achieving Human Parity on Automatic Chinese to English News Translation](https://www.microsoft.com/en-us/research/publication/achieving-human-parity-on-automatic-chinese-to-english-news-translation/)
### Contributions
Thanks to [@leoxzhao](https://github.com/leoxzhao) for adding this dataset. | [
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opus_fiskmo | null | 2022-11-03T16:08:01Z | 278 | 0 | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:fi",
"language:sv",
"license:unknown",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- fi
- sv
license:
- unknown
multilinguality:
- translation
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusFiskmo
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- fi
- sv
config_name: fi-sv
splits:
- name: train
num_bytes: 326528834
num_examples: 2100001
download_size: 144858927
dataset_size: 326528834
---
# Dataset Card for [opus_fiskmo]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[fiskmo](http://opus.nlpl.eu/fiskmo.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
fiskmo, a massive parallel corpus for Finnish and Swedish.
### Supported Tasks and Leaderboards
The underlying task is machine translation for language pair Finnish and Swedish.
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. | [
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|
opus_memat | null | 2022-11-03T16:08:11Z | 278 | 1 | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:xh",
"license:unknown",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- xh
license:
- unknown
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusMemat
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- xh
- en
config_name: xh-en
splits:
- name: train
num_bytes: 25400570
num_examples: 154764
download_size: 8382865
dataset_size: 25400570
---
# Dataset Card for [opus_memat]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[memat](http://opus.nlpl.eu/memat.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Xhosa-English parallel corpora, funded by EPSRC, the Medical Machine Translation project worked on machine translation between ixiXhosa and English, with a focus on the medical domain.
### Supported Tasks and Leaderboards
The underlying task is machine translation from Xhosa to English
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. | [
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|
opus_montenegrinsubs | null | 2022-11-03T16:08:11Z | 278 | 0 | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:cnr",
"language:en",
"license:unknown",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- cnr
- en
license:
- unknown
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusMontenegrinsubs
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- en
- me
config_name: en-me
splits:
- name: train
num_bytes: 4896403
num_examples: 65043
download_size: 1990570
dataset_size: 4896403
---
# Dataset Card for [opus_montenegrinsubs]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[opus MontenegrinSubs ](http://opus.nlpl.eu/MontenegrinSubs.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Opus MontenegrinSubs dataset for machine translation task, for language pair en-me: english and montenegrin
### Supported Tasks and Leaderboards
The underlying task is machine translation from en to me
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. | [
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]
|
psc | null | 2023-01-25T14:42:57Z | 278 | 1 | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:cc-by-sa-3.0",
"region:us"
]
| [
"summarization"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
pretty_name: psc
dataset_info:
features:
- name: extract_text
dtype: string
- name: summary_text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 5026582
num_examples: 4302
- name: test
num_bytes: 1292103
num_examples: 1078
download_size: 2357808
dataset_size: 6318685
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
http://zil.ipipan.waw.pl/PolishSummariesCorpus
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Polish Summaries Corpus contains news articles and their summaries. We used summaries of the same article as positive pairs and sampled the most similar summaries of different articles as negatives.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Polish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- extract_text: text to summarise
- summary_text: summary of extracted text
- label: 1 indicates summary is similar, 0 means that it is not similar
### Data Splits
Data is splitted in train and test dataset. Test dataset doesn't have label column, so -1 is set instead.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CC BY-SA 3.0
### Citation Information
@inproceedings{ogro:kop:14:lrec,
title={The {P}olish {S}ummaries {C}orpus},
author={Ogrodniczuk, Maciej and Kope{\'c}, Mateusz},
booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
year = "2014",
}
### Contributions
Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset. | [
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|
wikitext_tl39 | null | 2022-11-03T16:15:46Z | 278 | 0 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:fil",
"language:tl",
"license:gpl-3.0",
"arxiv:1907.00409",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- fil
- tl
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: wikitext-tl-39
pretty_name: WikiText-TL-39
dataset_info:
features:
- name: text
dtype: string
config_name: wikitext-tl-39
splits:
- name: test
num_bytes: 46182996
num_examples: 376737
- name: train
num_bytes: 217182748
num_examples: 1766072
- name: validation
num_bytes: 46256674
num_examples: 381763
download_size: 116335234
dataset_size: 309622418
---
# Dataset Card for WikiText-TL-39
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Filipino Text Benchmarks](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Repository:**
- **Paper:** [Evaluating language model finetuning techniques for low-resource languages](https://arxiv.org/abs/1907.00409)
- **Leaderboard:**
- **Point of Contact:** Jan Christian Blaise Cruz ([email protected])
### Dataset Summary
Large scale, unlabeled text dataset with 39 Million tokens in the training set. Inspired by the original WikiText Long Term Dependency dataset (Merity et al., 2016). TL means "Tagalog." Published in Cruz & Cheng (2019).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Filipino/Tagalog
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- `text` (`str`)
The dataset is in plaintext and only has one field ("text") as it is compiled for language modeling.
### Data Splits
Split | Documents | Tokens
------|-----------|-------
Train | 120,975 | 39M
Valid | 25,919 | 8M
Test | 25,921 | 8M
Please see the paper for more details on the dataset splits
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
Tagalog Wikipedia
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@jcblaisecruz02](https://github.com/jcblaisecruz02) for adding this dataset. | [
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|
Aisha/BAAD16 | Aisha | 2022-10-22T05:31:54Z | 278 | 0 | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:bn",
"license:cc-by-4.0",
"arxiv:2001.05316",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
- crowdsourced
- expert-generated
language_creators:
- found
- crowdsourced
language:
- bn
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: 'BAAD16: Bangla Authorship Attribution Dataset (16 Authors)'
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
## Description
**BAAD16** is an **Authorship Attribution dataset for Bengali Literature**. It was collected and analyzed by the authors of [this paper](https://arxiv.org/abs/2001.05316). It was created by scraping text from an online Bangla e-library using custom web crawler and contains literary works of various famous Bangla writers. It contains novels, stories, series, and other works of 16 authors. Each sample document is created with 750 words. The dataset is imbalanced and resembles real-world scenarios more closely, where not all the authors will have a large number of sample texts. The following table gives more details about the dataset.
| Author Name | Number of Samples | Word Count | Unique Word
| --- | --- | --- | --- |
| zahir rayhan | 185 | 138k | 20k
|nazrul | 223 | 167k | 33k
|manik bandhopaddhay | 469 | 351k | 44k
|nihar ronjon gupta | 476 | 357k | 43k
|bongkim | 562 | 421k | 62k
|tarashonkor | 775 | 581k | 84k
|shottojit roy | 849 | 636k | 67k
|shordindu | 888 | 666k | 84k
|toslima nasrin | 931 | 698k | 76k
|shirshendu | 1048 | 786k | 69k
|zafar iqbal | 1100 | 825k | 53k
|robindronath | 1259 | 944k | 89k
|shorotchandra | 1312 | 984k | 78k
|shomresh | 1408 | 1056k|69k
|shunil gongopaddhay | 1963 | 1472k|109k
|humayun ahmed | 4518 | 3388k |161k
**Total**| 17,966|13,474,500 | 590,660
**Average**|1,122.875|842,156.25| 71,822.25
## Citation
If you use this dataset, please cite the paper [Authorship Attribution in Bangla literature using Character-level CNN](https://ieeexplore.ieee.org/abstract/document/9038560/). [Archive link](https://arxiv.org/abs/2001.05316).
```
@inproceedings{BAAD16Dataset,
title={Authorship Attribution in Bangla literature using Character-level CNN},
author={Khatun, Aisha and Rahman, Anisur and Islam, Md Saiful and others},
booktitle={2019 22nd International Conference on Computer and Information Technology (ICCIT)},
pages={1--5},
year={2019},
organization={IEEE}
doi={10.1109/ICCIT48885.2019.9038560}
}
```
This dataset is also available in Mendeley: [BAAD16 dataset](https://data.mendeley.com/datasets/6d9jrkgtvv/4). Always make sure to use the latest version of the dataset. Cite the dataset directly by:
```
@misc{BAAD6Dataset,
author = {Khatun, Aisha and Rahman, Anisur and Islam, Md. Saiful},
title = {BAAD16: Bangla Authorship Attribution Dataset},
year={2019},
doi = {10.17632/6d9jrkgtvv.4},
howpublished= {\url{https://data.mendeley.com/datasets/6d9jrkgtvv/4}}
}
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|
Aisha/BAAD6 | Aisha | 2022-10-22T05:30:28Z | 278 | 0 | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:bn",
"license:cc-by-4.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
- crowdsourced
- expert-generated
language_creators:
- found
- crowdsourced
language:
- bn
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: 'BAAD6: Bangla Authorship Attribution Dataset (6 Authors)'
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
## Description
**BAAD6** is an **Authorship Attribution dataset for Bengali Literature**. It was collected and analyzed by Hemayet et al [[1]](https://ieeexplore.ieee.org/document/8631977). The data was obtained from different online posts and blogs. This dataset is balanced among the 6 Authors with 350 sample texts per author. This is a relatively small dataset but is noisy given the sources it was collected from and its cleaning procedure. Nonetheless, it may help evaluate authorship attribution systems as it resembles texts often available on the Internet. Details about the dataset are given in the table below.
| Author | Samples | Word count | Unique word |
| ------ | ------ | ------ | ------ |
|fe|350|357k|53k|
| ij | 350 | 391k | 72k
| mk | 350 | 377k | 47k
| rn | 350 | 231k | 50k
| hm | 350 | 555k | 72k
| rg | 350 | 391k | 58k
**Total** | 2,100 | 2,304,338 | 230,075
**Average** | 350 | 384,056.33 | 59,006.67
## Citation
If you use this dataset, please cite the paper [A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature](https://ieeexplore.ieee.org/document/8631977).
```
@INPROCEEDINGS{BAAD6Dataset,
author={Ahmed Chowdhury, Hemayet and Haque Imon, Md. Azizul and Islam, Md. Saiful},
booktitle={2018 21st International Conference of Computer and Information Technology (ICCIT)},
title={A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature},
year={2018},
volume={},
number={},
pages={1-6},
doi={10.1109/ICCITECHN.2018.8631977}
}
```
This dataset is also available in Mendeley: [BAAD6 dataset](https://data.mendeley.com/datasets/w9wkd7g43f/5). Always make sure to use the latest version of the dataset. Cite the dataset directly by:
```
@misc{BAAD6Dataset,
author = {Ahmed Chowdhury, Hemayet and Haque Imon, Md. Azizul and Khatun, Aisha and Islam, Md. Saiful},
title = {BAAD6: Bangla Authorship Attribution Dataset},
year={2018},
doi = {10.17632/w9wkd7g43f.5},
howpublished= {\url{https://data.mendeley.com/datasets/w9wkd7g43f/5}}
}
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|
DDSC/europarl | DDSC | 2022-07-01T15:42:03Z | 278 | 2 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:da",
"license:cc-by-4.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- da
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: TwitterSent
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for DKHate
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Direct Download**: http://danlp-downloads.alexandra.dk/datasets/europarl.sentiment2.zip
### Dataset Summary
This dataset consists of Danish data from the European Parliament that has been annotated for sentiment analysis by the [Alexandra Institute](https://github.com/alexandrainst) - all credits go to them.
### Supported Tasks and Leaderboards
This dataset is suitable for sentiment analysis.
### Languages
This dataset is in Danish.
## Dataset Structure
### Data Instances
Every entry in the dataset has a document and an associated label.
### Data Fields
An entry in the dataset consists of the following fields:
- `text` (`str`): The text content.
- `label` (`str`): The label of the `text`. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively.
### Data Splits
A `train` and `test` split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 669 documents in the training split and 288 in the test split.
## Additional Information
### Dataset Curators
The collection and annotation of the dataset is solely due to the [Alexandra Institute](https://github.com/alexandrainst).
### Licensing Information
The dataset is released under the CC BY 4.0 license.
### Citation Information
```
@misc{europarl,
title={EuroParl},
author={Alexandra Institute},
year={2020},
note={\url{https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2}}
}
```
### Contributions
Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub. | [
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|
collectivat/tv3_parla | collectivat | 2022-12-12T09:01:48Z | 278 | 3 | [
"task_categories:automatic-speech-recognition",
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ca",
"license:cc-by-nc-4.0",
"region:us"
]
| [
"automatic-speech-recognition",
"text-generation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- ca
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- text-generation
task_ids:
- language-modeling
pretty_name: TV3Parla
---
# Dataset Card for TV3Parla
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://collectivat.cat/asr#tv3parla
- **Repository:**
- **Paper:** [Building an Open Source Automatic Speech Recognition System for Catalan](https://www.isca-speech.org/archive/iberspeech_2018/kulebi18_iberspeech.html)
- **Point of Contact:** [Col·lectivaT](mailto:[email protected])
### Dataset Summary
This corpus includes 240 hours of Catalan speech from broadcast material.
The details of segmentation, data processing and also model training are explained in Külebi, Öktem; 2018.
The content is owned by Corporació Catalana de Mitjans Audiovisuals, SA (CCMA);
we processed their material and hereby making it available under their terms of use.
This project was supported by the Softcatalà Association.
### Supported Tasks and Leaderboards
The dataset can be used for:
- Language Modeling.
- Automatic Speech Recognition (ASR) transcribes utterances into words.
### Languages
The dataset is in Catalan (`ca`).
## Dataset Structure
### Data Instances
```
{
'path': 'tv3_0.3/wav/train/5662515_1492531876710/5662515_1492531876710_120.180_139.020.wav',
'audio': {'path': 'tv3_0.3/wav/train/5662515_1492531876710/5662515_1492531876710_120.180_139.020.wav',
'array': array([-0.01168823, 0.01229858, 0.02819824, ..., 0.015625 ,
0.01525879, 0.0145874 ]),
'sampling_rate': 16000},
'text': 'algunes montoneres que que et feien anar ben col·locat i el vent també hi jugava una mica de paper bufava vent de cantó alguns cops o de cul i el pelotón el vent el porta molt malament hi havia molts nervis'
}
```
### Data Fields
- `path` (str): Path to the audio file.
- `audio` (dict): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling
rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and
resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might
take a significant amount of time. Thus, it is important to first query the sample index before the `"audio"` column,
*i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- `text` (str): Transcription of the audio file.
### Data Splits
The dataset is split into "train" and "test".
| | train | test |
|:-------------------|-------:|-----:|
| Number of examples | 159242 | 2220 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
@inproceedings{kulebi18_iberspeech,
author={Baybars Külebi and Alp Öktem},
title={{Building an Open Source Automatic Speech Recognition System for Catalan}},
year=2018,
booktitle={Proc. IberSPEECH 2018},
pages={25--29},
doi={10.21437/IberSPEECH.2018-6}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
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|
oscar-corpus/OSCAR-2109 | oscar-corpus | 2022-11-08T09:04:43Z | 278 | 32 | [
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"language:nds",
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"language:new",
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"language:wa",
"language:war",
"language:wuu",
"language:xal",
"language:xmf",
"language:yi",
"language:yo",
"language:zh",
"license:cc0-1.0",
"arxiv:2010.14571",
"arxiv:2103.12028",
"region:us"
]
| [
"sequence-modeling"
]
| 2022-03-02T23:29:22Z | ---
pretty_name: OSCAR
annotations_creators:
- no-annotation
language_creators:
- found
language:
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- als
- gsw
- am
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- ar
- arz
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- ur
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- vec
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- xmf
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license:
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multilinguality:
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- 1M<n<10M
unshuffled_original_su:
- n<1K
unshuffled_original_sv:
- 10M<n<100M
unshuffled_original_sw:
- 10K<n<100K
unshuffled_original_ta:
- 1M<n<10M
unshuffled_original_te:
- 100K<n<1M
unshuffled_original_tg:
- 10K<n<100K
unshuffled_original_th:
- 1M<n<10M
unshuffled_original_tk:
- 1K<n<10K
unshuffled_original_tl:
- 100K<n<1M
unshuffled_original_tr:
- 10M<n<100M
unshuffled_original_tt:
- 100K<n<1M
unshuffled_original_tyv:
- n<1K
unshuffled_original_ug:
- 10K<n<100K
unshuffled_original_uk:
- 10M<n<100M
unshuffled_original_ur:
- 100K<n<1M
unshuffled_original_uz:
- 10K<n<100K
unshuffled_original_vec:
- n<1K
unshuffled_original_vi:
- 10M<n<100M
unshuffled_original_vo:
- 1K<n<10K
unshuffled_original_wa:
- 1K<n<10K
unshuffled_original_war:
- 1K<n<10K
unshuffled_original_wuu:
- n<1K
unshuffled_original_xal:
- n<1K
unshuffled_original_xmf:
- 1K<n<10K
unshuffled_original_yi:
- 10K<n<100K
unshuffled_original_yo:
- n<1K
unshuffled_original_yue:
- n<1K
unshuffled_original_zh:
- 10M<n<100M
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- language-modeling
paperswithcode_id: oscar
---
# Dataset Card for "oscar"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://oscar-corpus.com](https://oscar-corpus.com)
- **Repository:** [github.com/oscar-corpus/corpus](https://github.com/oscar-corpus/corpus)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
OSCAR or **O**pen **S**uper-large **C**rawled **A**ggregated co**R**pus is a huge multilingual corpus obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) corpus using the [ungoliant](https://github.com/oscar-corpus/ungoliant) architecture. Data is distributed by language in both original and deduplicated form.
### Supported Tasks and Leaderboards
OSCAR is mainly inteded to pretrain language models and word represantations.
### Languages
All the data is distributed by language, both the original and the deduplicated versions of the data are available. 168 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR.
### Issues
OSCAR 21.09 has known issues regarding specific languages.
Note that other issues may (and could) be present in other languages.
**If you encounter something that is unexpected, please file an issue here: https://github.com/oscar-corpus/corpus/issues.**
|Language code|Language|Issues|
|-------------|--------|------|
|`tg`|Tajik|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Atg+label%3Aver%3A21.09)|
|`tr`|Turkish|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Atr+label%3Aver%3A21.09)|
|`vls`|West Flemish|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aopen+label%3Alang%3Avls+label%3Aver%3A21.09)|
|`wuu`|Wu Chinese|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Awuu+label%3Aver%3A21.09)|
|`nap`|Neapolitan|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Anap+label%3Aver%3A21.09)|
|`so`|Somali|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Aso+label%3Aver%3A21.09)|
|`frr`|Northern Frisian|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Afrr+label%3Aver%3A21.09)|
|`cbk`|Chavacano|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Acbk+label%3Aver%3A21.09)|
|`sco`|Scots|[](https://github.com/oscar-corpus/corpus/issues?q=is%3Aissue+is%3Aopen+label%3Alang%3Asco+label%3Aver%3A21.09)|
## Dataset Structure
We show detailed information for all the configurations of the dataset.
### Data Instances
<details>
<summary>Click to expand the Data/size information for each language (deduplicated)</summary>
#### deduplicated_af
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3287,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:BUOBNDDY3VZKNNUOY33PAWBXEVNDCDJK',
'warc-date': '2021-03-09T04:21:33Z',
'warc-identified-content-language': 'afr,eng',
'warc-record-id': '<urn:uuid:dece1e30-a099-411a-87fd-483791342d48>',
'warc-refers-to': '<urn:uuid:5a35e8b2-0fcb-4600-9d15-f5c6469ddf01>',
'warc-target-uri': 'http://www.northwestnewspapers.co.za/gemsbok/2015-06-18-10-02-17/hoe-om-n-ad-te-plaas/1907-man-betrap-met-jagluiperd-en-leeu-bene',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': 'Stap 2: Tik jou ad in die teks boksie, jy sal sien dat die prys aan '
'die regterkant van die boksie verander volgens di...'}
```
#### deduplicated_als
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4607,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:URQ53Z4I4KGPHICZYLW2ZOX7OWWCGZUA',
'warc-date': '2021-03-03T16:09:20Z',
'warc-identified-content-language': 'deu,eng',
'warc-record-id': '<urn:uuid:134499db-d54a-4c29-9517-350cacc3d29d>',
'warc-refers-to': '<urn:uuid:073aeb77-b4ed-47eb-b955-27031963acf4>',
'warc-target-uri': 'https://als.m.wikipedia.org/wiki/Neukaledonien',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'D Wirtschaft bestoot vor allem us Handwärk, Bärgbau, Industrii und '
'Turismus. 40 Kilometer vo dr Hauptstadt Nouméa äwä...'}
```
#### deduplicated_am
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9679,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:YADJOQVUOQHUKJ7BXCKKU4LRFKE3JPOA',
'warc-date': '2021-03-09T04:16:32Z',
'warc-identified-content-language': 'amh,eng',
'warc-record-id': '<urn:uuid:fa02fe22-c72e-42e8-9cb3-89da85a80941>',
'warc-refers-to': '<urn:uuid:ff89f862-5e6a-41aa-bc40-ef1d2f91d258>',
'warc-target-uri': 'http://ethioforum.ethiopiaforums.com/viewtopic.php?f=6&t=3874&p=6511',
'warc-type': 'conversion'},
'nb_sentences': 10,
'offset': 0},
'text': '(ፍኖተ ነፃነት) በኢትዮጵያ የአዉሮፓ ሕብረት ልኡካን ቡድን መሪ አምባሳደር ቻንታል ሔበሬሽ፣ በአዉሮፓ '
'ሕብረት የአፍሪካ ቀንድ እና የሕንድ ዉቂያኖስ አካባቢ ዴስክ ኦፌሴር ቪክቶሪያ ጋርሲ...'}
```
#### deduplicated_an
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 134014,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:OG2T3MJFSLSH33PVI7D3WPXVE6ZFLZ4Z',
'warc-date': '2021-03-08T00:58:33Z',
'warc-identified-content-language': 'ara,fra',
'warc-record-id': '<urn:uuid:0ef1d002-86e7-49c1-ac8a-8ba933d190ee>',
'warc-refers-to': '<urn:uuid:5071f1f7-3350-406d-ad97-f292fe7a2ff0>',
'warc-target-uri': 'http://dorous.ek.la/1-5-a6032874?reply_comm=68653652',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووو...'}
```
#### deduplicated_ar
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 12677,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:NFDDUGANGSGSFXIQAXEGIVHGRLFCUW55',
'warc-date': '2021-03-04T02:22:39Z',
'warc-identified-content-language': 'ara,eng',
'warc-record-id': '<urn:uuid:3ea1e651-68f3-4dde-bfea-7a12e5331084>',
'warc-refers-to': '<urn:uuid:dcecf9ad-1797-44d0-b06a-010c424ba396>',
'warc-target-uri': 'https://elmgals.net/?p=62804',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'مطحنة الكرة في ماسبات - orioloingeu. مطاحن الفرينة في مطحنة الكرة '
'مراكز بيع الة طحن التوابل بيع ألات لرحي اسعار بيع ا...'}
```
#### deduplicated_arz
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9603,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:6O2LEGAWXAWYSRH2TQNYOWX47ZFWTKRC',
'warc-date': '2021-03-09T03:51:17Z',
'warc-identified-content-language': 'ara',
'warc-record-id': '<urn:uuid:0578411b-367f-4d52-b85c-56b4bb64c0be>',
'warc-refers-to': '<urn:uuid:8777119c-434c-49a1-80a8-f2b23fa0e21c>',
'warc-target-uri': 'https://www.hko-ommen.nl/Nov_01/605.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'مستعملة 4265 كسارات للبيع - كسارة الحجر. كسارات مستعمله للبيع فى '
'مصر. للبيع كسارات فى مصرمطلوب كسارات حجر مستعملة للب...'}
```
#### deduplicated_as
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9280,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DORQKORQ4TURDN35T75TW72IZ7IZIEFG',
'warc-date': '2021-03-03T15:06:57Z',
'warc-identified-content-language': 'asm,eng',
'warc-record-id': '<urn:uuid:fd6c3650-f91f-4f03-ae7a-bea654e043bb>',
'warc-refers-to': '<urn:uuid:48f057d6-f642-42d2-8de1-fec8e4fca4d4>',
'warc-target-uri': 'https://assam.nenow.in/%E0%A6%95%E0%A6%BE%E0%A6%87%E0%A6%B2%E0%A7%88%E0%A7%B0-%E0%A6%AA%E0%A7%B0%E0%A6%BE-%E0%A6%AF%E0%A7%8B%E0%A7%B0%E0%A6%B9%E0%A6%BE%E0%A6%9F%E0%A6%A4-%E0%A6%86%E0%A7%B0%E0%A6%AE%E0%A7%8D%E0%A6%AD/',
'warc-type': 'conversion'},
'nb_sentences': 8,
'offset': 0},
'text': 'যোৰহাট জিলাৰ এন আৰ চি উন্নিতকৰণৰ প্ৰথম পৰ্য্যায়ৰ বংশবৃক্ষ পৰীক্ষণৰ '
'কাম কাইলৈৰ পৰা পৰীক্ষামূলকভাৱে আৰু ১৯ ফেব্ৰুৱাৰিৰ ...'}
```
#### deduplicated_ast
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3752,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:BU44BHPYU2BOWH4TUAY7ZOEBFVQ6KD44',
'warc-date': '2021-03-01T15:56:44Z',
'warc-identified-content-language': 'spa',
'warc-record-id': '<urn:uuid:2b3ca12f-6614-4662-a4e9-16e1ce13a8b0>',
'warc-refers-to': '<urn:uuid:0e132db0-e0f4-44c5-ab63-48b7594a35a6>',
'warc-target-uri': 'https://elsummum.es/tag/dial-traxel-pais/',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Esta ye la galería d’imáxenes de los participantes nel concursu, el '
'xuráu y dellos miembros de la organización de la ...'}
```
#### deduplicated_av
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2012,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:EULKS66PQCWWVXHNRPSISI72G3GFJD7L',
'warc-date': '2021-03-01T10:13:53Z',
'warc-identified-content-language': 'rus,eng',
'warc-record-id': '<urn:uuid:c2986179-7947-4184-9df5-dca05c987055>',
'warc-refers-to': '<urn:uuid:8b3e82e1-0964-4677-8b39-9bd3c67be25b>',
'warc-target-uri': 'http://gazetalevashi.ru/articles/media/2019/10/25/diktant-tiobitiana/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Дагъистаналъул жамгIият рахьдал мацIал цIуниялде ва '
'церетIезариялде, тарих, гIадатал, маданият ва дагъистаналъул '
'халк...'}
```
#### deduplicated_az
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 59868,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:LDASIZ5NDJU6NRCJW7XCCI4QRLFIZZQX',
'warc-date': '2021-02-26T04:13:32Z',
'warc-identified-content-language': 'aze',
'warc-record-id': '<urn:uuid:a35cc521-926e-442d-b285-299ea4a3b72a>',
'warc-refers-to': '<urn:uuid:b60fd7ea-7056-4ebb-8ae5-eb02617ca8cd>',
'warc-target-uri': 'https://azrefs.org/iqtisadi-tesebbuslere-yardim-ictimai-birliyi-yerli-seviyyede-i.html',
'warc-type': 'conversion'},
'nb_sentences': 70,
'offset': 0},
'text': 'İQTİsadi TƏŞƏBBÜSLƏRƏ yardim iCTİMAİ BİRLİYİ Yerli səviyyədə içməli '
'su təchizatı sisteminin idarə olunması\n'
'Az1009, Az...'}
```
#### deduplicated_azb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 5245,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XWTKHZGKVJI6ZAIKSTOA4AOP5PCWI2SH',
'warc-date': '2021-03-05T13:35:27Z',
'warc-identified-content-language': 'fas,uzb,eng',
'warc-record-id': '<urn:uuid:41816fd7-985e-4e35-b79b-bf471e68dd80>',
'warc-refers-to': '<urn:uuid:5717a90d-021c-428b-a69d-45d6cb2fc692>',
'warc-target-uri': 'https://azb.wikipedia.org/wiki/%D8%A2%D9%85%D8%B3%D8%AA%D8%B1%D8%AF%D8%A7%D9%85_%D8%A8%DB%8C%D9%84%DB%8C%D9%85%E2%80%8C%DB%8C%D9%88%D8%B1%D8%AF%D9%88',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'یازی Creative Commons Attribution-ShareAlike '
'License;آلتیندا\u200cدیر آرتیق شرطلر آرتیریلا بیلر. آرتیق ایطلاعات '
'اوچون ایشل...'}
```
#### deduplicated_ba
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9444,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:NRTIKDSYAPTPQ64CKKLNR6TFVUYG7CLR',
'warc-date': '2021-03-09T04:46:56Z',
'warc-identified-content-language': 'uig,eng',
'warc-record-id': '<urn:uuid:b69f43f4-0e19-4cad-b083-fce91a40f64b>',
'warc-refers-to': '<urn:uuid:3176da53-14ff-4f65-91e4-4d209e9c7190>',
'warc-target-uri': 'https://uyghurix.net/archives/date/2016/05?uls=us',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': 'линакис системисиниң көрүнмә йүзи барғансери ишлитишкә қулайлиқ '
'болуп, кәң ишлитиливатқан болсиму, әмили хизмәттә йән...'}
```
#### deduplicated_bar
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 105623,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:L7EXHEWTVKPV7BWPZJFKHM2TZ3ZNKPWC',
'warc-date': '2021-03-07T18:33:16Z',
'warc-identified-content-language': 'fra',
'warc-record-id': '<urn:uuid:578af8ce-2149-42e3-978c-5191caaaca8c>',
'warc-refers-to': '<urn:uuid:a7afc792-983c-43b7-9b5b-75b2dc5fcd77>',
'warc-target-uri': 'https://fr.readkong.com/page/automne-hiver-printemps-2017-8342349',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': ' '
'vo\n'
' ...'}
```
#### deduplicated_be
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3159,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TEJML7M4S55254DZU43DXXORKPZMKGUL',
'warc-date': '2021-03-09T05:47:09Z',
'warc-identified-content-language': 'bel,eng',
'warc-record-id': '<urn:uuid:e22883c9-5622-4a0e-b259-b5265e6e345a>',
'warc-refers-to': '<urn:uuid:7ec2102d-2645-4fd9-89b8-557762996439>',
'warc-target-uri': 'https://be-tarask.wikipedia.org/wiki/%D0%9A%D0%B0%D1%82%D1%8D%D0%B3%D0%BE%D1%80%D1%8B%D1%8F:%D0%9F%D1%80%D1%8D%D1%81%D0%BD%D0%B0%D1%8F_%D0%B2%D0%B0%D0%B4%D0%B0',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Гэты тэкст даступны на ўмовах ліцэнзіі Creative Commons '
'Attribution/Share-Alike 3.0; у асобных выпадках могуць ужывац...'}
```
#### deduplicated_bg
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 23651,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:QDAV5ZVRR2IGND4ANWTVOBPNO2POZUEQ',
'warc-date': '2021-03-08T21:47:20Z',
'warc-identified-content-language': 'bul',
'warc-record-id': '<urn:uuid:0e422a1d-ac8c-4f21-bb71-e5c65282f30c>',
'warc-refers-to': '<urn:uuid:0109dba6-8f1a-4047-bdd5-cbcc38de63a8>',
'warc-target-uri': 'http://europe.bg/bg/bulgariya-poluchava-resor-inovacii-i-mladezh',
'warc-type': 'conversion'},
'nb_sentences': 37,
'offset': 0},
'text': 'От хилядите кубинци и другите граждани на страните от СИВ, '
'командировани на строежа на АЕЦ-а, в Белене е останал само...'}
```
#### deduplicated_bh
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9021,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:IN7PHDOP7MZD6RHN6KIJ7SXTY7VC76SK',
'warc-date': '2021-03-08T22:57:31Z',
'warc-identified-content-language': 'hin,eng',
'warc-record-id': '<urn:uuid:62e18c96-cd2c-461b-93d9-900d95eec89e>',
'warc-refers-to': '<urn:uuid:73ee6388-6f0a-460d-ac2e-bbc1a2b63bb4>',
'warc-target-uri': 'https://bh.wikipedia.org/wiki/%E0%A4%B6%E0%A5%8D%E0%A4%B0%E0%A5%87%E0%A4%A3%E0%A5%80:%E0%A4%B5%E0%A4%BF%E0%A4%95%E0%A4%BF%E0%A4%AA%E0%A5%80%E0%A4%A1%E0%A4%BF%E0%A4%AF%E0%A4%BE_%E0%A4%97%E0%A5%88%E0%A4%B0-%E0%A4%AE%E0%A5%81%E0%A4%95%E0%A5%8D%E0%A4%A4_%E0%A4%AB%E0%A4%BE%E0%A4%87%E0%A4%B2_%E0%A4%B5%E0%A5%88%E0%A4%A7_%E0%A4%AC%E0%A5%88%E0%A4%95%E0%A4%B2%E0%A4%BF%E0%A4%82%E0%A4%95_%E0%A4%95%E0%A5%87_%E0%A4%B8%E0%A4%BE%E0%A4%A5?from=Ea',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ई एगो छुपावल गइल श्रेणी बाटे। ई पन्ना सभ पर तबले ना लउकी जबले कि '
'प्रयोगकर्ता के सेटिंग, छुपावल गइल श्रेणी देखावे खाति...'}
```
#### deduplicated_bn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 36198,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:7QRYGJ3YDG7SBTFUVMMALFA6UWNDVLVY',
'warc-date': '2021-03-05T07:10:58Z',
'warc-identified-content-language': 'ben',
'warc-record-id': '<urn:uuid:050c0cdb-562c-49e5-bcb6-7e5350531ea6>',
'warc-refers-to': '<urn:uuid:a3749b59-4285-4e90-ba64-aa9d745c1f46>',
'warc-target-uri': 'https://www.kalerkantho.com/online/business/2020/12/06/982949',
'warc-type': 'conversion'},
'nb_sentences': 8,
'offset': 0},
'text': 'নিজস্ব সংবাদদাতা: গাড়ি নয় যেন মানুষের খাঁচা। নেই কোন ভালো বসার '
'আসন, যা আছে সেগুলো ভাঙ্গাচুরা, ময়লা ও ধুলাবালিতে ভর...'}
```
#### deduplicated_bo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 5059,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XHKOQL5IQBLCVBANFVH66ZZXJZHEEMYW',
'warc-date': '2021-03-03T15:06:26Z',
'warc-identified-content-language': 'zho,bod',
'warc-record-id': '<urn:uuid:3a406f8f-58cd-4990-ae6f-f63dff7e06e3>',
'warc-refers-to': '<urn:uuid:806c4a11-f8cd-49e8-bc22-cae5e0cf6ef2>',
'warc-target-uri': 'http://tcansee.com/goods.php?id=392',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '所有分类 藏学名家名著 国内名家名著 国外名家名著政治 社会 法律 政治 法律 社会 经济文学 艺术 旅游 艺术 文学 旅游宗教 历史 '
'文化 宗教 历史 文化教育 童书 工具书 教辅 童书 工具书语言文字 语言研究 语言 文字期刊 社...'}
```
#### deduplicated_bpy
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8270,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:POHCGWDC32KW74IE26NTJ2UMNX7QRBDB',
'warc-date': '2021-03-05T14:00:16Z',
'warc-identified-content-language': 'ben',
'warc-record-id': '<urn:uuid:d53007ee-ddbe-44e9-8253-235567d2960c>',
'warc-refers-to': '<urn:uuid:0409ce75-26bc-4a60-b08d-4e2b6174127e>',
'warc-target-uri': 'http://pobnapurup.gaibandha.gov.bd/site/page/5dc0a075-18fd-11e7-9461-286ed488c766/%E0%A6%95%E0%A6%BE%E0%A6%B0%E0%A7%8D%E0%A6%AF%E0%A6%BE%E0%A6%AC%E0%A6%B2%E0%A7%80',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'পবনাপুর ইউনিয়ন---কিশোরগাড়ী ইউনিয়নহোসেনপুর ইউনিয়নপলাশবাড়ী '
'ইউনিয়নবরিশাল ইউনিয়নমহদীপুর ইউনিয়নবেতকাপা ইউনিয়নপবনাপুর ইউনিয়...'}
```
#### deduplicated_br
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3134,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:U353JBWLMC22GRYEIDN4WOSBUOIUMYQT',
'warc-date': '2021-02-24T21:00:25Z',
'warc-identified-content-language': 'bre',
'warc-record-id': '<urn:uuid:49d1650d-aaf5-43b9-b340-326746e88b31>',
'warc-refers-to': '<urn:uuid:04877e5f-6b86-497e-b39c-30a72683261f>',
'warc-target-uri': 'https://br.m.wiktionary.org/wiki/dont',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Sellet e vez ouzh ar bajenn pe ar gevrenn-mañ evel un divraz da '
'glokaat e brezhoneg. Mar gouezit tra pe dra diwar-ben...'}
```
#### deduplicated_bs
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8483,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:HS77KGP5HJKJASHMW6WSYV326BPGVM35',
'warc-date': '2021-02-24T18:13:58Z',
'warc-identified-content-language': 'bos,hrv',
'warc-record-id': '<urn:uuid:c12f1b14-4194-405e-a059-9af2f7146940>',
'warc-refers-to': '<urn:uuid:31bedcb4-265f-4aa3-8d2c-cfdc64c42325>',
'warc-target-uri': 'http://mojusk.ba/zastrasujuce-slike-tamnice-u-kojoj-je-skolski-domar-silovao-12-godisnjakinju/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Predsjednica Evropske centralne banke Christine Lagarde izjavila je '
'da njen najveći strah nije da će Evropska...'}
```
#### deduplicated_bxr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6751,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:RELUZWSMYT63FAPLHP55SMNNCSXIQEDX',
'warc-date': '2021-02-26T07:18:33Z',
'warc-identified-content-language': 'mon,rus',
'warc-record-id': '<urn:uuid:efe8d9fa-4329-4479-aa56-43938e8e5370>',
'warc-refers-to': '<urn:uuid:bba3bfb2-b7c7-4605-9f49-34598eac9a5b>',
'warc-target-uri': 'http://soyol.ru/bur/yoho-zanshal/hoityn/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Хүнэй бэе мүнхэ бэшэ. Һүнэһэнэй бэеымнай орхижо, түрэлөө '
'урилхадань, тэрэнэй хальһан боложо ябаһан бэемнай үхэнэ, газ...'}
```
#### deduplicated_ca
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 30591,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DJYNCXSBI5JH4V3LKGE7YNQBL34E3W5G',
'warc-date': '2021-03-02T21:39:28Z',
'warc-identified-content-language': 'cat,eng',
'warc-record-id': '<urn:uuid:ec350f95-900b-4164-aab3-8a6451228d5b>',
'warc-refers-to': '<urn:uuid:4c8e31b8-3011-4a21-9591-39be0942e121>',
'warc-target-uri': 'https://ca.m.wikipedia.org/wiki/Regne_d%27Ayutthaya',
'warc-type': 'conversion'},
'nb_sentences': 33,
'offset': 0},
'text': "El regne d'Ayutthaya va ser un estat a Tailàndia que va existir de "
'1351 a 1767 governat per un rei. El rei Rāmadhipat...'}
```
#### deduplicated_cbk
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 151273,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:JCULI5BTSXOFUJYKZPPLMU5BZEZJZEVJ',
'warc-date': '2021-03-04T21:00:26Z',
'warc-identified-content-language': 'ita',
'warc-record-id': '<urn:uuid:ca25bd6b-9a5f-41b5-8b0f-ad437a545cee>',
'warc-refers-to': '<urn:uuid:ac67c26c-c62a-4c3d-9bd9-dd66a78a474f>',
'warc-target-uri': 'https://it.readkong.com/page/note-di-un-anno-di-lavoro-plural-3281543',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': ' '
'na '
'...'}
```
#### deduplicated_ce
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 5944,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:AXGWUWKZ5HO42LSEO32HWLT77MATHGXB',
'warc-date': '2021-03-03T14:41:28Z',
'warc-identified-content-language': 'eng',
'warc-record-id': '<urn:uuid:1333c910-7921-4bdd-9bb9-1a8322dfa74b>',
'warc-refers-to': '<urn:uuid:9e976ac2-74e4-4e30-8c49-12f2dc1c257c>',
'warc-target-uri': 'https://www.radiomarsho.com/a/27368811.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Апти Бисултанов вина 1959 шарахь. Апти -- гоьваьлла нохчийн '
'кхузаманахьлера байтанча ву. 1983 шарахь цо чекхъяккхира ...'}
```
#### deduplicated_ceb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8799,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:GSVQUFRLD3BYXEG2ASAEVHR2IH4D7A2S',
'warc-date': '2021-03-09T04:28:21Z',
'warc-identified-content-language': 'ceb,eng',
'warc-record-id': '<urn:uuid:e53f5344-29f5-4e59-8dac-8fdc92d1758f>',
'warc-refers-to': '<urn:uuid:03c0e7e5-b84c-4205-80cc-c3fb3dc82406>',
'warc-target-uri': 'https://www.safesworld.com/ceb/safewell-17ef-small-combination-lock-digital-safe-box-with-electronic-combination.html',
'warc-type': 'conversion'},
'nb_sentences': 4,
'offset': 0},
'text': '17EF SERYE Talagsaong design ug madanihon nga kolor naghimo 17EF '
'popular nga sa taliwala sa mga anak ug mga babaye, k...'}
```
#### deduplicated_ckb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8668,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XZOIJPSX5QTL5QQPQMXEVADFHZTXMP5I',
'warc-date': '2021-03-09T03:25:59Z',
'warc-identified-content-language': 'kur,eng',
'warc-record-id': '<urn:uuid:9fe2f7e9-c158-4b84-a4a3-24e51acbd69e>',
'warc-refers-to': '<urn:uuid:14902cc0-948b-4dcf-bde6-e687ba41212f>',
'warc-target-uri': 'https://www.dastihawkary.org/blog/portfolio/social-harms-of-drugs/?lang=en',
'warc-type': 'conversion'},
'nb_sentences': 9,
'offset': 0},
'text': 'وەبیرم دێ\u200c لە كۆتایی هەشتاكانی سەدەی ڕابردوو دیاردەیەك هەبوو '
'لەنێو گەنجە لادەرەكانی شاری هەولێر و سەرشەقام هەڵدەستان ...'}
```
#### deduplicated_cs
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 17263,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:EJZ477E7PWMVVVM777MHB5DMDHVYEWK6',
'warc-date': '2021-03-05T11:28:42Z',
'warc-identified-content-language': 'ces',
'warc-record-id': '<urn:uuid:6fc03e7f-9768-4f26-89ce-84fa4732e3c0>',
'warc-refers-to': '<urn:uuid:d78128e5-f667-4461-9f0c-2263d75b74a1>',
'warc-target-uri': 'https://www.lidovky.cz/relax/dobra-chut/mak-a-svestky-vyzkousejte-makovec-podle-romana-pauluse.A150427_125913_dobra-chut_ape?recommendationId=00000000-0000-5000-8000-000000000000',
'warc-type': 'conversion'},
'nb_sentences': 12,
'offset': 0},
'text': 'Porno motor vyhledávání o nové sedlo masáž se svou. pro měkký sex '
'voda učitelka kočička videa stránky Starý pár sex n...'}
```
#### deduplicated_cv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4133,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FKR5EKWIFACLGBIK6IKLHTHDNTEZNF3T',
'warc-date': '2021-03-03T14:25:27Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:8140dbf0-2fb0-48d8-a834-c1b052bcc72d>',
'warc-refers-to': '<urn:uuid:cca433fe-6646-4ab7-b5da-f8e17821b43d>',
'warc-target-uri': 'http://chuv-krarm.3dn.ru/blog/vladimir_leontev_savna_masharam_emer_perle_purnar_i/2013-02-08-47',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Сайт авторĕ тата модераторĕ- Михайлов Алексей, Чăваш Республикин '
'Президенчĕн 2010,2012 çулсенчи стипендиачĕ, Сайт адм...'}
```
#### deduplicated_cy
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1967,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:RNFNJNY7RHGXN5NPEVF2PYNNIWOTDAMJ',
'warc-date': '2021-03-09T03:48:16Z',
'warc-identified-content-language': 'cym,eng',
'warc-record-id': '<urn:uuid:66f063ba-6a33-4f53-9cfb-7dc64a292e89>',
'warc-refers-to': '<urn:uuid:281f9c10-2d7d-4781-82f6-a504f27852a1>',
'warc-target-uri': 'https://cy.wikipedia.org/wiki/John_T._Koch',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Graddiodd o Brifysgol Harvard, gan gymeryd doethuriaeth mewn '
'Ieithoedd a Llenyddiaethau Celtaidd yn 1985. Bu hefyd yn...'}
```
#### deduplicated_da
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 22154,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:AF2FFBNZQ3TOEEZ3MFDU77CXZ6PVU3ZB',
'warc-date': '2021-03-01T12:49:13Z',
'warc-identified-content-language': 'dan',
'warc-record-id': '<urn:uuid:92fffabd-5d36-4539-b8eb-18a0f2554ddb>',
'warc-refers-to': '<urn:uuid:1970d6bb-474f-448b-a3e1-8a77c9a32cb6>',
'warc-target-uri': 'http://rosamundis.dk/thai-horsens-gode-parfumer-til-m%C3%A6nd/',
'warc-type': 'conversion'},
'nb_sentences': 16,
'offset': 0},
'text': 'Mange praler af den sindsro, de har fundet i huler i det '
'norske/forfaldne franske ferielejligheder etc., hvor de har ...'}
```
#### deduplicated_de
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 11180,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:LLCPCA3RGKMXLYUEA3OZ2KFEEBNEOPE2',
'warc-date': '2021-03-09T01:22:52Z',
'warc-identified-content-language': 'eng,deu',
'warc-record-id': '<urn:uuid:0128ab60-86c8-4dc2-b1cf-57950654ae38>',
'warc-refers-to': '<urn:uuid:ff27032b-b843-4ba3-b1e2-377793173071>',
'warc-target-uri': 'http://bioconcepts.de/views/search.php?term=231&listed=y',
'warc-type': 'conversion'},
'nb_sentences': 16,
'offset': 0},
'text': 'Kreismeisterschaften bringen zahlreiche Sunderner Medaillengewinner '
'und Titelträger - Tischtennis im Sauerland\n'
'Am ver...'}
```
#### deduplicated_diq
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4196,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DTA56M722SM5BZLNADOCPXQGGT32J46O',
'warc-date': '2021-03-06T15:51:03Z',
'warc-identified-content-language': 'tur,srp,nno',
'warc-record-id': '<urn:uuid:b7dcd4a4-b130-4009-88d0-631ca51a7bcc>',
'warc-refers-to': '<urn:uuid:fe4e4ad7-3089-40d2-aa29-f675e3cea0dd>',
'warc-target-uri': 'https://diq.wikipedia.org/wiki/Z%C4%B1wan%C3%AA_Slawki',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Zıwanê Slawki, zıwano merdumanê Slawano. Zıwanê Slawki yew lızgeyê '
'Zıwananê Hind u Ewropao. Keyeyê Zıwananê Slawki be...'}
```
#### deduplicated_dsb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 20663,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:WWZOAFJJLJ4OHG2PTVLCMP664OR26XCR',
'warc-date': '2021-02-27T22:03:14Z',
'warc-identified-content-language': None,
'warc-record-id': '<urn:uuid:239b7155-8f37-4889-bad8-5bdb0aaa83c2>',
'warc-refers-to': '<urn:uuid:2714b744-a080-4807-a29a-d8f99c80e49c>',
'warc-target-uri': 'https://dsb.m.wikipedia.org/wiki/P%C5%9Bed%C5%82oga:LocMap',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Mjaz tamnjejšej pśedłogu a </noinclude>-kodom mógu pśidatne '
'kategorije a cuzorěcne wótkaze stojaś. Ewentualne pśikład...'}
```
#### deduplicated_dv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7923,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:ECFUNRNYICXFAZXP5TLM45DPGJX5AHOI',
'warc-date': '2021-02-24T19:53:40Z',
'warc-identified-content-language': 'div,eng',
'warc-record-id': '<urn:uuid:23e2557a-dacc-428c-99fc-e41d4ce2ed95>',
'warc-refers-to': '<urn:uuid:067b6719-0209-49df-8198-27b1954b61b4>',
'warc-target-uri': 'https://dhiislam.com/114288',
'warc-type': 'conversion'},
'nb_sentences': 7,
'offset': 0},
'text': 'މީސްތަކުންގެ ފިކުރާއި ކުޅެލުމަށްޓަކައި މިޒަމާނުގެ ވަސީލަތްތަކުގެ '
'ބޭނުން އެންމެ ރަނގަޅު ގޮތުގައި ހިފަމުންދޭ: ޝެއިޚް ފި...'}
```
#### deduplicated_el
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 12604,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2LXNVVGR3C4G72RLJUJBKUWLZZJ53TPX',
'warc-date': '2021-03-03T11:34:34Z',
'warc-identified-content-language': 'ell,eng',
'warc-record-id': '<urn:uuid:d95ddbe8-2e54-4d61-a6af-227212090684>',
'warc-refers-to': '<urn:uuid:a0e15450-8455-4b2f-ad8f-3858873a538d>',
'warc-target-uri': 'https://www.androsportal.gr/category/topika/nea-syllogwn/',
'warc-type': 'conversion'},
'nb_sentences': 18,
'offset': 0},
'text': 'Η ραδιοφωνική διαφήμιση χαρακτηρίζεται από αμεσότητα και οικειότητα '
'λόγω της στενής σχέσης του μέσου με τους ακροατές...'}
```
#### deduplicated_eml
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 11710,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:OM2W34UTSIJJHAEXEX42BYMZWBB7U3FS',
'warc-date': '2021-03-05T23:48:29Z',
'warc-identified-content-language': 'ita',
'warc-record-id': '<urn:uuid:26a267af-a6de-4e84-b945-411b78b4815a>',
'warc-refers-to': '<urn:uuid:656aaba2-ff1d-4d7c-915a-9a555533aa42>',
'warc-target-uri': 'https://eml.wikipedia.org/wiki/2_(n%C3%B9mer)',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': "Al 2 'l è al prim nùmer prim ed tùta la séri ch'a s cata in di "
"nùmer naturèl e anc 'l ùnic ch'al sìa pèra:\n"
"Insèm a 'l..."}
```
#### deduplicated_en
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 15201,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:EIQTEGOE4V5SDID2OLTO4PWWCTW3AD5H',
'warc-date': '2021-03-03T18:20:30Z',
'warc-identified-content-language': 'eng',
'warc-record-id': '<urn:uuid:7cec445b-76fe-4ce2-ab43-8a85de680c6f>',
'warc-refers-to': '<urn:uuid:1cf845b2-3015-4f01-abaf-262af4adeba5>',
'warc-target-uri': 'https://www.aqueencitysound.com/2016/05',
'warc-type': 'conversion'},
'nb_sentences': 28,
'offset': 0},
'text': 'But the term “extension” also means lengthening. EkhartYoga members '
'can get to k… Renforcement du dos (muscles para-v...'}
```
#### deduplicated_eo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 27953,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:YO4NP6746IFQDF5KISEPLNFA2QD3PTEO',
'warc-date': '2021-03-09T05:29:46Z',
'warc-identified-content-language': 'epo,eng',
'warc-record-id': '<urn:uuid:5e3bc7b3-723f-4de9-8202-790351a2253f>',
'warc-refers-to': '<urn:uuid:dd5e537a-f340-4418-bc07-487232ea197c>',
'warc-target-uri': 'http://kantaro.ikso.net/cxu?image=kis_kut.png&ns=&tab_details=view&do=media',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Iloj Montri paĝonMalnovaj reviziojRetroligoj Freŝaj '
'ŝanĝojMedio-administriloIndekso RegistriĝiEnsaluti'}
```
#### deduplicated_es
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8322,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DXIQKIWES4PP64BTGK5BYTJ3TX4RVQSI',
'warc-date': '2021-03-03T23:27:45Z',
'warc-identified-content-language': 'spa,eng',
'warc-record-id': '<urn:uuid:4275a14a-f997-4e58-8cf6-046006d76dab>',
'warc-refers-to': '<urn:uuid:d54d1a7b-1316-4bd1-8147-7a44ec5b3803>',
'warc-target-uri': 'https://www.rcrperu.com/defensoria-del-pueblo-oficina-en-lima-sur-registro-mas-de-3000-casos-durante-el-2020/',
'warc-type': 'conversion'},
'nb_sentences': 7,
'offset': 0},
'text': 'Se prevé que a finales de mes haya llegado al 92,5 por ciento de '
'los centros, aquellos en los que no hay confirmados ...'}
```
#### deduplicated_et
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 57234,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:JU7SWP3ZS36M3ABAEPNTFH37MVI2SLAF',
'warc-date': '2021-02-24T20:43:43Z',
'warc-identified-content-language': 'est',
'warc-record-id': '<urn:uuid:2bbcaa39-7336-4ade-accf-1b582785f731>',
'warc-refers-to': '<urn:uuid:849563c9-8549-4bdc-a09c-d179c8399ae0>',
'warc-target-uri': 'https://cardiaccareclinic.com/chto-luchshe-panangin-ili-kardiomagnil.html',
'warc-type': 'conversion'},
'nb_sentences': 129,
'offset': 0},
'text': 'Kas hirmu ei pruugi tekitada hoopis segadus? Näiteks võtame Ukraina '
'kogemuse. Järsku ilmusid välja lindikestega mehed...'}
```
#### deduplicated_eu
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4248,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:STDEJOH35DPN5UB52OUZJJC4YCN7EH3N',
'warc-date': '2021-03-09T05:11:48Z',
'warc-identified-content-language': 'spa,eus',
'warc-record-id': '<urn:uuid:fb6752f7-5e91-4d0c-b022-71bd5d3ce910>',
'warc-refers-to': '<urn:uuid:faca7a42-20c2-4c4c-bd8a-6d4be5a1adb6>',
'warc-target-uri': 'http://intermedia.eus/la-comunicacion-imprescindible-lo-que-no-debemos-olvidar-de-2015-resumido-en-447/',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Nesken artean bokazio zientifikoak eta teknologikoak sustatzeko '
'INSPIRA STEAM proiektua ia 120 ikastetxetako 5.000 ik...'}
```
#### deduplicated_fa
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 10411,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VM7Q7TXNMU2SRNHFJSZMBCKU2YVRKI56',
'warc-date': '2021-03-02T11:23:27Z',
'warc-identified-content-language': 'fas',
'warc-record-id': '<urn:uuid:9f666d03-9592-4f59-9111-981a558b3a32>',
'warc-refers-to': '<urn:uuid:8daf3dc1-92dd-4dbf-a339-992c99f09112>',
'warc-target-uri': 'https://zhycan.com/concough/blog/%D9%86%D8%AD%D9%88%D9%87-%D8%AB%D8%A8%D8%AA-%D9%86%D8%A7%D9%85-%DA%A9%D9%86%DA%A9%D9%88%D8%B1-%D8%AF%DA%A9%D8%AA%D8%B1%DB%8C-97-%D8%A7%D8%B9%D9%84%D8%A7%D9%85-%D8%B4%D8%AF-%D8%A7%D9%85/',
'warc-type': 'conversion'},
'nb_sentences': 16,
'offset': 0},
'text': 'انجمن دانشجویان پیام نور تبليغات تماس با ما تبلیغات دسته بندی باز / '
'بسته کردن دسته بندی ها . شرایط اختصاصی برای شغل د...'}
```
#### deduplicated_fi
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 19216,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:5OUEZDSL7KB2VHT2R67YZDER6UO5FHON',
'warc-date': '2021-03-05T00:14:23Z',
'warc-identified-content-language': 'fin,eng',
'warc-record-id': '<urn:uuid:61e0fc42-ceee-4026-ba76-3c8a8addd596>',
'warc-refers-to': '<urn:uuid:c4ba3c9f-5a6c-4de5-8f77-f5beb547315c>',
'warc-target-uri': 'https://kreditassms.eu/arvostelut-treffisivusto-py%C3%B6re%C3%A4-tanssi/',
'warc-type': 'conversion'},
'nb_sentences': 46,
'offset': 0},
'text': 'Facebook ulkomaiset morsiamet fantasia lähellä lohja mistä pillua '
'porno leffat sex treffit karvaiset tussut Thai mass...'}
```
#### deduplicated_fr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 5274,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XUVXOZU2BIT4TIDEVHLLBLUIHRS4L7WV',
'warc-date': '2021-03-03T14:00:24Z',
'warc-identified-content-language': 'fra,eng',
'warc-record-id': '<urn:uuid:76252d00-9672-479c-9580-722614e078f9>',
'warc-refers-to': '<urn:uuid:4a6bde1e-9596-4388-9334-cc473a7c93ee>',
'warc-target-uri': 'https://www.cahier-des-charges.net/produit/modele-cahier-des-charges-de-logiciel-de-gestion-de-processus-metier/',
'warc-type': 'conversion'},
'nb_sentences': 9,
'offset': 0},
'text': 'Créée en 1765 par le duc de Villars, alors gouverneur de Provence, '
'l’École supérieure d’art d’Aix en Provence est un ...'}
```
#### deduplicated_frr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 27381,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DJE2KO4YWWRERKS5JYSK5JCJWYZ6DJHM',
'warc-date': '2021-03-01T03:40:10Z',
'warc-identified-content-language': 'ell',
'warc-record-id': '<urn:uuid:3a2a34ae-1c42-4d2e-bb08-8dabc916ea30>',
'warc-refers-to': '<urn:uuid:caeb39b2-da76-463d-b80c-4917d3dca230>',
'warc-target-uri': 'https://www.sedik.gr/neo/el/%CE%B1%CF%81%CF%87%CE%B5%CE%AF%CE%BF-%CE%B5%CE%BB%CE%B1%CE%B9%CE%BF%CE%BD%CE%AD%CF%89%CE%BD/%CE%B1%CF%81%CF%87%CE%B5%CE%AF%CE%BF-%CE%B5%CE%BB%CE%B1%CE%B9%CE%BF%CE%BD%CE%AD%CF%89%CE%BD-2009/178-178-title',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ '
'’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’...'}
```
#### deduplicated_fy
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1807,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:JABSHFJ2L6SQOXPPTBYGZGR24GCEDTTM',
'warc-date': '2021-03-09T04:24:30Z',
'warc-identified-content-language': 'fry',
'warc-record-id': '<urn:uuid:fd1b28cb-20ce-4082-b1ca-40045ed6af73>',
'warc-refers-to': '<urn:uuid:bc50e1f0-6384-4054-8916-2a489e9a0ffd>',
'warc-target-uri': 'https://www.omropfryslan.nl/nijs/201805-gruttere-lisboksstal-tastien',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Melkfeehâlders yn Súdwest-Fryslân kinne tenei makliker '
"lisboksstâlen fergrutsje no't de gemeente de lanlike wet op st..."}
```
#### deduplicated_ga
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3296,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:WF6SCFDXN3NOT7FPKTEFOAMMPKXSEZ2W',
'warc-date': '2021-03-09T04:37:11Z',
'warc-identified-content-language': 'gle',
'warc-record-id': '<urn:uuid:bff39289-dbf7-444c-8df1-382fd46c993d>',
'warc-refers-to': '<urn:uuid:e27ba1c5-5707-4e9f-8ba8-f42c67bd9fc9>',
'warc-target-uri': 'http://nos.ie/cultur/iarratais-a-lorg-don-slam-filiochta-agus-duaischiste-700-ann-i-mbliana/',
'warc-type': 'conversion'},
'nb_sentences': 6,
'offset': 0},
'text': 'Tá duaischiste £700 ar fáil do Slam Filíochta Liú Lúnasa a bheidh '
'ar siúl ar líne ag deireadh na míosa seo chugainn. ...'}
```
#### deduplicated_gd
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7659,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:OO363HOO6EDDYSBTTYB6H4WYAJBBMJ6D',
'warc-date': '2021-03-03T15:22:11Z',
'warc-identified-content-language': 'gla',
'warc-record-id': '<urn:uuid:e24cc86f-ae2c-49f6-b668-cda4f514a34d>',
'warc-refers-to': '<urn:uuid:1739d2d8-974d-4c29-b8d0-3a3ef9082537>',
'warc-target-uri': 'http://gd.cnswmc.com/ty320-3-bulldozer-product/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Tha inneal-brathaidh TY320-3 crochte leth-chruaidh, gluasad '
'uisgeachaidh, inneal tarbh fo smachd seòrsa hydraulic. Ta...'}
```
#### deduplicated_gl
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4202,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TIH7ARF4FNLH7VRGHXKOWVHNXNXC2HZX',
'warc-date': '2021-03-09T04:47:46Z',
'warc-identified-content-language': 'glg',
'warc-record-id': '<urn:uuid:983dd790-0846-4232-a7b4-3956af0982a8>',
'warc-refers-to': '<urn:uuid:b77207af-29d0-459f-9a55-0b25501d3e8b>',
'warc-target-uri': 'http://concellomuxia.com/item/outras-capelas/',
'warc-type': 'conversion'},
'nb_sentences': 8,
'offset': 0},
'text': 'O templo actual é producto de diversas reconstrucións que se '
'realizaron a finais do século XVII e principios do XVIII...'}
```
#### deduplicated_gn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3873,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FWN62CTWNJKPWUARS4BMBUFU6OVHL6XP',
'warc-date': '2021-02-27T22:49:49Z',
'warc-identified-content-language': 'grn,eng,bih',
'warc-record-id': '<urn:uuid:b4954ced-abe0-487e-b5b0-a26beb751a02>',
'warc-refers-to': '<urn:uuid:be5468f1-47f0-4bd8-a177-3529a14dead7>',
'warc-target-uri': 'https://gn.wikipedia.org/wiki/Apere%27arusu',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Ko ñe\'ẽ "apere\'arusu" ou avañe\'ẽ ñe\'ẽngue "apere\'a" he\'ise '
'India Tapiti, ha avañe\'ẽ ñe\'ẽngue "rusu" he\'iséva iguasúva.'}
```
#### deduplicated_gom
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8747,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CKNSFAH2KISLLR7222FSQSPENYHQTAX3',
'warc-date': '2021-03-01T11:10:29Z',
'warc-identified-content-language': 'mar',
'warc-record-id': '<urn:uuid:d4622a3e-1b0e-4775-b25d-273ee14ae176>',
'warc-refers-to': '<urn:uuid:9d00e57b-9031-4f86-a9c8-cc3c0c2213a7>',
'warc-target-uri': 'https://gom.m.wikipedia.org/wiki/%E0%A4%B5%E0%A5%80%E0%A4%9C',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'कांय वस्तू रगडल्यो तर तांचेकडेन हलक्यो वस्तू आकर्शित जाता हेंजेन्ना '
'पळयलें तेन्ना वीज हे ऊर्जेची कल्पना मनशाक आयली.हे...'}
```
#### deduplicated_gu
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 15036,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2FGV42SN72HRKRBEEQ7QJVJBLUYQPCIH',
'warc-date': '2021-03-09T04:48:08Z',
'warc-identified-content-language': 'eng,khm,lao',
'warc-record-id': '<urn:uuid:04d772d6-09db-4d5a-86c8-22b914a35b6f>',
'warc-refers-to': '<urn:uuid:f3cdcafa-5a28-4fbb-81df-7cc5e7bb3248>',
'warc-target-uri': 'http://www.ahealthyme.com/RelatedItems/RelatedDocuments.pg?d=&TypeId=121&ContentId=761&Category=DC',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ધ્યાન આપો: જો તમે ગુજરા તી બોલતા હો, તો તમને ભા ષા કીય સહાય તા સેવા '
'ઓ વિ ના મૂલ્યે ઉપલબ્ધ છે. તમા રા આઈડી કાર ્ડ પર આ...'}
```
#### deduplicated_gv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 29707,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TIDW47D4MAHOLY6PQZ5SHLDYQIJ66REQ',
'warc-date': '2021-03-06T18:16:22Z',
'warc-identified-content-language': 'glv,eng',
'warc-record-id': '<urn:uuid:c7a5e531-487b-4e52-96ca-33b658691652>',
'warc-refers-to': '<urn:uuid:fa7285d4-126c-458f-9a72-d0d8615ce494>',
'warc-target-uri': 'https://gv.wikipedia.org/wiki/%C3%87hengoaylleeaght',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Ta çhengoaylleeaght feamagh eiyrt er sheiltynyssyn çhengoaylleeagh '
'ayns ayrnyn myr ynsaghey çhengaghyn joaree, glare-...'}
```
#### deduplicated_he
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 12254,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:BL56ZUXYO5GLIO6YTBUWKPVYJN2BKCIM',
'warc-date': '2021-03-09T10:29:09Z',
'warc-identified-content-language': 'heb,eng',
'warc-record-id': '<urn:uuid:1ae77825-a836-424e-a8b1-1f9c985a41b9>',
'warc-refers-to': '<urn:uuid:fce3d3dc-979e-4603-82e3-027b75346e52>',
'warc-target-uri': 'https://shop.makeup.land/collections/frontpage',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'הולדת פג היא אירוע מטלטל לכל משפחה, אך הולדת פג בצל מגפת הקורונה '
'מאתגרת אף יותר? מהם האתגרים עמם מתמודדים ההורים והצו...'}
```
#### deduplicated_hi
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7897,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VZCN5HXN57VQHZJT5G3NWV7RCIT4GP7T',
'warc-date': '2021-02-26T10:18:11Z',
'warc-identified-content-language': 'hin,eng',
'warc-record-id': '<urn:uuid:6cccccb7-be0e-4c16-83be-7b4150b107ac>',
'warc-refers-to': '<urn:uuid:41eda5d1-e2cf-44f4-9f5b-c074a2de89da>',
'warc-target-uri': 'https://36.gurturgoth.com/2019/11/blog-post_8.html',
'warc-type': 'conversion'},
'nb_sentences': 5,
'offset': 0},
'text': 'Bill Gates Biography in Hindi, विश्व के सबसे अमीर इंसान और '
'माइक्रोसॉफ्ट कंपनी के संस्थापक Bill Gates जिसने अपनी बुद्ध...'}
```
#### deduplicated_hr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 41545,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:6NTZEPK7ETF4AOLM3YDZRLRGZAKH7XM3',
'warc-date': '2021-03-09T04:58:04Z',
'warc-identified-content-language': 'hrv,bos,eng',
'warc-record-id': '<urn:uuid:32361cc9-e12a-4861-978a-b94b84efe78c>',
'warc-refers-to': '<urn:uuid:f0476e5f-e04c-4741-94a6-ddbcfb25c17e>',
'warc-target-uri': 'http://mjesec.ffzg.hr/webpac/?rm=results&show_full=1&f=PersonalName&v=Sanader%20Mirjana',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': 'Impresum: Pula : Sveučilište u Zagrebu, Međunarodno središte '
'hrvatskih sveučilišta u Istri, Međunarodni istraživački ...'}
```
#### deduplicated_hsb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3352,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:E5ZCT5OIZBDV2EFBNX3MSLFJKKMZWQWI',
'warc-date': '2021-03-08T22:15:50Z',
'warc-identified-content-language': None,
'warc-record-id': '<urn:uuid:374a31b4-d38f-4d94-b3df-59013b15e644>',
'warc-refers-to': '<urn:uuid:fa9b7b26-2b4c-4acc-a652-47047617b0c0>',
'warc-target-uri': 'https://www.serbske-nowiny.de/index.php/hsb/z-luzicy/lokalka/item/50643-jednotna-proty-ka-tr-bna',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Žonjace akciske tydźenje zahajene\tDźensniši Mjezynarodny dźeń '
'žonow je zazběh hač do 22. apryla trajacych ...\t\n'
'Wotstr...'}
```
#### deduplicated_ht
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 17823,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:LXQEYMTPIKHPAYKEKIZF6FCMC6WH66PW',
'warc-date': '2021-02-25T02:48:22Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:a5599306-82ad-4740-9c00-5bba34c96d54>',
'warc-refers-to': '<urn:uuid:2378d2f7-69a4-4f8a-ad03-4d556d031ebb>',
'warc-target-uri': 'http://mywebstores.ru/index.php?id_product=1841&controller=product',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'начать us $ nan us $ nan us $ nan us $ nan us $ nan us $ nan us $ '
'nan us $ nan us $ nan us $ nan us $ nan us $ nan us...'}
```
#### deduplicated_hu
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 39801,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:B3XHZ4C4AJYQLVV3ESGOVZU6FZ5N5637',
'warc-date': '2021-02-26T07:03:18Z',
'warc-identified-content-language': 'hun',
'warc-record-id': '<urn:uuid:926ed467-3adb-44f5-b33c-63112879ba5a>',
'warc-refers-to': '<urn:uuid:9d9175b4-6b0a-45e8-961b-61e9d50eb684>',
'warc-target-uri': 'https://luminanz.eu/anya-hatartalan-ingyen-videok-pina-nagy-video-video-sex-szekx-hd-videa-nyelvu-%C3%B6reg/',
'warc-type': 'conversion'},
'nb_sentences': 104,
'offset': 0},
'text': 'A WordPress egy ingyenesen letölthető rendszer. Letöltés után csak '
'telepíteni kell a webszerverre és máris használhat...'}
```
#### deduplicated_hy
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6269,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:42PWBXN2Q7PFCRFWIDLTW42KUUGAKQOE',
'warc-date': '2021-02-24T23:49:31Z',
'warc-identified-content-language': 'hye,eng',
'warc-record-id': '<urn:uuid:932d1903-aea7-4be9-abb4-6b3114592c9c>',
'warc-refers-to': '<urn:uuid:cecf676f-884a-4311-a0b5-45ade0f517b7>',
'warc-target-uri': 'https://www.usanogh.am/lur/tramp-amn-coronavirus/',
'warc-type': 'conversion'},
'nb_sentences': 4,
'offset': 0},
'text': 'ՀՀ ԳԱԱ Զեկույցներ =Reports NAS RA կիրառում է «Ստեղծագործական '
'համայնքներ» հեղինակային իրավունքի արտոնագիրը համաձայն որ...'}
```
#### deduplicated_ia
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9479,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:4JBN4SUDHHRPZI3TAVTZ4JUYSSOGGRFX',
'warc-date': '2021-03-01T17:14:58Z',
'warc-identified-content-language': 'ron,eng',
'warc-record-id': '<urn:uuid:5abe05ff-7309-4c3f-8ccd-175a12a655a2>',
'warc-refers-to': '<urn:uuid:8dec50fd-2be1-4bcf-8bb2-8cb9826c2465>',
'warc-target-uri': 'https://www.monitorulsv.ro/Ultima-ora-local/2008-02-18/Campania-electorala-interzisa-in-Primaria-Suceava',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha '
'ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ...'}
```
#### deduplicated_id
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3080,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XU6GIUNYT5ELGH5XSZ4FUARC3YTJAD5P',
'warc-date': '2021-03-05T03:32:56Z',
'warc-identified-content-language': 'ind',
'warc-record-id': '<urn:uuid:2328da88-ee5f-4b4c-af3e-25dc4a574041>',
'warc-refers-to': '<urn:uuid:0781f7e2-f020-402b-b204-71fdf299f956>',
'warc-target-uri': 'https://sulsel.kemenag.go.id/berita/berita-kontributor/stqh-26-tingkat-kabupaten-jeneponto-siap-di-gelar',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': '* Masa berlaku normal poin 1 (satu) tahun dan masa berlaku bonus '
'poin sampai dengan 31 Desember 2020.\n'
'Diskon dari Ban...'}
```
#### deduplicated_ie
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 16919,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:W7UDGWMCEYQFEIPJMFZKX72Z6MH4XCUP',
'warc-date': '2021-03-08T16:16:42Z',
'warc-identified-content-language': 'ron,eng',
'warc-record-id': '<urn:uuid:f5ba5473-8eb2-41f4-9e43-3d36f14243a1>',
'warc-refers-to': '<urn:uuid:d2784efa-8250-4370-a348-28c640195663>',
'warc-target-uri': 'https://rolabel.info/door/yX-WpseZpNycfXY/luis-gabriel-haziran-te-am-cautat-si-te-am-gasit-official-video.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Va iubesc mult mult mult mult mult mult mult mult mult mult mult '
'mult mult mult mult mult mult mult mult mult mult mu...'}
```
#### deduplicated_ilo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3511,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:NLHH2LVPZTUZE37ET2FJIRZNOLPLKK4O',
'warc-date': '2021-03-03T15:52:32Z',
'warc-identified-content-language': 'tgl',
'warc-record-id': '<urn:uuid:2fb6a437-41c8-4c2c-9f5d-2e8c34df9f2b>',
'warc-refers-to': '<urn:uuid:bdc072a0-db63-4256-a96b-7515a2c4fdfd>',
'warc-target-uri': 'https://ilo.m.wikipedia.org/wiki/Amphibia',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Daytoy nga artikulo dagiti nangruna nga artikulo ket pungol. '
'Makatulongka iti Wikipedia babaen ti panagnayon iti daytoy.'}
```
#### deduplicated_io
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3586,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VUQPETM2PUWBL5AGADEVN2FPE7KURXG4',
'warc-date': '2021-03-03T15:22:41Z',
'warc-identified-content-language': 'ara',
'warc-record-id': '<urn:uuid:fd8a899b-d54a-424d-9955-a90b81e16439>',
'warc-refers-to': '<urn:uuid:c40226a6-6851-4009-a834-77a1a3e0c0f3>',
'warc-target-uri': 'https://io.wikipedia.org/wiki/New_Vienna,_Iowa',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': "Segun l'Usana Kontado Ministerio, l'urbo havas entote 1.2 km², "
'equivalanta a 0.4 mi², di qui 1.2 km² (0.4 mi²) esas l...'}
```
#### deduplicated_is
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1829,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DXUGRT4OK7WRCOPGB7AAKLHPUDTBDRO2',
'warc-date': '2021-03-09T04:40:07Z',
'warc-identified-content-language': 'isl',
'warc-record-id': '<urn:uuid:6568bf31-b402-45b8-9ddb-6ce0f3d0a323>',
'warc-refers-to': '<urn:uuid:5daa12c0-604a-4233-9ed8-d4e245af4048>',
'warc-target-uri': 'http://hugvis.hi.is/',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Vegna hertra aðgerða í bará ttunni við Covid19 munum við takmarka '
'gestafjölda í laugum okkar við 80 manns. Thank you ...'}
```
#### deduplicated_it
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 14112,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:MLJ4TW2HJZAPE2ORVARPJES6GRGO6ZLK',
'warc-date': '2021-03-05T13:56:32Z',
'warc-identified-content-language': 'ita',
'warc-record-id': '<urn:uuid:31d7ebb5-c1f7-468b-92f8-b79b7c28af9f>',
'warc-refers-to': '<urn:uuid:f92f33a2-6940-49fd-a21e-228ee5d2efb1>',
'warc-target-uri': 'https://mauriziomezzetti.com/patologie-trattate/',
'warc-type': 'conversion'},
'nb_sentences': 47,
'offset': 0},
'text': 'Il Presidente del Caffè Letterario Quasimodo di Modica, Domenico '
'Pisana, sarà ospite a Taranto, il prossimo 4 maggio,...'}
```
#### deduplicated_ja
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 16411,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XOFBBBX7LINQS3EZN5VH6OQ7PPFNRICJ',
'warc-date': '2021-03-09T01:09:27Z',
'warc-identified-content-language': 'jpn,eng,lat',
'warc-record-id': '<urn:uuid:5c0685f4-736d-4155-9153-56cf79462df4>',
'warc-refers-to': '<urn:uuid:88586e1b-926d-4291-910f-53680e3d6482>',
'warc-target-uri': 'http://flpj.karapyzi.ru/30',
'warc-type': 'conversion'},
'nb_sentences': 14,
'offset': 0},
'text': '番組『日本を元気に！スマイルサプライズ！』が、28日に放送（後7：00）。コロナ禍や自然災害など、日本が長いトンネルに入ってしまったような状態だが、「でも、きっとこの先に明るい出口がある！」と明るい未...\n'
'プリゲーム『ポケモンスマイ...'}
```
#### deduplicated_jbo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6970,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2EVVU2OCTSB5EYCHSV6Z7I3PMQSNNOED',
'warc-date': '2021-03-03T23:28:54Z',
'warc-identified-content-language': None,
'warc-record-id': '<urn:uuid:0d4387a2-391d-4e3e-8772-808face0ab78>',
'warc-refers-to': '<urn:uuid:4e45af2a-aea7-4f1a-af89-6ee5f69b7bfd>',
'warc-target-uri': 'https://jbo.m.wikipedia.org/wiki/mumyma%27i_7moi',
'warc-type': 'conversion'},
'nb_sentences': 26,
'offset': 0},
'text': "ni'o 7 la mumast. cu 7moi djedi fi'o masti la mumast. noi ke'a cu "
'mumoi masti .i 6 la mumast. cu purlamdei .ije 8 la ...'}
```
#### deduplicated_jv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8822,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:NPQGATEVIAYLOSLDB22EB7IYDVBZ7N6Q',
'warc-date': '2021-03-09T11:14:25Z',
'warc-identified-content-language': 'jav',
'warc-record-id': '<urn:uuid:db7d8bd7-a3a3-4a30-8786-7efb2352285d>',
'warc-refers-to': '<urn:uuid:2cb85a37-545e-471a-b7e7-cb334112f0e3>',
'warc-target-uri': 'https://jv.wikipedia.org/wiki/Bon%C3%A9kah',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Yèn sadurungé golèkan digawé kanggo awaké dhéwé, wiwit jaman iki '
'dikomersialakaké. Fungsiné owah saka ritual lan mode...'}
```
#### deduplicated_ka
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 42480,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:HHSMTLZXKA4SQDPDBWAOUFELXBUJZJKO',
'warc-date': '2021-03-06T15:33:35Z',
'warc-identified-content-language': 'kat,eng',
'warc-record-id': '<urn:uuid:7d931f2a-a6ef-4070-9277-2033e7e96b9b>',
'warc-refers-to': '<urn:uuid:89429497-9722-45e6-95a6-699ef7280e6c>',
'warc-target-uri': 'https://ka.m.wikipedia.org/wiki/%E1%83%93%E1%83%90%E1%83%A1%E1%83%A2%E1%83%98%E1%83%9C_%E1%83%B0%E1%83%9D%E1%83%A4%E1%83%9B%E1%83%90%E1%83%9C%E1%83%98',
'warc-type': 'conversion'},
'nb_sentences': 36,
'offset': 0},
'text': 'დასტინ ჰოფმანი[1] (ინგლ. Dustin Lee Hoffman დ. 8 აგვისტო, 1937) — '
'ორგზის კინოაკადემიის ოსკარისა და ექვსგზის ოქროს გლო...'}
```
#### deduplicated_kk
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9197,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:BJW4PLV2UOAJLJO6E55YH7DAEWQTFQUZ',
'warc-date': '2021-03-09T04:35:14Z',
'warc-identified-content-language': 'rus,kaz',
'warc-record-id': '<urn:uuid:ddd1d3e1-3bf3-4c4a-b722-8e293ab16f75>',
'warc-refers-to': '<urn:uuid:097c4f10-4bdc-400d-ab39-c04e4f98f51f>',
'warc-target-uri': 'http://blogs.kazakh.ru/blogs/index.php?page=group&gid=6&id=3&PAGEN_1=3%3Fid%3D2?id=6',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Бұрынғы жоғары лауазымды шенеунік Анатолий Шкарупа (сол жақта) '
'өзіне қарсы қозғалған қылмыстық іс бойынша өтіп жатқан...'}
```
#### deduplicated_km
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 15036,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2FGV42SN72HRKRBEEQ7QJVJBLUYQPCIH',
'warc-date': '2021-03-09T04:48:08Z',
'warc-identified-content-language': 'eng,khm,lao',
'warc-record-id': '<urn:uuid:04d772d6-09db-4d5a-86c8-22b914a35b6f>',
'warc-refers-to': '<urn:uuid:f3cdcafa-5a28-4fbb-81df-7cc5e7bb3248>',
'warc-target-uri': 'http://www.ahealthyme.com/RelatedItems/RelatedDocuments.pg?d=&TypeId=121&ContentId=761&Category=DC',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ការជូនដំណឹង៖ ប្រសិនប. ើអ្នកនិយាយភាសា ខ្មែរ សេ វាជំនួយភាសាឥតគិតថ្លៃ '
'គឺអាចរកបានសម្ រាប ់អ្នក។ សូមទូរស័ព្ទទ ៅផ ្នែ កសេ វ...'}
```
#### deduplicated_kn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8425,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TMWGSQVJMRPZCPMDM5D3AK2YKGMWBZZI',
'warc-date': '2021-03-09T04:21:39Z',
'warc-identified-content-language': 'kan,eng',
'warc-record-id': '<urn:uuid:ca35da96-ee3a-43ad-8082-a10b055200ca>',
'warc-refers-to': '<urn:uuid:a57cc8f6-c5ed-47a2-9322-2259687cdbde>',
'warc-target-uri': 'https://kannada.b4blaze.com/tag/rachitha-ram/',
'warc-type': 'conversion'},
'nb_sentences': 16,
'offset': 0},
'text': 'ಅಡಿಗರು ಮತ್ತು ರಾಯರು ಚಾಪೆ ಹಾಸಿ ಸ್ವಲ್ಪ ಹೊತ್ತು ಮಲಗಿ ಕಾಫಿ ಕುಡಿದು '
'ಹೊರಟುಹೋದಿದ್ದರು. ಜಾತ್ರೆ ದಿನ ಜಗನ್ನಾಥನ ಮನೆಗೆ ಬರಬಹುದಾದ ನೂರಾರು...'}
```
#### deduplicated_ko
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2831,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DLTUACNWU3R5KYI7HMMZF4CYR4WGRMWU',
'warc-date': '2021-02-26T10:13:10Z',
'warc-identified-content-language': 'kor,eng',
'warc-record-id': '<urn:uuid:7f7727bf-bf3d-45c3-8e3c-b595f67f9d90>',
'warc-refers-to': '<urn:uuid:17735508-d2ce-4e0a-a3ba-86acb749b9a2>',
'warc-target-uri': 'http://excel2017.zz.am/entry/mousqul',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': '인류는 최근 수백년 동안 물질적 풍요를 행복의 최대 조건으로 믿고, 이를 추구해 왔다. 그러나 이 과정에서 사람들은 '
'상대방에게 사랑을 베풀기보다는 상처를 입히는 일이 많아졌고, 물질적 풍요는 내면의 충족을 동반...'}
```
#### deduplicated_krc
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4806,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CWWWGTU7JCHS7SR5A7D7QMDTF4JBMCA6',
'warc-date': '2021-02-26T04:08:10Z',
'warc-identified-content-language': 'nno,bih',
'warc-record-id': '<urn:uuid:ef2175c0-4887-4006-9b21-374282abf2d2>',
'warc-refers-to': '<urn:uuid:d5aaef09-6f3c-427a-8c2f-664e639c2a0f>',
'warc-target-uri': 'https://krc.wikipedia.org/wiki/1606_%D0%B4%D0%B6%D1%8B%D0%BB',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Бу, тамамланмагъан статьяды. Сиз болушургъа боллукъсуз проектге, '
'тюзетиб эм информация къошуб бу статьягъа.'}
```
#### deduplicated_ku
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 12767,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:BQQEDD5HKU6LXDRIDLMWPIESOMEGIUX6',
'warc-date': '2021-03-09T04:11:10Z',
'warc-identified-content-language': 'eng',
'warc-record-id': '<urn:uuid:5a67e5e4-f688-4aa1-a9a0-2e4f6217ef21>',
'warc-refers-to': '<urn:uuid:40fa61be-18d1-4bd5-9267-252720cd5b05>',
'warc-target-uri': 'http://www.peyamakurd.org/kurmanci/Kurdistan/gruben-smo-ye-bi-hawane-li-til-rifete-xistin-3-miri-u-6-birindar',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'PeyamaKurd – Grûbên bi ser Tirkiyê de li Binxetê li bajarokê Til '
'Rifetê bi hawanê lê dan û di encamê de 3 kes mirin û...'}
```
#### deduplicated_kv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 14161,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:JH3R64H4VMXQ3NRHTX3LO3B4VFN6IZ62',
'warc-date': '2021-03-03T15:09:36Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:a94b390c-8e72-475d-bf76-c523c20908ce>',
'warc-refers-to': '<urn:uuid:e11eee46-e68f-4e1b-b4a3-0b9eeb74a877>',
'warc-target-uri': 'https://kv.wikipedia.org/wiki/%D0%9C%D0%B8%D0%BA%D1%83%D1%88%D0%B5%D0%B2_%D0%90%D0%BD%D0%B0%D1%82%D0%BE%D0%BB%D0%B8%D0%B9_%D0%9A%D0%BE%D0%BD%D1%81%D1%82%D0%B0%D0%BD%D1%82%D0%B8%D0%BD%D0%BE%D0%B2%D0%B8%D1%87',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '1947, моз тӧлысь–1950, кӧч тӧлысь – уджалiс велöдысьöн да '
'директорöн Сыктывдiн районса Ыб шöр школаын.'}
```
#### deduplicated_kw
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3496,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:S5H4MWHD4QTG74ZNJZ5X63W2XSLUJU7C',
'warc-date': '2021-02-26T18:49:31Z',
'warc-identified-content-language': 'cym',
'warc-record-id': '<urn:uuid:44d32e62-4240-413a-9f8a-562fe27223c6>',
'warc-refers-to': '<urn:uuid:7d95741c-6974-427f-80f7-d08559f799aa>',
'warc-target-uri': 'https://kw.m.wikipedia.org/wiki/Kembra',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Kembra yw konna-tir menydhek yn Howlsedhes Breten Veur. Glow hag '
'owr o poesek yn erbysieth Pow Kembra seulajydh, mes ...'}
```
#### deduplicated_ky
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 28946,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TVCYX44AC2J2TBVAYMQW62P4XYHWPSAH',
'warc-date': '2021-02-24T20:28:28Z',
'warc-identified-content-language': 'kir,eng',
'warc-record-id': '<urn:uuid:b0b897b8-5d55-4109-967f-9e368be6b7aa>',
'warc-refers-to': '<urn:uuid:b7ac5729-15cb-44c8-a0a2-096cb46cb1de>',
'warc-target-uri': 'http://mezgilnews.kg/tag/klip/',
'warc-type': 'conversion'},
'nb_sentences': 6,
'offset': 0},
'text': 'Мезгил. Ырчы Зерени соцтармактар аркылуу коркуткан белгисиз '
'адамдарды милиция издеп баштады. Чүй облустук ИИБинин маа...'}
```
#### deduplicated_la
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2647,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:QXPYMWAXXOOHWKBNAYCNUODKWSB56XU4',
'warc-date': '2021-03-09T04:51:12Z',
'warc-identified-content-language': 'lat,eng',
'warc-record-id': '<urn:uuid:684bcdce-19ec-4a44-b814-949eb5ceff66>',
'warc-refers-to': '<urn:uuid:2cd40ddd-0087-41ba-8442-8b2b6b1bbcd2>',
'warc-target-uri': 'http://grhpay.es/index.php/about-us/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Nam libero tempore, cum soluta nobis est eligendi optio cumque '
'nihil impedit quo minus id quod maxime placeat facere ...'}
```
#### deduplicated_lb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2060,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:5YXISU3T3UP7WKUDJ2W45OAKEFJ7ZD2T',
'warc-date': '2021-03-09T04:51:26Z',
'warc-identified-content-language': 'ltz',
'warc-record-id': '<urn:uuid:534e6ce8-782c-4813-9dfb-902736ffc141>',
'warc-refers-to': '<urn:uuid:5829843c-0428-4098-9213-52bb2fb319b2>',
'warc-target-uri': 'https://online-archive-extractor.com/lb/open-7z-file',
'warc-type': 'conversion'},
'nb_sentences': 4,
'offset': 0},
'text': 'Eis Online Archiv Extraiteren erlaabt Iech den Inhalt vu '
'kompriméierten Archiven direkt aus Ärem Browser ze extrahier...'}
```
#### deduplicated_lez
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6238,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:4MMTYN2QRKUOUZESCUL3AOZJTMDM5YSY',
'warc-date': '2021-03-02T18:06:44Z',
'warc-identified-content-language': 'nno,eng',
'warc-record-id': '<urn:uuid:78581b3a-c21f-46a2-b168-bff6f147c337>',
'warc-refers-to': '<urn:uuid:02f1447d-0b61-4ad5-ac56-0f42c2438e6b>',
'warc-target-uri': 'https://lez.wikipedia.org/wiki/1877_%D0%B9%D0%B8%D1%81',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '1877 йис (са агъзурни муьжуьдвишни пудкъанницIеирид лагьай йис) — '
'григорийдин чIаваргандал гьалтайла ислендиз эгечӀза...'}
```
#### deduplicated_li
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2199,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:IIZSY6KLHN5WSCCGU4NZ6K6WYLIMJP4I',
'warc-date': '2021-03-04T07:19:27Z',
'warc-identified-content-language': 'nld',
'warc-record-id': '<urn:uuid:c7eb18bb-ea03-43c2-a1e9-e8eb5b15e25b>',
'warc-refers-to': '<urn:uuid:486a5d06-6dd8-46d2-a93f-d798b8a5bd07>',
'warc-target-uri': 'https://li.m.wikipedia.org/wiki/Waterop',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': "Hoes Karsveld aan de Gulp sjtamp oet de 18e ièw. 't Kesjtièlechtig "
"hoes ies van mergel mèt 'ne trapgevel. 't Ies gebo..."}
```
#### deduplicated_lmo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6553,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DAJPSPBN7BVZNRWANXQAW2KP6LQEWNUW',
'warc-date': '2021-03-04T10:49:45Z',
'warc-identified-content-language': None,
'warc-record-id': '<urn:uuid:d9452b27-9a95-47e9-8274-518138812f56>',
'warc-refers-to': '<urn:uuid:4ff4e796-c685-4c81-adc9-fecbd50e79cb>',
'warc-target-uri': 'https://lmo.wikipedia.org/wiki/Antrenas',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': "El sò teretóre el g'ha 'na superfìce de 17,55 km² e 'l và de 'na "
"altèsa mìnima de 720 méter a 'na altèsa màsima de 11..."}
```
#### deduplicated_lo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 15036,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2FGV42SN72HRKRBEEQ7QJVJBLUYQPCIH',
'warc-date': '2021-03-09T04:48:08Z',
'warc-identified-content-language': 'eng,khm,lao',
'warc-record-id': '<urn:uuid:04d772d6-09db-4d5a-86c8-22b914a35b6f>',
'warc-refers-to': '<urn:uuid:f3cdcafa-5a28-4fbb-81df-7cc5e7bb3248>',
'warc-target-uri': 'http://www.ahealthyme.com/RelatedItems/RelatedDocuments.pg?d=&TypeId=121&ContentId=761&Category=DC',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ຂໍ້ຄວນໃສ່ໃຈ: ຖ້າເຈົ້າເວົ້າພາສາລາວໄດ້, '
'ມີການບໍລິການຊ່ວຍເຫຼືອດ້ານພາສາໃຫ້ທ່ານໂດຍບໍ່ເສຍຄ່າ. ໂທ ຫາ '
'ຝ່າຍບໍລິການສະ ມາ ຊິກທີ່...'}
```
#### deduplicated_lrc
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7958,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:GTR6WCXERTVUI5RIKHE7MC7LTACF7R2W',
'warc-date': '2021-03-01T04:48:39Z',
'warc-identified-content-language': 'fas,eng',
'warc-record-id': '<urn:uuid:7ba618e0-f09e-48c2-a0be-a1b77ba5678a>',
'warc-refers-to': '<urn:uuid:2e4504e7-46c9-4aaa-818f-3077c73f1d97>',
'warc-target-uri': 'http://www.shaya.me/2013/01/blog-post_3.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'یار یار یار یار یار یار یار یار یار یار یار یار یار یار یار یار یار '
'یار یار یار یار یار یار یار یار یار'}
```
#### deduplicated_lt
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 221005,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:KSLULK6RGSIW43IBMSAEU4643LSRMW3V',
'warc-date': '2021-03-05T07:21:10Z',
'warc-identified-content-language': 'lit',
'warc-record-id': '<urn:uuid:fa6592a5-bc87-4683-88d6-37ce74af5058>',
'warc-refers-to': '<urn:uuid:d78122b4-90d8-4cdf-a205-579bcff9ec88>',
'warc-target-uri': 'https://apcis.ktu.edu/lt/site/katalogas?cat_id=132&type=2',
'warc-type': 'conversion'},
'nb_sentences': 219,
'offset': 0},
'text': 'Telšių apskritis – viena iš Lietuvos sričių, kuri turi ką parodyti '
'pasauliui, ir iš to galima pasiekti didelės naudos...'}
```
#### deduplicated_lv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4036,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:NUB75CFJHUBI7HOED4HVCNHGQUIVCBO3',
'warc-date': '2021-03-09T03:46:31Z',
'warc-identified-content-language': 'lav,eng',
'warc-record-id': '<urn:uuid:9ad87feb-993f-45b9-bf1e-53a8185b3dc6>',
'warc-refers-to': '<urn:uuid:64eb85d8-c204-4cf8-a6c3-29760fe1f362>',
'warc-target-uri': 'http://igatesbaznica.lv/augupvrsta-stratijas-binr-opcijas.php',
'warc-type': 'conversion'},
'nb_sentences': 10,
'offset': 0},
'text': 'Latvijā šobrīd nav normatīvu aktu mājas un istabas dzīvnieku '
'vairotāju regulēšanai, jo vairākums audzētāju savu nodar...'}
```
#### deduplicated_mai
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3632,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:OQRKDLTDWJCD37HVHGXYU7E3BXBR5NB3',
'warc-date': '2021-03-01T16:25:27Z',
'warc-identified-content-language': 'bih,hin,fra',
'warc-record-id': '<urn:uuid:da0cf739-4c6c-46d4-9c32-8e34a673fa26>',
'warc-refers-to': '<urn:uuid:0c39ca75-b871-431b-8c89-63d58ea0893f>',
'warc-target-uri': 'https://mai.m.wikipedia.org/wiki/%E0%A4%B0%E0%A4%BE%E0%A4%9C%E0%A4%A7%E0%A4%BE%E0%A4%A8%E0%A5%80',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'शब्द राजधानी संस्कृत सँ आएल अछि । राजधानी आम तौर पर सङ्घटक क्षेत्रक '
'सब सँ पैग सहर होएत अछि मुदा ई जरुरी नै अछि ।[१]'}
```
#### deduplicated_mg
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2714,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:OGAHJNKN3OSLXYKJKK2LQAFKAEM67DFQ',
'warc-date': '2021-03-03T15:32:59Z',
'warc-identified-content-language': 'mlg,nno',
'warc-record-id': '<urn:uuid:f5a6492f-29c4-4de9-baaa-12edb86d89cd>',
'warc-refers-to': '<urn:uuid:970362fe-4102-481e-8f4b-db5f3e8ce4db>',
'warc-target-uri': 'https://mg.wikipedia.org/wiki/Barro_Alto_(Bahia)',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': "I Barro Alto (Bahia) dia kaominina ao Brazila, ao amin'i Bahia, ao "
"amin'i Centro-Norte Baiano, Irecê.\n"
'Ny velarantanin...'}
```
#### deduplicated_mhr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 27685,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:YJYVG5XEYRKALEYIO5PCK34QFNUO3JRD',
'warc-date': '2021-03-06T17:12:45Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:3405f528-672f-449c-a2a3-cfa73f5d17b0>',
'warc-refers-to': '<urn:uuid:dfe46be9-656c-4b02-9384-fd1e75987a15>',
'warc-target-uri': 'http://marisong.ru/mar/kalendar',
'warc-type': 'conversion'},
'nb_sentences': 31,
'offset': 0},
'text': '1982 — 1985 ийлаште — Палантай лӱмеш музыкальный училищыште баян '
'дене отделенийыште шинчымашым налын.\n'
'Тыгак шуко жап ...'}
```
#### deduplicated_min
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4309,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XV23LOBECSVNRXJ2NJTCZVJXOCVQ3BBR',
'warc-date': '2021-03-08T22:10:36Z',
'warc-identified-content-language': 'eng,spa',
'warc-record-id': '<urn:uuid:fdaddf50-1986-44b3-b84b-d9a5d0fa27f1>',
'warc-refers-to': '<urn:uuid:257f7969-3a19-42d6-ae1a-ddb5c0486bb8>',
'warc-target-uri': 'https://cookingwithmydoctor.com/?LOSS=danger-of-keto-diet%2F',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f\u200e '
'\u200e\u200f\u200f\u200e \u200e\u200f\u200f...'}
```
#### deduplicated_mk
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 22483,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:SGEJ6O6XOEVCQXKXT2XRSRBOSH3ZDSVJ',
'warc-date': '2021-03-02T05:16:16Z',
'warc-identified-content-language': 'mkd,srp,eng',
'warc-record-id': '<urn:uuid:168d1661-a73f-4687-a614-e8cecf7a70a0>',
'warc-refers-to': '<urn:uuid:a61ec44e-a4c1-4b8e-837c-7adc80e853e2>',
'warc-target-uri': 'http://zenica.mk/2018/02/10/tri-dena-kultura-vo-karev-festival/',
'warc-type': 'conversion'},
'nb_sentences': 4,
'offset': 0},
'text': '„Три дена културa“ е настан кој ќе се одржи од 21-23 февруари '
'(среда, четврток и петок, 20:00ч.) во гимназијата „Нико...'}
```
#### deduplicated_ml
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 20202,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:ZOEIO7AIEAGDR2S6TOZYZOAQDOV6QJUE',
'warc-date': '2021-03-08T00:10:05Z',
'warc-identified-content-language': 'mal,eng',
'warc-record-id': '<urn:uuid:f19a2925-0064-47e2-9ec9-48b2786657bd>',
'warc-refers-to': '<urn:uuid:20c7b8fd-1909-480f-b36c-89cd1d0ee3c4>',
'warc-target-uri': 'https://boolokam.com/what-to-do-for-police-clearance-conduct-certificate-in-uae/227247',
'warc-type': 'conversion'},
'nb_sentences': 12,
'offset': 0},
'text': 'രണ്ടുപേര്\u200d തമ്മിലുള്ള സ്നേഹ ബന്ധം അവര്\u200dക്കിടയില്\u200d '
'പൊതുവായി കാണപ്പെടുന്ന മൂല്യങ്ങളുടെ അടിസ്ഥാനത്തില്\u200d '
'ആയിരിക്കും.\n'
'ഒരുവ...'}
```
#### deduplicated_mn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 5616,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:ILMC56UA63RNTABOJTVMUJQJHMKKC6QR',
'warc-date': '2021-03-09T04:20:37Z',
'warc-identified-content-language': 'mon,ell',
'warc-record-id': '<urn:uuid:07697b69-9e58-4e84-bc0e-a536bcc1ae11>',
'warc-refers-to': '<urn:uuid:704af2f1-3094-45dc-a1c5-63bd08d53069>',
'warc-target-uri': 'http://mn.uncyclopedia.info/index.php?title=%D0%A5%D1%8D%D1%80%D1%8D%D0%B3%D0%BB%D1%8D%D0%B3%D1%87:Mongol_Emperor&action=edit',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': 'Анциклопедиа-д оруулсан бүх хувь нэмэр Creative Commons '
'Attribution-NonCommercial-ShareAlike-н хувьд (дэлгэрэнгүй мэд...'}
```
#### deduplicated_mr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 11373,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:V3PQES342QGJGRFZ6QMXNB6RIX2ST3V5',
'warc-date': '2021-03-09T05:01:31Z',
'warc-identified-content-language': 'mar,eng',
'warc-record-id': '<urn:uuid:b96cf6ee-7cda-4a7a-9364-08b51284a05e>',
'warc-refers-to': '<urn:uuid:92e533ed-c2c7-4ac7-9b17-af780a503ce6>',
'warc-target-uri': 'https://marathi.thewire.in/devangana-kalita-uapa-bail-rejected-natasha-narwal',
'warc-type': 'conversion'},
'nb_sentences': 9,
'offset': 0},
'text': 'पुण्यातील कार्यक्रमांना स्थगिती:पुण्यातील अनेक सांस्कृतिक नियोजित '
'कार्यक्रमांना स्थगिती, कोरोनाच्या वाढत्या रुग्णांमु...'}
```
#### deduplicated_mrj
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3492,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:7B242FKI45QVEGJQTF46YCRFYMYW6YFG',
'warc-date': '2021-03-03T05:03:02Z',
'warc-identified-content-language': 'eng',
'warc-record-id': '<urn:uuid:bd7d5682-be60-4a00-9781-29b03a87b30e>',
'warc-refers-to': '<urn:uuid:49641a15-2834-4a72-a011-fdc9cd7273c7>',
'warc-target-uri': 'https://mrj.wikipedia.org/wiki/%D0%91%D0%B0%D1%80%D0%BA%D0%B5%D1%80%D0%B8',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Баркери (латинлӓ Barkeria) – Орхидейвлӓ (Orchidaceae) йыхыш пырышы '
'пеледшӹ кушкыш. Америкышты вӓшлиӓлтеш. Цилӓжӹ 15 й...'}
```
#### deduplicated_ms
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7939,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:7BWXR4LQ6O2IBJLKLKWJKHTF3JBXB26T',
'warc-date': '2021-03-09T05:38:44Z',
'warc-identified-content-language': 'msa,eng',
'warc-record-id': '<urn:uuid:35a9d91c-3a64-4748-b135-3c467bfa403f>',
'warc-refers-to': '<urn:uuid:9cf4de91-0523-4327-9fcb-5c8f99956da0>',
'warc-target-uri': 'https://kheru2006.livejournal.com/1665383.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Bagaimanapun beliau memiliki satu lagi pandangan iaitu perkara '
'paling bodoh seseorang boleh lakukan ialah menjangka d...'}
```
#### deduplicated_mt
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 98714,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:HC75UY5ZHRC3AY4C2VHFR4JADUM2AZBH',
'warc-date': '2021-03-09T04:29:23Z',
'warc-identified-content-language': 'eng,mlt',
'warc-record-id': '<urn:uuid:45dec17d-a638-454e-a136-c45579517b53>',
'warc-refers-to': '<urn:uuid:c82d8d7c-05b6-43d8-be17-5072323aab01>',
'warc-target-uri': 'https://carmelcacopardo.wordpress.com/2015/07/28/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Kemmuna hi protetta bħala sit Natura 2000. Imma ma nistgħux '
'neskludu logħob tas-soltu biex iduru ma din il-protezzjon...'}
```
#### deduplicated_mwl
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 11598,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2A22BTIRZ4E5FI2FCG7AUCWJQTY2J4ST',
'warc-date': '2021-02-26T13:58:26Z',
'warc-identified-content-language': None,
'warc-record-id': '<urn:uuid:73a60756-1664-410f-bf62-ab44c88c074f>',
'warc-refers-to': '<urn:uuid:800d3642-449d-4be0-817c-edc7fb64c1b4>',
'warc-target-uri': 'https://mwl.wikipedia.org/wiki/R%C3%A1dio_(quemunica%C3%A7on)',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'La radioquemunicaçon ye un meio de quemunicaçon por trascepçon de '
'anformaçon, podendo ser rializada por Radiaçon eile...'}
```
#### deduplicated_my
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 237288,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:U2QEC6RSZR5UW5LXTNN6QRD47FHVYVJY',
'warc-date': '2021-02-27T06:07:58Z',
'warc-identified-content-language': 'mya,eng',
'warc-record-id': '<urn:uuid:817de4f8-0b7a-446e-bae2-8436019dd34f>',
'warc-refers-to': '<urn:uuid:b364cc33-c1bf-4adb-8317-1aad1cfd4aa0>',
'warc-target-uri': 'http://www.pnsjapan.org/2010/05/',
'warc-type': 'conversion'},
'nb_sentences': 248,
'offset': 0},
'text': 'စတိုင္လည္းက် စမတ္လည္းက်တဲ့ ေန႔စဥ္ လႈပ္ရွားမႈဘဝေလးေတြကို '
'ပိုင္ဆိုင္ႏိုင္ဖို႔အတြက္ Samsung ကေန မၾကာေသးခင္က ထုတ္လုပ္လိုက...'}
```
#### deduplicated_myv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 11091,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:IFCGUVXSCYHEFYLUVOQ5QMGJWYL2CTVJ',
'warc-date': '2021-03-02T21:05:00Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:ea77b8a6-e394-48c1-b865-3cea87e7b906>',
'warc-refers-to': '<urn:uuid:a4927904-4e3c-4f22-858a-adad9bbb1e63>',
'warc-target-uri': 'https://ru.m.wikinews.org/wiki/%D0%9E%D0%BC%D0%B1%D0%BE%D0%BC%D0%B0%D1%81%D1%82%D0%BE%D1%80%D1%81%D0%BE_%C2%AB%D0%90%D0%B7%D0%BE%D1%80%C2%BB_%D1%8D%D1%80%D0%B7%D1%8F%D0%BD%D1%8C_%D1%8D%D1%80%D1%8F%D0%BC%D0%B0%D1%80%D1%82%D0%BE%D0%BD%D1%82%D1%8C_%D0%B2%D0%B0%D1%81%D0%B5%D0%BD%D1%86%D0%B5_%D0%BD%D0%B5%D0%B2%D1%82%D0%B5%D0%BC%D0%B0%D1%81%D1%8C_%D1%8E%D1%82%D1%8B_%D0%A1%D1%83%D0%BE%D0%BC%D0%B8%D1%81%D1%81%D1%8D',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '«Азор» — васенце эрзянь кельсэ артонь эриванмо-фильманть теемстэ. '
'Орданьбуень Баеньбуе веле, Мордовиясо.'}
```
#### deduplicated_mzn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6193,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:QVLHP3APVA34EQ4YFDRJWF2ODTQZ3QG6',
'warc-date': '2021-03-08T00:11:58Z',
'warc-identified-content-language': 'fas',
'warc-record-id': '<urn:uuid:c86dfe2b-795d-4e5d-aaa0-75c1e98690a6>',
'warc-refers-to': '<urn:uuid:b6258701-626d-4a7c-b79e-1c526f9892a6>',
'warc-target-uri': 'https://mzn.wikipedia.org/wiki/%D8%A7%D9%88%D8%B3%D9%88%DA%A9%DB%8C%D8%8C_%D8%A7%D9%88%D8%A6%DB%8C%D8%AA%D8%A7',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'اوسوکی اتا شهر نوم هسته که جاپون ِاوئیتا استان دله دره. ونه جمعیت '
'ره سال ۲۰۰۸ گادِر ۴۲٬۴۶۴ نفر اعلام هاکاردنه. این شه...'}
```
#### deduplicated_nah
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2517,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DSXC3C7F2LUL47USAV5ZRT4HMVQ4XGUI',
'warc-date': '2021-03-03T14:32:16Z',
'warc-identified-content-language': 'spa,ell',
'warc-record-id': '<urn:uuid:a305013e-01ba-49a3-89b9-027dc622576f>',
'warc-refers-to': '<urn:uuid:073b9e5a-a0d3-41c3-89bd-8f972b6a4154>',
'warc-target-uri': 'https://nah.wikipedia.org/wiki/%CF%98',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Ϙ ītōcā inic cē huēhuehtlahtōl īpan '
'greciamachiyōtlahtōltecpantiliztli. Ītlahtōl nō ic 90 tlapōhualli.'}
```
#### deduplicated_nap
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2331,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:EXGUINJCGD2K4E2IVQNJJAQLS4UDJ2TG',
'warc-date': '2021-03-07T13:12:47Z',
'warc-identified-content-language': 'cos,srp,lav',
'warc-record-id': '<urn:uuid:7362689d-31bc-492d-8e60-851c963b5313>',
'warc-refers-to': '<urn:uuid:ecd1bb5f-d247-4739-b9e9-4f93d46081d6>',
'warc-target-uri': 'https://nap.wikipedia.org/wiki/Priatorio',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': "'Int'ô cattolicesimo, priatorio è 'o pruciesso 'e purefecazzione 'e "
"ll'aneme ca moreno 'into ll'amicizzia 'e Dio ma n..."}
```
#### deduplicated_nds
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 5066,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:G2O2EJZLTIU5IDSXMYHPP3TMXVXMAZ3P',
'warc-date': '2021-03-08T22:13:48Z',
'warc-identified-content-language': 'nno,srp',
'warc-record-id': '<urn:uuid:d7f0c9a0-9c12-4d9a-ae5a-184bf7b59c5d>',
'warc-refers-to': '<urn:uuid:31f4d793-f3a4-4403-9c1f-a52f878b63c8>',
'warc-target-uri': 'https://nds.wikipedia.org/wiki/1763',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '7. Oktober: In London geiht en königliche Proklamatschoon rut, dat '
'vun nu af an in de Kolonien vun Amerika de Kamm vu...'}
```
#### deduplicated_ne
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 17723,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:AZ2CUDZ672TVV2R3O643TJAX7JGXASP2',
'warc-date': '2021-03-08T22:24:08Z',
'warc-identified-content-language': 'nep',
'warc-record-id': '<urn:uuid:fa642413-904a-4def-86fc-a4889e5e9e71>',
'warc-refers-to': '<urn:uuid:f7caed4f-c5ae-4f55-944a-1f06ed71e438>',
'warc-target-uri': 'https://postpati.com/2017/26/07/1353',
'warc-type': 'conversion'},
'nb_sentences': 9,
'offset': 0},
'text': 'युएइको दूतावास बिरुद्द युएइमा रहेका संघ संस्थाहरु द्वारा निरन्तर '
'दवाव आउने क्रमजारि रहेको छ। नेकपा माओबादी सम्बद्ध रह...'}
```
#### deduplicated_new
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2388,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:E6YZSKQK57PDBRG7VPE64CGOL3N4D63I',
'warc-date': '2021-03-09T04:24:48Z',
'warc-identified-content-language': 'nep,eng,bih',
'warc-record-id': '<urn:uuid:20692995-9d67-4b05-ba9b-9dbac80b4441>',
'warc-refers-to': '<urn:uuid:a8445a70-117a-42c1-89ca-aa5df0cc5616>',
'warc-target-uri': 'https://new.wikipedia.org/wiki/%E0%A4%A7%E0%A4%BE%E0%A4%AA%E0%A4%BE',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'धापा (अंग्रेजी भाय:Dhapa), नेपायागु कर्णाली अञ्चलयागु जुम्ला '
'जिल्लायागु गाँ विकास समिति खः। थ्व थासे231खा छेँ दु।'}
```
#### deduplicated_nl
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 766978,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:77YAXN3F4IGI2CYBM3IESJRTCIB4WY2F',
'warc-date': '2021-02-25T16:49:18Z',
'warc-identified-content-language': 'nld',
'warc-record-id': '<urn:uuid:0b08e51a-1b82-4fb9-a420-8556f2fb47a3>',
'warc-refers-to': '<urn:uuid:dae7ca23-9b7e-45d1-9a1c-604942af8cb9>',
'warc-target-uri': 'https://www.delpher.nl/nl/tijdschriften/view?identifier=MMUBA13:001691001:00689&coll=dts',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '1 Deze Duitse hond is nauw verwant aan de Duitse Brak, de '
'Westfaalse Dasbrak werd gefokt om op dieren te jagen, zoals...'}
```
#### deduplicated_nn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2770,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FLRYPK225URFXO3IG4LP6D5TI2WW7MNU',
'warc-date': '2021-03-09T03:50:05Z',
'warc-identified-content-language': 'nno',
'warc-record-id': '<urn:uuid:de821d19-abed-4a35-9284-91176a5428b9>',
'warc-refers-to': '<urn:uuid:7ed9913e-e7dd-496f-b0ef-e82098dd53ca>',
'warc-target-uri': 'https://www.avisa-hordaland.no/trafikk/tunell-pa-e16-stengd-2/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Bilføraren som vart stogga på E16 i helga hadde 2,28 i promille: – '
'Han var ikkje i stand til å ta vare på seg sjølv'}
```
#### deduplicated_no
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1329,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:G7JC2T5AD4YK4WWFGTYHHGP5VHB6M7KU',
'warc-date': '2021-03-08T13:17:52Z',
'warc-identified-content-language': 'nor',
'warc-record-id': '<urn:uuid:9e215de3-f988-4754-9ef5-6370121b9b5e>',
'warc-refers-to': '<urn:uuid:1facfcb5-da68-4122-9257-102271944050>',
'warc-target-uri': 'https://www.miljoindex.no/781825/nexans-norway-hovedkontor/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Utvikling, produksjon og markedsføring av kabler og '
'kablingssystemer, samt annen tilknyttet virksomhet, herunder del...'}
```
#### deduplicated_oc
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 20117,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2XDHRCL2CSS7YFAM2IAGQL6CSJJEQDXI',
'warc-date': '2021-03-03T15:40:21Z',
'warc-identified-content-language': 'oci',
'warc-record-id': '<urn:uuid:c9ebdec5-af68-4756-88c8-1df831621c5b>',
'warc-refers-to': '<urn:uuid:199db451-0e6f-4f75-ad81-2e7612295452>',
'warc-target-uri': 'https://oc.wikipedia.org/wiki/2',
'warc-type': 'conversion'},
'nb_sentences': 18,
'offset': 0},
'text': "8 : dins l'Empèri Part, assassinat dau rèi Orodes III, probablament "
'en causa de son autoritarisme, que foguèt remplaç...'}
```
#### deduplicated_or
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 12859,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:KQDIT6NHKBV43F56DTHTM5ZS3GHJT5SY',
'warc-date': '2021-03-09T05:25:21Z',
'warc-identified-content-language': 'ori,eng',
'warc-record-id': '<urn:uuid:e25e33da-92c5-42d6-aef8-c3465855312a>',
'warc-refers-to': '<urn:uuid:7457ac60-4aae-44ad-aaec-314795ea0708>',
'warc-target-uri': 'https://or.wikipedia.org/wiki/%E0%AC%A6%E0%AD%8D%E0%AD%B1%E0%AC%BF%E0%AC%A4%E0%AD%80%E0%AD%9F_%E0%AC%AC%E0%AC%BF%E0%AC%B6%E0%AD%8D%E0%AD%B1%E0%AC%AF%E0%AD%81%E0%AC%A6%E0%AD%8D%E0%AC%A7',
'warc-type': 'conversion'},
'nb_sentences': 3,
'offset': 0},
'text': 'ଇଉରୋପ, ପ୍ରଶାନ୍ତ ମହାସାଗର, ଆଟଲାଣ୍ଟିକ ମହାସାଗର, ଦକ୍ଷିଣ-ପୂର୍ବ ଏସିଆ, ଚୀନ, '
'ମଧ୍ୟପ୍ରାଚ୍ୟ, ଭୂମଧ୍ୟସାଗର, ଉତ୍ତର ଆଫ୍ରିକା, ପୂର୍ବ ଆଫ୍...'}
```
#### deduplicated_os
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7079,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:N7CKDF6E3SJBINW4SR6LIUNKLIJP2ROL',
'warc-date': '2021-03-08T22:01:32Z',
'warc-identified-content-language': 'nno',
'warc-record-id': '<urn:uuid:4cd86a68-815b-4539-84a8-bab850034e60>',
'warc-refers-to': '<urn:uuid:8774fb5e-b7fb-4feb-85e7-8c7b33f5980b>',
'warc-target-uri': 'https://os.wikipedia.org/wiki/%D0%9F%D1%83%D1%88%D0%BA%D0%B8%D0%BD,_%D0%A1%D0%B5%D1%80%D0%B3%D0%B5%D0%B9%D1%8B_%D1%84%D1%8B%D1%80%D1%82_%D0%90%D0%BB%D0%B5%D0%BA%D1%81%D0%B0%D0%BD%D0%B4%D1%80',
'warc-type': 'conversion'},
'nb_sentences': 4,
'offset': 0},
'text': 'Пушкин Александр Сергейы фырт (уырыс. Александр Сергеевич Пушкин; '
'райгуырдис 1799 азы 6 июны Мæскуыйы — амардис 1837 ...'}
```
#### deduplicated_pa
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3990,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:HBYN5XY3CD2KI4XIWMBJYPSV2ZPNBWUN',
'warc-date': '2021-03-09T05:05:20Z',
'warc-identified-content-language': 'pan,eng',
'warc-record-id': '<urn:uuid:1ac5c8d1-e750-492e-b35e-b9780bfd16fd>',
'warc-refers-to': '<urn:uuid:b4d8f997-8c9a-43cf-b16c-e8a77c209062>',
'warc-target-uri': 'https://pa.nhp.gov.in/Detail/getdirection?url=radha-krishna-nurshing-andmat-home-rae_bareli-uttar_pradesh',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ਇਹ ਪੋਰਟਲ ਰਾਸ਼ਟਰੀ ਸਿਹਤ ਪੋਰਟਲ ਦੇ ਸਿਹਤ ਸੂਚਨਾ ਕੇਂਦਰ (CHI) ਦੁਆਰਾ ਵਿਕਸਿਤ '
'ਤੇ ਤਿਆਰ ਕੀਤਾ ਗਿਆ ਹੈ ਅਤੇ ਸਿਹਤ ਤੇ ਪਰਿਵਾਰ ਭਲਾਈ ਮੰਤਰਾਲੇ...'}
```
#### deduplicated_pam
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4615,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:WOAFTI75LXN3LAF6WFDRDHITPU33CZRK',
'warc-date': '2021-03-07T22:02:39Z',
'warc-identified-content-language': 'eng',
'warc-record-id': '<urn:uuid:9d7a202a-0fec-4aac-9921-2ebf5aa7f9a2>',
'warc-refers-to': '<urn:uuid:70b6a707-77b1-4a0f-84e6-d75ed8d729ad>',
'warc-target-uri': 'https://toddlers.me/kpai-sarankan-gading-beri-penguatan-psikologi-untuk-gempi/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '“Káláu Gádìng tìdák mámpu melákukán ìtu, yá bìsá mìntá tolong '
'kepádá oráng yáng berkompeten, mìsálnyá psìkolog átáu s...'}
```
#### deduplicated_pl
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 51849,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:25YENUTK4YA3ZYGCWQH5Z6YDINCMI6SI',
'warc-date': '2021-03-05T22:43:01Z',
'warc-identified-content-language': 'pol',
'warc-record-id': '<urn:uuid:753116b6-f680-448d-ae8a-8fc88ce061b1>',
'warc-refers-to': '<urn:uuid:926693c4-5b59-4f50-98b9-787576fc71d7>',
'warc-target-uri': 'https://igraszki-jezykowe.pl/category/tips-and-tricks-metodyka/',
'warc-type': 'conversion'},
'nb_sentences': 60,
'offset': 0},
'text': 'W niedzielę, 12 czerwca w Orlando na Florydzie islamski terrorysta, '
'powiązany z ISIS zastrzelił 50 osób i drugie tyle...'}
```
#### deduplicated_pms
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2620,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2T5H5XDLC3KPDB33XXVCTGNNYYDJXQWQ',
'warc-date': '2021-03-03T16:04:55Z',
'warc-identified-content-language': 'srp',
'warc-record-id': '<urn:uuid:952c2dda-041e-40ff-bf28-8a39075f53d9>',
'warc-refers-to': '<urn:uuid:6d526022-b736-4a51-9b9c-c5bdd5a546f9>',
'warc-target-uri': 'https://pms.wikipedia.org/wiki/Auer',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': "Auer (Ora për j'italian) a l'é un comun ëd 3.025 abitant dla "
'provincia ëd Bolsan (Region Autònoma Trentin-Sud Tiròl)....'}
```
#### deduplicated_pnb
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2896,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:GWWDSJAQDB7JDQWV65CI6WT7E6C33DL4',
'warc-date': '2021-03-08T23:01:08Z',
'warc-identified-content-language': 'urd',
'warc-record-id': '<urn:uuid:8c385ca8-7561-4f47-b5a3-0862488eb948>',
'warc-refers-to': '<urn:uuid:837d621d-3540-44fd-a4d0-6cb3c6f2327f>',
'warc-target-uri': 'https://pnb.wikipedia.org/wiki/453%DA%BE',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'لکھت کریئیٹیو کامنز انتساب/ اکوجہے-شراکت لائسنس دے ہیٹھ دستیاب اے، '
'ہور شرطاں وی لاگو ہوسکدیاں نیں۔ ویروے لئی ورتن شرط...'}
```
#### deduplicated_ps
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2424,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CAUU5Y7TOTASV7WYKCYRCVXTZ7GGN2VO',
'warc-date': '2021-03-09T05:08:35Z',
'warc-identified-content-language': 'pus',
'warc-record-id': '<urn:uuid:d784cf7a-91e1-4c54-96a2-e41c67318548>',
'warc-refers-to': '<urn:uuid:98aed7d2-c3e3-4039-af83-f2c73a5c19f5>',
'warc-target-uri': 'https://www.mashaalradio.com/a/29821043.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'د افغانستان په فاریاب ولایت کې په یوه پارک کې ښځو په برقعو کې ورزش '
'کړی دی. د سیمې چارواکي وايي، د ښځو د ورزش لپاره ځا...'}
```
#### deduplicated_pt
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 79931,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:JYDP4XMEGW2XPPV6NAAF772KDH4X2CCF',
'warc-date': '2021-02-25T13:48:41Z',
'warc-identified-content-language': 'por',
'warc-record-id': '<urn:uuid:3b50f546-e03b-461f-98c8-5a38920d7c0a>',
'warc-refers-to': '<urn:uuid:564bfb21-0705-4997-bbb9-472f0cbcad3e>',
'warc-target-uri': 'http://www.artefazparte.com/',
'warc-type': 'conversion'},
'nb_sentences': 117,
'offset': 0},
'text': 'A reflexão sobre identidade de género anda a cansar muitos de nós. '
'Sobretudo os que não têm dúvidas e nela se sentem ...'}
```
#### deduplicated_qu
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2630,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:34TX2UNXR2JLRLAFTE3ILOBMEBRMWIRH',
'warc-date': '2021-03-09T05:23:48Z',
'warc-identified-content-language': 'que',
'warc-record-id': '<urn:uuid:237398f6-a300-449b-9e09-7a1ed8cf1e97>',
'warc-refers-to': '<urn:uuid:84b20aab-d538-4efc-bc97-33d546d84802>',
'warc-target-uri': 'https://qu.wikipedia.org/wiki/Sapaq:HukchasqaTinkimuq/Chinchay_Chungcheong_pruwinsya',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': "Kay sapaq p'anqaqa t'inkisqa p'anqakunapi ñaqha hukchasqakunatam "
"rikuchin. Watiqasqayki p'anqakunaqa yanasapa qillqas..."}
```
#### deduplicated_rm
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 100558,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:Z7R6QV2K5FDIHR4QJH7F2NTXND6NDEFY',
'warc-date': '2021-02-27T13:53:32Z',
'warc-identified-content-language': 'deu',
'warc-record-id': '<urn:uuid:da3aec28-6c61-470c-a5d2-66710bc1fb35>',
'warc-refers-to': '<urn:uuid:9d04f371-89a7-4ac2-9b1e-883aa93e4ace>',
'warc-target-uri': 'http://lexbrowser.provinz.bz.it/doc/la/lp-2009-5/lege_provinzialadi_28_de_set_mber_dl_2009_n_5.aspx?view=1',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '(2) La prestaziun dla garanzia é sotmetüda al’aprovaziun di decunć '
'finanziars da pert dl’aministraziun dl consorz.'}
```
#### deduplicated_ro
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1677,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DXKBGKXVETQLCHTHRMLLSWUPXTDNJDVV',
'warc-date': '2021-02-26T12:19:49Z',
'warc-identified-content-language': 'ron',
'warc-record-id': '<urn:uuid:2c20c06f-ca98-4118-9222-7b3b74bc760b>',
'warc-refers-to': '<urn:uuid:e77c028a-5857-4ec2-90db-58a9bb57c510>',
'warc-target-uri': 'https://ro.visafoto.com/es-visa-photo',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Căluşarii sau Boristenii, melodie culeasă din Braşov, în 1832, de '
'Canzler cav. de Ferio şi publicată târziu de Otto H...'}
```
#### deduplicated_ru
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 14025,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2HSXIFOHEJZOTJV2EVDSZDVF26ATVATE',
'warc-date': '2021-03-07T02:45:16Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:aa9b3fc9-fb66-45fa-a064-62ae5fd67970>',
'warc-refers-to': '<urn:uuid:e9145f1e-4ce5-44db-a7d7-234842b31973>',
'warc-target-uri': 'http://budzdorov-kaluga.ru/statyi_i_materialy/o-grippe',
'warc-type': 'conversion'},
'nb_sentences': 15,
'offset': 0},
'text': '«Геро́й» (кит. 英雄) — исторический фильм режиссёра Чжана Имоу, '
'снятый в 2002 году. Продолжительность — 93 минуты (суще...'}
```
#### deduplicated_rue
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 17472,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:YBMO2PR3WF7WQ7UEU5YLRBI7BZ6IP6KB',
'warc-date': '2021-03-06T15:24:27Z',
'warc-identified-content-language': 'ukr,rus',
'warc-record-id': '<urn:uuid:ca71a8fe-adb9-4346-a5b4-7d283f1410f8>',
'warc-refers-to': '<urn:uuid:a609d9f9-5040-4ca5-80a8-aa2c4c7a3525>',
'warc-target-uri': 'https://rue.wikipedia.org/wiki/%D0%9F%D0%BE%D0%BC%D1%96%D1%87:%D0%9A%D0%B0%D1%82%D0%B5%D2%91%D0%BE%D1%80%D1%96%D1%97',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Наприклад можете едітовати Катеґорія:Фізіци і додати одказ '
'[[Катеґорія:Фізіка]]. Катеґорія Фізіци буде пікатеґоріёв к...'}
```
#### deduplicated_sa
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 4166,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:ACZ66HH67HYSPS6I7YYQX64HRD4O5GIH',
'warc-date': '2021-02-24T20:35:30Z',
'warc-identified-content-language': 'san,eng',
'warc-record-id': '<urn:uuid:12bc2393-cb9b-492d-9398-f6b1090bd999>',
'warc-refers-to': '<urn:uuid:6e883bd6-350e-4280-94dc-ee84f44d2458>',
'warc-target-uri': 'https://sa.wikipedia.org/wiki/%E0%A4%B5%E0%A4%BF%E0%A4%B6%E0%A5%87%E0%A4%B7%E0%A4%83:%E0%A4%95%E0%A4%BF%E0%A4%AE%E0%A4%A4%E0%A5%8D%E0%A4%B0_%E0%A4%B8%E0%A4%81%E0%A4%B2%E0%A5%8D%E0%A4%B2%E0%A4%97%E0%A5%8D%E0%A4%A8%E0%A4%AE%E0%A5%8D/%E0%A4%B5%E0%A4%B0%E0%A5%8D%E0%A4%97%E0%A4%83:%E0%A5%A9%E0%A5%AC%E0%A5%A7',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'केभ्यः पृष्ठेभ्यः सम्बद्धम् पृष्ठम्: नामाकाशः : सर्वाणि (मुख्यम्) '
'सम्भाषणम् सदस्यः सदस्यसम्भाषणम् विकिपीडिया विकिपीडि...'}
```
#### deduplicated_sah
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1724,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:5PKOMLENZCNOU6PT27NCNKTQFPRC37RQ',
'warc-date': '2021-03-03T15:19:03Z',
'warc-identified-content-language': 'ukr,rus',
'warc-record-id': '<urn:uuid:59b7bbeb-e375-4d8c-8b7c-fbe09e5ce21e>',
'warc-refers-to': '<urn:uuid:512d4df0-bd91-47aa-8f23-eb2a8d4b426e>',
'warc-target-uri': 'https://sah.m.wikipedia.org/wiki/%D0%A7%D0%B5%D1%80%D0%BD%D0%B8%D0%B3%D0%BE%D0%B2_%D1%83%D0%BE%D0%B1%D0%B0%D0%BB%D0%B0%D2%BB%D0%B0',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Тиэкис Creative Commons Attribution-ShareAlike лиссиэнсийэ '
'усулуобуйатынан тарҕанар, сорох түбэлтэҕэ эбии көрдөбүллэр...'}
```
#### deduplicated_scn
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3622,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VGCXGU3B2WY722G2LRJ56RSYT4HSLUGI',
'warc-date': '2021-03-03T02:35:42Z',
'warc-identified-content-language': 'cos,ita',
'warc-record-id': '<urn:uuid:caeb7ba3-1bc2-4ef7-95cb-eb0d4d0792d6>',
'warc-refers-to': '<urn:uuid:19e33395-5981-4f6d-857b-12cf7d761b58>',
'warc-target-uri': 'https://scn.wikipedia.org/wiki/Canali_d%C3%A2_M%C3%A0nica',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Lu ripartu francisi dâ Mànica, chi cumprenni la pinìsula dû '
'Cotentin, chi si nesci ntô canali, pigghia lu sò nomu dû ...'}
```
#### deduplicated_sco
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 140370,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TRXAEE4XHP7FT4FCJF3DSEKD7YBPCFOR',
'warc-date': '2021-03-02T07:33:12Z',
'warc-identified-content-language': 'eng,vol',
'warc-record-id': '<urn:uuid:d406a6c9-dba6-4955-8ede-f8082f7da58f>',
'warc-refers-to': '<urn:uuid:155919e0-a689-415c-b2aa-eccd06021476>',
'warc-target-uri': 'https://baggato.com/fo',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'fowjo fowjp fowjq fowjr fowka fowkb fowkc fowkd fowke fowkf fowkg '
'fowkh fowki fowkj fowkk fowkl fowkm fowkn fowko fow...'}
```
#### deduplicated_sd
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 17619,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DLWVP7WGNP64RB6ZLHDNQEJ7D24BYXOR',
'warc-date': '2021-02-24T20:04:37Z',
'warc-identified-content-language': 'snd,eng',
'warc-record-id': '<urn:uuid:8997e1c6-4d72-47f1-bffe-d18a00ae6b94>',
'warc-refers-to': '<urn:uuid:946e892e-46c3-4a68-8532-1eac8b65b76a>',
'warc-target-uri': 'https://sd.info-4all.ru/%D8%B1%D8%AA%D9%88%D9%BD%D9%88-%D8%A2%D8%A6%D9%8A%D8%B1%D8%B1%D8%A7/%DA%AA%D9%84%D8%A7%DA%AA/',
'warc-type': 'conversion'},
'nb_sentences': 21,
'offset': 0},
'text': 'بيلففيل ڪيئن ٿيو؟ پهرين توهان کي پنهنجو ضمير وڃائڻ جي ضرورت آهي. '
'اهي تعليم کان سواءِ صرف سست ماڻهو نه وٺندا آهن ، پر ...'}
```
#### deduplicated_sh
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 12517,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:IH6O64JAV4PLXURRD5LKU6C46DGGXS27',
'warc-date': '2021-03-09T06:06:53Z',
'warc-identified-content-language': 'fra,hrv,eng',
'warc-record-id': '<urn:uuid:ddc0f982-aea2-4206-a431-02e6c89ab090>',
'warc-refers-to': '<urn:uuid:904a206d-515a-4f11-ad25-9035adbf0cfa>',
'warc-target-uri': 'https://sh.wikipedia.org/wiki/Cliponville',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Po podacima iz 1999. godine u opštini je živelo 245 stanovnika, a '
'gustina naseljenosti je iznosila 33 stanovnika/km²....'}
```
#### deduplicated_si
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 18426,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CZO426HASJ2VV5IMXEAHY2T53ZTDOZEP',
'warc-date': '2021-02-24T20:38:23Z',
'warc-identified-content-language': 'sin,eng',
'warc-record-id': '<urn:uuid:bec8b1fe-0659-4f47-b244-018b5dac9e30>',
'warc-refers-to': '<urn:uuid:1c918e04-8c2d-4bc0-bcfb-bf978ab0c0ea>',
'warc-target-uri': 'https://androidwedakarayo.com/before-you-look-for-a-job-please-fix-your-facebook-account/',
'warc-type': 'conversion'},
'nb_sentences': 19,
'offset': 0},
'text': 'ඉස්සර තමයි අපි සෝෂල්මීඩියා පාවිච්චි කරන්නේ අපි ආස නළු නිළියන්ගේ '
'ෆොටෝ, හදපු කෑම, ඩ්\u200dරින්ක් එකක් දාන්න සෙට් වෙච්චි වෙලා...'}
```
#### deduplicated_sk
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 37910,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:ODXVMZXR34B45NQTMJIKKK2VGBGRXKEA',
'warc-date': '2021-03-01T16:29:19Z',
'warc-identified-content-language': 'slk',
'warc-record-id': '<urn:uuid:6a22612f-9bbf-4f74-8cca-0457f069baa4>',
'warc-refers-to': '<urn:uuid:3981cb48-fadf-463f-9fc9-a6d717b9dc71>',
'warc-target-uri': 'http://www.tomsta.sk/',
'warc-type': 'conversion'},
'nb_sentences': 56,
'offset': 0},
'text': 'Keďže všade naokolo sú iba kopce, mohol byť jedine horský. Dnes je '
'z toho najlepší horský triatlon na Slovensku, ktor...'}
```
#### deduplicated_sl
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8130,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:UFZ4P4LVU4TXYJIHZULTCIVJ4GA3JT54',
'warc-date': '2021-03-07T14:50:23Z',
'warc-identified-content-language': 'slv,eng',
'warc-record-id': '<urn:uuid:e50a528d-ebd3-46dc-92d7-af394aaa896a>',
'warc-refers-to': '<urn:uuid:dbfe8ac4-b415-45a8-a16c-c168ed5ce37b>',
'warc-target-uri': 'https://www.edi-nm.com/si/varicosen-mnenja-cena-lekarna/',
'warc-type': 'conversion'},
'nb_sentences': 6,
'offset': 0},
'text': 'Po najnovejših raziskavah v Sloveniji vsaka 4. oseba med 36. in 95. '
'letom trpi zaradi kronične venske insuficience – ...'}
```
#### deduplicated_so
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 17837,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:WIS4GECYGJYMTZMVFOUVUMRWTAPFZUSK',
'warc-date': '2021-03-03T20:11:46Z',
'warc-identified-content-language': 'bul,eng,srp',
'warc-record-id': '<urn:uuid:976de977-97b9-4517-8a42-2fc82fdda461>',
'warc-refers-to': '<urn:uuid:a0f1fbd0-b2cb-495f-93f3-53e77acae3f5>',
'warc-target-uri': 'https://studioqueens.bgnick.info/l4fOorCpgdutsnY/igra-na.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'ххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххххх...'}
```
#### deduplicated_sq
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6129,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:D3PWGEKLJKJEGOTQLYVQNUV4URWEFH2P',
'warc-date': '2021-03-09T03:17:23Z',
'warc-identified-content-language': 'sqi',
'warc-record-id': '<urn:uuid:3299bc56-c7fb-4655-bebd-393510d89aaa>',
'warc-refers-to': '<urn:uuid:1416a2ad-d319-4c60-b663-29239ff79154>',
'warc-target-uri': 'http://ata.gov.al/2019/11/03/video-u-prek-nga-termeti-ndertohet-nga-e-para-banesa-e-familjes-stafa-ne-petrele/',
'warc-type': 'conversion'},
'nb_sentences': 11,
'offset': 0},
'text': 'TIRANË, 3 nëntor/ATSH/- Në Petrelë të Tiranës ka nisur puna për '
'ndërtimin nga e para të shtëpisë së familjes Stafa, e...'}
```
#### deduplicated_sr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7735,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:7LKRS7R2L2K53YTV5CYR2IAJRNIQKGBJ',
'warc-date': '2021-03-03T11:23:25Z',
'warc-identified-content-language': 'srp,eng',
'warc-record-id': '<urn:uuid:8ade8406-bedb-41a7-b854-8429b6b21214>',
'warc-refers-to': '<urn:uuid:cca5c75c-7221-4247-a51e-f7be99661793>',
'warc-target-uri': 'https://vojvodjanske.rs/40-jubilarni-somborski-polumaraton-u-nedelju-19-maja/',
'warc-type': 'conversion'},
'nb_sentences': 4,
'offset': 0},
'text': '„У недељу 19. маја, у Сомбору се одржава јубиларна 40. најстарија '
'улична трка у Републици Србији, Сомборски полумарат...'}
```
#### deduplicated_su
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 14013,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:IMFFV646FPXSYLMOATX7O6CDMKUU4BFL',
'warc-date': '2021-03-09T10:29:19Z',
'warc-identified-content-language': 'sun,ind',
'warc-record-id': '<urn:uuid:02eb1f6f-7040-4b8f-b995-7c547196da4b>',
'warc-refers-to': '<urn:uuid:4a9807f7-0c98-493f-ab84-8fafc61a1e50>',
'warc-target-uri': 'https://www.masdinko.com/2019/04/soal-utspts-bahasa-sunda-sd-kelas-4.html',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Pikeun urang lembur, daun seureuh téh geus teu anéh deui. Seureuh '
'mah mangrupa tangkal nu ngarémbét kana tangkal séjéna.'}
```
#### deduplicated_sv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 87099,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:TKLP6CG56M45ABZQGDD7EDTCQMKTSAVS',
'warc-date': '2021-03-05T20:01:45Z',
'warc-identified-content-language': 'swe',
'warc-record-id': '<urn:uuid:97860695-1688-46ef-93db-5e15742820af>',
'warc-refers-to': '<urn:uuid:7c924b0e-39e1-4921-a561-52dc5453b886>',
'warc-target-uri': 'https://fortretligheter.blogspot.com/2011/01/',
'warc-type': 'conversion'},
'nb_sentences': 255,
'offset': 0},
'text': 'Svenska trupper hade en kväll för flera hundra år sedan när Sverige '
'och Danmark låg i Krig med varandra kommit med sk...'}
```
#### deduplicated_sw
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 2098,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FPGJP34F47FJQSZF62PELBLYNJ4RTCSE',
'warc-date': '2021-03-03T15:24:39Z',
'warc-identified-content-language': 'swa',
'warc-record-id': '<urn:uuid:d42018de-64be-41f9-b4b6-700dd0051ce3>',
'warc-refers-to': '<urn:uuid:a40c8328-ab33-4113-9ea1-8c35967b0bde>',
'warc-target-uri': 'http://mwanza.go.tz/videos/78',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Mkuu wa Mkoa wa Mwanza Mhe.John Mongella akifungua Baraza la '
'biashara katika kikao kilichofanyika kwenye ukumbi wa mk...'}
```
#### deduplicated_ta
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 49341,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FQEPDKJ7AYCAEVL5SRUQ5QOULOOSHECD',
'warc-date': '2021-03-09T04:15:52Z',
'warc-identified-content-language': 'tam',
'warc-record-id': '<urn:uuid:2fa70e6a-a31a-4359-b4ff-54ce7f5d6200>',
'warc-refers-to': '<urn:uuid:92eb01ff-4f82-438b-8d1f-1722fe23285a>',
'warc-target-uri': 'https://thiru2050.blogspot.com/2019_05_26_archive.html',
'warc-type': 'conversion'},
'nb_sentences': 15,
'offset': 0},
'text': '... 2017 adimmix psychic leah அறிவுரை கும்பம் மேஷம் ஜோதிடம் '
'புற்றுநோய் மகர படிக குழந்தைகள் மனநோய் புத்தகங்கள் முன்அ...'}
```
#### deduplicated_te
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 31516,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:MG3MFYW5T6XSW3XYZ4ZIKGJW5XAY2RCG',
'warc-date': '2021-03-06T18:07:45Z',
'warc-identified-content-language': 'tel',
'warc-record-id': '<urn:uuid:238b108b-d16e-41d2-b06e-464267352b0e>',
'warc-refers-to': '<urn:uuid:3663318c-d256-4c97-b71b-e4eeb2e6b58a>',
'warc-target-uri': 'https://telugu.greatandhra.com/articles/mbs/ammo-ativa-01-114908.html',
'warc-type': 'conversion'},
'nb_sentences': 15,
'offset': 0},
'text': 'అది 1868. ఇంగ్లండ్\u200cలోని బ్రైటన్\u200cలో క్రిస్టియానా ఎడ్మండ్స్ '
'అనే 40 ఏళ్ల మహిళ వుండేది. పెళ్లి కాలేదు. తల్లితో కలిసి ఒక ఎ...'}
```
#### deduplicated_tg
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 16112,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:LDBVTK3U6MY7J475ZR4LRLFK2CC2QWG5',
'warc-date': '2021-03-09T03:53:03Z',
'warc-identified-content-language': 'tgk,tat,rus',
'warc-record-id': '<urn:uuid:b2519476-6812-4a38-8522-f5292b95e73a>',
'warc-refers-to': '<urn:uuid:f11fa878-d4c6-4e56-bc50-a76554b7d811>',
'warc-target-uri': 'http://hamsafon.tj/2784-imr1263z-1203avoi-1207um1203ur1251-sofu-be1171ubor-meshavad.html',
'warc-type': 'conversion'},
'nb_sentences': 15,
'offset': 0},
'text': 'ДУШАНБЕ, 10.01.2017/АМИТ «Ховар»/. 10 январ дар пойтахти кишвар '
'ҳавои тағйирёбандаи бебориш дар назар дошта шудааст. ...'}
```
#### deduplicated_th
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 50841,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:MESEMAONUQXZZEA6IKBT3VCUZ43ZP4B7',
'warc-date': '2021-02-28T15:41:47Z',
'warc-identified-content-language': 'tha,eng',
'warc-record-id': '<urn:uuid:46495e6b-f22f-4dc6-86ab-3bbed66ce7e4>',
'warc-refers-to': '<urn:uuid:10946c1b-9dc5-4afb-bc74-d6baf9793a03>',
'warc-target-uri': 'https://www.thaicsr.com/2009/02/blog-post_08.html',
'warc-type': 'conversion'},
'nb_sentences': 34,
'offset': 0},
'text': 'ปี พ.ศ. 2521 '
'พระบาทสมเด็จพระเจ้าอยู่หัวเสด็จเยี่ยมราษฎรบ้านพระบาทห้วยต้ม '
'ทรงทอดพระเนตรเห็นสภาพพื้นที่และชีวิตความเป็น...'}
```
#### deduplicated_tk
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 22486,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VNR5UQCQIGPEZQBZL4VAOQDASFOVNRDL',
'warc-date': '2021-03-03T15:07:09Z',
'warc-identified-content-language': 'eng,rus',
'warc-record-id': '<urn:uuid:b514b9c5-1ccd-4cf0-bea7-ea38a5aef686>',
'warc-refers-to': '<urn:uuid:edf1f6cb-9f46-4790-8256-eb984db0f0d5>',
'warc-target-uri': 'http://www.newscentralasia.net/2020/12/02/move-forward-with-universal-right-and-responsibility/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Türkmenistanyň Daşary işler ministriniň Owganystanyň Milli Yslam '
'Hereketi partiýasynyň ýolbaşçysy bilen duşuşygy'}
```
#### deduplicated_tl
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 15036,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:2FGV42SN72HRKRBEEQ7QJVJBLUYQPCIH',
'warc-date': '2021-03-09T04:48:08Z',
'warc-identified-content-language': 'eng,khm,lao',
'warc-record-id': '<urn:uuid:04d772d6-09db-4d5a-86c8-22b914a35b6f>',
'warc-refers-to': '<urn:uuid:f3cdcafa-5a28-4fbb-81df-7cc5e7bb3248>',
'warc-target-uri': 'http://www.ahealthyme.com/RelatedItems/RelatedDocuments.pg?d=&TypeId=121&ContentId=761&Category=DC',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'PAUNAWA: Kung nagsasalita ka ng wikang Tagalog, mayroon kang '
'magagamit na mga libreng serbisyo para sa tulong sa wika...'}
```
#### deduplicated_tr
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 14815,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:GVNKVEGK7TMZGXIIMLV2O2YWYJRAKBO2',
'warc-date': '2021-03-04T00:44:44Z',
'warc-identified-content-language': 'tur,eng',
'warc-record-id': '<urn:uuid:7acbe6a8-83c4-4ebd-8d29-62cb0b150b2f>',
'warc-refers-to': '<urn:uuid:038ffe28-2fd1-49b9-a5c6-3dddd1af6318>',
'warc-target-uri': 'https://www.kadikoygitarkursum.com/search/label/g%C3%B6ztepe%20gitar%20dersi',
'warc-type': 'conversion'},
'nb_sentences': 5,
'offset': 0},
'text': 'İlk olarak, bir tek siyah kirpik takımı için fiyat belirleyin, '
"örneğin, 4000 ruble'ye eşittir. Artık bir müşteriyle ç..."}
```
#### deduplicated_tt
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 26112,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FAPA2JNYP6OL53T6OIL3SR3EGMX2R4XY',
'warc-date': '2021-03-09T04:42:07Z',
'warc-identified-content-language': 'tat,rus',
'warc-record-id': '<urn:uuid:5cac6257-fa6c-4e67-9ba1-8e7d7424ef54>',
'warc-refers-to': '<urn:uuid:52642c8d-da35-462f-9776-ccfa88353466>',
'warc-target-uri': 'http://saby-rt.ru/news/konkurslar/fotokonkurs',
'warc-type': 'conversion'},
'nb_sentences': 12,
'offset': 0},
'text': 'Хөрмәтле хатын-кызларбыз! Сезне чын күңелдән 8 Март бәйрәме белән '
'тәбрик итәбез! Яраткан әниләребез, әбиләребез, гоме...'}
```
#### deduplicated_tyv
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7766,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:L5GRAANBGMGNYXDFF3ECSWJ5Q6D4QFHS',
'warc-date': '2021-02-28T07:20:44Z',
'warc-identified-content-language': 'rus',
'warc-record-id': '<urn:uuid:238082a9-0adf-4c8c-b749-1a523c91e229>',
'warc-refers-to': '<urn:uuid:4bfd0ca2-52bb-4ece-9ccf-cdcee0b30ee9>',
'warc-target-uri': 'https://tyv.wikipedia.org/wiki/%D0%A1%D0%B0%D1%80%D0%BB%D1%8B%D0%BA',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Сарлык бызаазы – ниити ады, назыны бир хар чедир, сарлыктың эр '
'бызаазы аза сарлыктың кыс бызаазы деп чугаалаар.'}
```
#### deduplicated_ug
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 19089,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:DHYFNWWKECLR6BHWF763HC62JRCASMGH',
'warc-date': '2021-03-09T04:33:38Z',
'warc-identified-content-language': 'uig',
'warc-record-id': '<urn:uuid:d1185989-9cd6-40f2-ad63-003e405c9141>',
'warc-refers-to': '<urn:uuid:923ac168-6484-49ea-807d-be3ced85a885>',
'warc-target-uri': 'https://www.akademiye.org/ug/?p=10959',
'warc-type': 'conversion'},
'nb_sentences': 30,
'offset': 0},
'text': 'شەرقىي تۈركىستانئاكادېمىيە ھەققىدەئەزالىقتەۋپىق '
'مۇكاپاتىئىئانەئالاقەTürkçeEnglishئۇيغۇرچەУйғурчәUyghurche\n'
'مىللىي مەۋج...'}
```
#### deduplicated_uk
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 16706,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:46XDNKJUJSG22BA4B6DDET2R5GMBU3LV',
'warc-date': '2021-02-26T22:04:41Z',
'warc-identified-content-language': 'ukr,eng',
'warc-record-id': '<urn:uuid:a3c68b5a-f9e8-41b6-b2bb-3d43e4d7a117>',
'warc-refers-to': '<urn:uuid:6a35e918-42ce-4349-9a6c-edcd22f07254>',
'warc-target-uri': 'https://www.interesniy.kiev.ua/vasil-boroday-korifey-mistetstva-pla/',
'warc-type': 'conversion'},
'nb_sentences': 14,
'offset': 0},
'text': 'На Женевському міжнародному автосалоні 2017 бренд Fiat буде '
'показувати дві свої душі, які співіснують у великій повні...'}
```
#### deduplicated_ur
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 9450,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:3SZ3UYOSHTRE3W3PDZXRO7DDSLRKENV2',
'warc-date': '2021-03-09T03:21:23Z',
'warc-identified-content-language': 'eng,urd,bos',
'warc-record-id': '<urn:uuid:0ded0cb4-2f73-41a7-a093-5dcfed204738>',
'warc-refers-to': '<urn:uuid:6b380ef1-fec4-4f48-bcdc-86700c508dfc>',
'warc-target-uri': 'http://www.khanaghar.org/?p=50',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'اتراکھنڈ کے سلماتا گاؤں کی لڑائیتی دیوی ایک پُر اعتماد اور عقلمند '
'مجاہد ہیں، جن کی طرف دیگر خواتین بھی دیکھ رہی ہیں۔ ...'}
```
#### deduplicated_uz
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3808,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:FYYLFGJTK74HXE2LRJOAR5E6BPGCQ5NU',
'warc-date': '2021-03-09T04:38:24Z',
'warc-identified-content-language': 'uzb,ben,ltz',
'warc-record-id': '<urn:uuid:2a56bf64-042e-47fa-9abb-819b13bf7920>',
'warc-refers-to': '<urn:uuid:155b1e81-dc6e-46dc-9544-5a6a97c05118>',
'warc-target-uri': 'https://uz.wikipedia.org/wiki/1408',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Matn Creative Commons Attribution-ShareAlike litsenziyasi boʻyicha '
'ommalashtirilmoqda, alohida holatlarda qoʻshimcha ...'}
```
#### deduplicated_vec
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7088,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CX2L4ZL4I4OLXG7YJTXLRKNFHE7RIHRX',
'warc-date': '2021-02-24T19:06:44Z',
'warc-identified-content-language': None,
'warc-record-id': '<urn:uuid:abc5a544-7009-407a-a5a3-5c2145195bd5>',
'warc-refers-to': '<urn:uuid:4a956690-536a-437b-afe2-50dc7ac54b39>',
'warc-target-uri': 'https://vec.wikipedia.org/wiki/Utensa:Aelwyn',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Łe parołe che vien dal łatin -TAS, TATIS łe termina par -DÁ. Łe '
'parołe che łe vien da -ICUS łe tèrmina par -ÉGO. Łe p...'}
```
#### deduplicated_vi
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7845,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CCXAI5SV5PFLNPSMP4UF4SQGGSYN37AP',
'warc-date': '2021-03-03T02:43:13Z',
'warc-identified-content-language': 'vie',
'warc-record-id': '<urn:uuid:7ce27f30-a1eb-4978-83d0-5110421393b0>',
'warc-refers-to': '<urn:uuid:5dad988d-2426-402c-ac0c-1fa811ed96dc>',
'warc-target-uri': 'http://httlvinhphuoc.org/vi/duong-linh/Hoc-Kinh-Thanh-hang-ngay/Lam-Dieu-Thien-Bang-Tinh-Yeu-Thuong-6521/',
'warc-type': 'conversion'},
'nb_sentences': 8,
'offset': 0},
'text': 'Bitcoin và tiền kỹ thuật số nói chung đang dần xâm nhập vào các '
'thị trường tài chính khi ngày càng có nhiều nhà đ...'}
```
#### deduplicated_vls
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 78684,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VQNDJYOQXZLCLMDXIFCT4BHSW6LVTJQE',
'warc-date': '2021-02-28T16:16:27Z',
'warc-identified-content-language': 'fra,eng',
'warc-record-id': '<urn:uuid:266acc08-1c69-449f-95ad-0dcc82565788>',
'warc-refers-to': '<urn:uuid:c45dcd64-1b20-4ffc-bdd7-7dbff4f0a726>',
'warc-target-uri': 'https://fr.readkong.com/page/livret-des-licences-faculte-des-sciences-et-des-techniques-7906239',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': ' '
'...'}
```
#### deduplicated_vo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 1937,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:VPG56ZACAOAZTXHSSXFJOBBH44NWUSJW',
'warc-date': '2021-03-09T06:02:56Z',
'warc-identified-content-language': 'vol,eng,srp',
'warc-record-id': '<urn:uuid:2cb96947-ee22-42a8-be36-31a03203efcc>',
'warc-refers-to': '<urn:uuid:da82b7d8-535b-4e39-8d9b-ea8c3d4a4460>',
'warc-target-uri': 'https://vo.wikipedia.org/wiki/Arnesano',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'Arnesano binon zif in topäd: Puglia, in Litaliyän. Arnesano topon '
'videtü 40° 20’ N e lunetü 18° 6’ L.'}
```
#### deduplicated_wa
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 6518,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:6NC6V46TRVMWTOHCPMTDVRTP7GGL3G3S',
'warc-date': '2021-02-26T09:47:28Z',
'warc-identified-content-language': 'wol',
'warc-record-id': '<urn:uuid:4d800a25-ccf5-4d55-9795-3f7974b988b1>',
'warc-refers-to': '<urn:uuid:87119673-154b-4246-8c39-35737821a7ff>',
'warc-target-uri': 'https://wa.wikipedia.org/wiki/Senegal',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': "Cisse pådje ci n' est co k' on djermon, dj' ô bén k' el pådje est "
"djusse sibåtcheye, eyet co trop tene; et s' divreut..."}
```
#### deduplicated_war
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7356,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:SVXPIA63QN77O2IJXL4Q75LNVLDEBHYW',
'warc-date': '2021-03-09T05:49:57Z',
'warc-identified-content-language': 'war,tha,eng',
'warc-record-id': '<urn:uuid:a143ebc6-a7b4-4fa7-96b3-59ba2c1dd03c>',
'warc-refers-to': '<urn:uuid:571d090a-cb65-41e7-ae7c-d95588d41c28>',
'warc-target-uri': 'https://war.wikipedia.org/wiki/Chakri_nga_Dinastiya',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'An Chakri nga Dinastiya (Thai: ราชวงศ์จักรี: Rajawongse Chakri) '
'namuno ngan naghadi han Thailand tikang han hi hadi T...'}
```
#### deduplicated_wuu
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 26503,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:XAH2SJIYORGGSMLN4DNJZCNVG2FVWF3C',
'warc-date': '2021-03-09T04:09:05Z',
'warc-identified-content-language': 'jpn',
'warc-record-id': '<urn:uuid:8df3f922-fbbf-4733-a3a8-9f34b7505cbf>',
'warc-refers-to': '<urn:uuid:a55eb04e-3679-4817-b94b-e0317142ab2b>',
'warc-target-uri': 'https://wpedia.goo.ne.jp/wiki/%E4%BC%8A%E5%8D%81%E4%BA%94%E5%9E%8B%E6%BD%9C%E6%B0%B4%E8%89%A6',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': '伊15 [I] | 伊17 | 伊19 | 伊21 | 伊23 | 伊25 | 伊26 | 伊27 | 伊28 | 伊29 | 伊30 '
'| 伊31 | 伊32 | 伊33 | 伊34 | 伊35 | 伊36 | 伊37 | 伊38 |...'}
```
#### deduplicated_xal
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 8598,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:KGZNUXNSFUSFYC45UQJRZPEHXNGK6C3H',
'warc-date': '2021-03-02T01:27:37Z',
'warc-identified-content-language': 'rus,spa',
'warc-record-id': '<urn:uuid:676f6ca8-706b-4f77-926f-bda90e3cd772>',
'warc-refers-to': '<urn:uuid:452efc2f-85ce-4e90-b268-2f46893172f8>',
'warc-target-uri': 'http://born.altnzam.com/2014/01/',
'warc-type': 'conversion'},
'nb_sentences': 2,
'offset': 0},
'text': 'Ааһ: Хоосн ааһ би, хагсхларн һанцардсн болҗ медгдҗәнә. Нанд усн йир '
'кергтә болҗана. Ус өгит, — эзнәсн сурна.\n'
'Ааһ ууль...'}
```
#### deduplicated_xmf
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 7053,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:OQKCWDGQCIJHXMM3SCUO2KPBMFCQACUJ',
'warc-date': '2021-03-03T14:27:35Z',
'warc-identified-content-language': 'kat',
'warc-record-id': '<urn:uuid:e701a584-a14f-49ac-80b3-a7604f98fc92>',
'warc-refers-to': '<urn:uuid:8fc0f735-6e2b-45b2-bee1-bf169e08433b>',
'warc-target-uri': 'https://xmf.wikipedia.org/wiki/%E1%83%99%E1%83%90%E1%83%A2%E1%83%94%E1%83%92%E1%83%9D%E1%83%A0%E1%83%98%E1%83%90:%E1%83%90%E1%83%94%E1%83%A0%E1%83%9D%E1%83%9E%E1%83%9D%E1%83%A0%E1%83%A2%E1%83%94%E1%83%A4%E1%83%98_%E1%83%90%E1%83%9C%E1%83%91%E1%83%90%E1%83%9C%E1%83%98%E1%83%A8_%E1%83%9B%E1%83%94%E1%83%AF%E1%83%98%E1%83%9C%E1%83%90%E1%83%97',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'მოჩამილი ტექსტი წჷმორინელი რე Creative Commons '
'Attribution-ShareAlike ლიცენზიათ; შილებე გეძინელი პირობეფიშ '
'არსებუა. კ...'}
```
#### deduplicated_yi
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 10420,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:CZAVPSCGNW77WY2V2IJNK7R2CCUEMZFB',
'warc-date': '2021-02-24T21:10:52Z',
'warc-identified-content-language': 'yid,eng',
'warc-record-id': '<urn:uuid:7aa9e375-726d-42bd-832a-deee6dce5e4a>',
'warc-refers-to': '<urn:uuid:53354991-7bca-4134-95ce-ce7edebf841b>',
'warc-target-uri': 'http://www.kaveshtiebel.com/viewtopic.php?p=237817',
'warc-type': 'conversion'},
'nb_sentences': 10,
'offset': 0},
'text': 'עמעזאן איז יעצט ארויסגעקומען מיט א נייע סמארט ספיקער סיסטעם. '
"ס'הייסט Echo. אין Echo דרייט זיך א ראבאטישקע זי הייסט אל..."}
```
#### deduplicated_yo
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 3627,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:UISXP36HUEMW2LBTMAR4CTISUYAVZZAD',
'warc-date': '2021-03-07T12:45:52Z',
'warc-identified-content-language': 'yor,eng',
'warc-record-id': '<urn:uuid:e67645e9-ee6c-4c88-9b27-a158dc7f83e9>',
'warc-refers-to': '<urn:uuid:07c8d83b-7840-4238-a3b4-edc3f98ecdd5>',
'warc-target-uri': 'https://edeyorubarewa.com/itelorun/',
'warc-type': 'conversion'},
'nb_sentences': 1,
'offset': 0},
'text': 'A dá sílè fún àwọn ènìyàn tí wọn fẹ́ràn láti mò nípa èdè Yorùbá, '
'àṣà àti ìṣe ilẹ̀ kóòtù ojire. Kíkó àwọn ọmọ wa ni Èd...'}
```
#### deduplicated_zh
* Size of downloaded dataset files: None
* Size of the generated dataset: None
* Total amount of disk used: None
An example of 'train' looks as follows:
```
{ 'id': 0,
'meta': { 'headers': { 'content-length': 108400,
'content-type': 'text/plain',
'warc-block-digest': 'sha1:PP6MQUJB3F4G63HKKGKO2QJG7SMRMTFJ',
'warc-date': '2021-02-28T09:41:11Z',
'warc-identified-content-language': 'zho',
'warc-record-id': '<urn:uuid:132aab53-daff-4bae-83d0-a0cdb4039d00>',
'warc-refers-to': '<urn:uuid:2f26c020-f1fc-4216-a616-4683e0b25b1e>',
'warc-target-uri': 'http://www.yummtumm.com/offer',
'warc-type': 'conversion'},
'nb_sentences': 7,
'offset': 0},
'text': '久久精品视频在线看15_久久人人97超碰_久久爱 '
'人人澡超碰碰中文字幕,人人天天夜夜日日狠狠,久久人人97超碰,人人婷婷开心情五月,日日摸天天摸人人看,碰人人么免费视频,色综合天天综合网 '
'久久爱免费视频在线观看_久久爱视频_久久爱在线...'}
```
</details>
### Data Fields
* `id`: a `int64` feature.
* `meta`: Metadata
* `meta.headers`: WARC Headers
* `meta.headers.content-length`: `int64` Content length (in bytes) **before** cleaning
* `meta.headers.content-type`: `string` MIME type
* `meta.headers.warc-block-digest`:`string` Algorithm name and calculated value of a digest applied to the full block of the record
* `meta.headers.warc-date`: `string` Crawl date (YYYY-MM-DDThh:mm:ssZ)
* `meta.headers.warc-identified-content-language`: `string` Comma-separated list of language identifications done by CommonCrawl (uses CLD3)
* `meta.headers.warc-record-id`: `string` Record ID
* `meta.headers.warc-refers-to`: `string` Record-ID of a single record for which the present record holds additional content
* `meta.headers.warc-target-uri`: `string` URI from where the content has been fetched
* `meta.headers.warc-type`: `string` Type of the WARC Record
* `meta.nb_sentences`: `int64` Number of sentences in the text
* `meta.offset`: `int64` line offset where the related text begins. Should be used with `meta.nb_sentences` when reading the source files rather than using iterators to get related data.
* `text`: `string` content
See the [WARC Format standard](https://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/#warc-type-mandatory) for more details.
### Data Splits
<details>
<summary>Click to expand the number of samples per configuration</summary>
## Table
| Language code | language | Size original | words original | size deduplicated | words deduplicated |
|:----|:----------------------------|:-------|:----------------|:---------------|:----------------|
| af | Afrikaans | 258MB | 44,628,392 | 157MB | 27,057,785 |
| als | Alemanic | 7MB | 1,212,699 | 5MB | 871,664 |
| am | Amharic | 405MB | 30,991,914 | 241MB | 18,326,043 |
| an | Aragonese | 1MB | 115,938 | 608KB | 89,043 |
| ar | Arabic | 69GB | 6,494,332,191 | 35GB | 3,365,025,866 |
| arz | Egyptian Arabic | 48MB | 4,998,963 | 21MB | 2,341,904 |
| ast | Asturian | 7MB | 1,085,670 | 4MB | 776,069 |
| as | Assamese | 135MB | 7,917,923 | 95MB | 5,605,207 |
| av | Avaric | 421KB | 25,104 | 325KB | 19,133 |
| azb | South Azerbaijani | 47MB | 3,595,569 | 29MB | 2,243,562 |
| az | Azerbaijani | 3GB | 344,187,319 | 1GB | 169,655,478 |
| bar | Bavarian | 2KB | 247 | 1KB | 245 |
| ba | Bashkir | 110MB | 8,121,603 | 77MB | 5,625,158 |
| be | Belarusian | 2GB | 168,911,341 | 1GB | 98,212,442 |
| bg | Bulgarian | 34GB | 2,994,775,106 | 15GB | 1,315,091,995 |
| bh | Bihari languages | 579KB | 46,436 | 120KB | 9,181 |
| bn | Bangla | 14GB | 814,550,777 | 7GB | 466,289,242 |
| bo | Tibetan | 439MB | 3,751,935 | 358MB | 2,797,085 |
| bpy | Bishnupriya | 11MB | 558,819 | 4MB | 280,825 |
| br | Breton | 49MB | 8,067,480 | 23MB | 4,032,467 |
| bs | Bosnian | 310KB | 50,266 | 175KB | 25,157 |
| bxr | Russia Buriat | 22KB | 1,625 | 18KB | 1,335 |
| ca | Catalan | 13GB | 2,110,833,307 | 6GB | 1,012,770,904 |
| cbk | Chavacano | 168B | 2 | 168B | 2 |
| ceb | Cebuano | 81MB | 12,921,589 | 58MB | 9,201,870 |
| ce | Chechen | 29MB | 2,283,093 | 20MB | 1,638,963 |
| ckb | Central Kurdish | 784MB | 63,417,572 | 367MB | 29,355,017 |
| cs | Czech | 72GB | 9,996,052,434 | 33GB | 4,739,928,730 |
| cv | Chuvash | 60MB | 4,592,449 | 41MB | 3,141,872 |
| cy | Welsh | 307MB | 50,606,998 | 180MB | 30,198,860 |
| da | Danish | 18GB | 2,892,004,180 | 10GB | 1,704,605,898 |
| de | German | 433GB | 58,716,727,164 | 184GB | 25,446,071,671 |
| diq | Dimli (individual language) | 294B | 38 | 147B | 19 |
| dsb | Lower Sorbian | 31KB | 4,115 | 14KB | 1,873 |
| dv | Divehi | 143MB | 8,293,093 | 111MB | 6,481,260 |
| el | Greek | 72GB | 6,024,414,850 | 30GB | 2,539,719,195 |
| eml | Unknown language [eml] | 22KB | 4,360 | 20KB | 3,876 |
| en | English | 2936GB | 488,723,815,522 | 1342GB | 223,669,114,922 |
| eo | Esperanto | 560MB | 84,432,772 | 390MB | 59,411,208 |
| es | Spanish | 342GB | 54,715,337,438 | 160GB | 25,877,724,186 |
| et | Estonian | 7GB | 954,732,803 | 3GB | 455,553,053 |
| eu | Basque | 900MB | 110,676,692 | 503MB | 62,812,888 |
| fa | Persian | 79GB | 8,566,653,720 | 35GB | 3,902,206,854 |
| fi | Finnish | 35GB | 4,074,911,658 | 20GB | 2,357,264,196 |
| frr | Northern Frisian | 7KB | 1,702 | 5KB | 1,267 |
| fr | French | 340GB | 52,839,365,242 | 161GB | 25,245,127,073 |
| fy | Western Frisian | 82MB | 13,094,538 | 57MB | 9,329,828 |
| ga | Irish | 131MB | 20,142,627 | 69MB | 10,835,410 |
| gd | Scottish Gaelic | 2MB | 332,946 | 1MB | 173,588 |
| gl | Galician | 989MB | 155,030,216 | 549MB | 87,015,417 |
| gn | Guarani | 32KB | 3,828 | 25KB | 3,056 |
| gom | Goan Konkani | 3MB | 177,357 | 2MB | 148,801 |
| gu | Gujarati | 1GB | 124,652,589 | 950MB | 63,150,641 |
| gv | Manx | 1KB | 264 | 907B | 141 |
| he | Hebrew | 29GB | 2,829,132,925 | 11GB | 1,156,588,919 |
| hi | Hindi | 26GB | 2,009,754,819 | 13GB | 1,038,914,735 |
| hr | Croatian | 361MB | 51,654,735 | 169MB | 24,583,270 |
| hsb | Upper Sorbian | 2MB | 305,176 | 1MB | 207,715 |
| ht | Haitian Creole | 2KB | 592 | 1KB | 351 |
| hu | Hungarian | 60GB | 7,415,936,687 | 29GB | 3,765,883,306 |
| hy | Armenian | 4GB | 322,429,587 | 1GB | 124,515,953 |
| ia | Interlingua | 291KB | 74,696 | 172KB | 41,625 |
| id | Indonesian | 40GB | 5,767,715,387 | 22GB | 3,126,926,138 |
| ie | Interlingue | 7KB | 1,432 | 2KB | 424 |
| ilo | Iloko | 1MB | 275,029 | 857KB | 140,579 |
| io | Ido | 276KB | 46,463 | 221KB | 36,976 |
| is | Icelandic | 2GB | 290,997,158 | 1GB | 176,018,529 |
| it | Italian | 192GB | 29,252,541,808 | 94GB | 14,426,829,908 |
| ja | Japanese | 208GB | 5,357,000,179 | 96GB | 1,319,938,248 |
| jbo | Lojban | 929KB | 179,684 | 731KB | 140,749 |
| jv | Javanese | 858KB | 121,271 | 728KB | 101,386 |
| ka | Georgian | 6GB | 304,329,117 | 2GB | 116,422,468 |
| kk | Kazakh | 3GB | 236,767,203 | 1GB | 126,886,720 |
| km | Khmer | 1GB | 28,188,612 | 860MB | 13,408,408 |
| kn | Kannada | 2GB | 111,460,546 | 1GB | 56,801,321 |
| ko | Korean | 35GB | 3,367,279,749 | 15GB | 1,475,474,588 |
| krc | Karachay-Balkar | 2MB | 193,207 | 2MB | 153,755 |
| ku | Kurdish | 152MB | 23,845,402 | 108MB | 17,264,310 |
| kv | Komi | 1MB | 89,105 | 588KB | 46,219 |
| kw | Cornish | 119KB | 20,775 | 72KB | 12,687 |
| ky | Kyrgyz | 485MB | 33,401,287 | 334MB | 23,102,129 |
| la | Latin | 103MB | 15,869,314 | 9MB | 1,488,545 |
| lb | Luxembourgish | 54MB | 7,953,887 | 37MB | 5,454,220 |
| lez | Lezghian | 2MB | 214,890 | 2MB | 198,433 |
| li | Limburgish | 76KB | 12,105 | 54KB | 8,472 |
| lmo | Lombard | 1MB | 203,002 | 1MB | 182,533 |
| lo | Lao | 287MB | 6,928,229 | 163MB | 3,620,360 |
| lrc | Northern Luri | 183B | 26 | 183B | 26 |
| lt | Lithuanian | 12GB | 1,573,926,673 | 5GB | 701,326,575 |
| lv | Latvian | 6GB | 799,923,431 | 2GB | 352,753,044 |
| mai | Maithili | 685KB | 144,859 | 24KB | 1,916 |
| mg | Malagasy | 59MB | 8,103,631 | 38MB | 5,220,655 |
| mhr | Eastern Mari | 15MB | 1,170,650 | 10MB | 784,071 |
| min | Minangkabau | 8MB | 451,591 | 1MB | 74,882 |
| mk | Macedonian | 3GB | 261,571,966 | 1GB | 134,544,934 |
| ml | Malayalam | 4GB | 182,898,691 | 2GB | 87,615,430 |
| mn | Mongolian | 1GB | 143,244,180 | 912MB | 71,138,550 |
| mrj | Western Mari | 645KB | 51,812 | 521KB | 41,950 |
| mr | Marathi | 3GB | 173,001,078 | 1GB | 99,858,901 |
| ms | Malay | 146MB | 20,433,250 | 60MB | 8,301,250 |
| mt | Maltese | 51MB | 6,162,888 | 26MB | 3,179,815 |
| mwl | Mirandese | 3KB | 419 | 2KB | 302 |
| my | Burmese | 2GB | 54,624,239 | 1GB | 35,969,724 |
| myv | Erzya | 29KB | 2,844 | 2KB | 236 |
| mzn | Mazanderani | 1MB | 134,128 | 1MB | 106,533 |
| nah | Nahuatl languages | 34KB | 3,664 | 21KB | 2,363 |
| nap | Neapolitan | 1KB | 550 | 1KB | 235 |
| nds | Low German | 25MB | 3,998,912 | 17MB | 2,868,608 |
| ne | Nepali | 3GB | 207,891,824 | 2GB | 142,087,100 |
| new | Newari | 6MB | 433,880 | 4MB | 254,711 |
| nl | Dutch | 97GB | 15,248,924,083 | 47GB | 7,584,055,321 |
| nn | Norwegian Nynorsk | 123MB | 20,629,675 | 66MB | 11,095,804 |
| no | Norwegian Bokmål | 9GB | 1,492,984,384 | 4GB | 776,354,517 |
| oc | Occitan | 12MB | 1,822,595 | 5MB | 834,187 |
| or | Odia | 538MB | 30,838,706 | 357MB | 20,357,839 |
| os | Ossetic | 11MB | 911,794 | 6MB | 536,525 |
| pam | Pampanga | 3KB | 405 | 3KB | 405 |
| pa | Punjabi | 769MB | 59,031,334 | 430MB | 33,413,527 |
| pl | Polish | 122GB | 16,120,806,481 | 48GB | 6,496,098,108 |
| pms | Piedmontese | 4MB | 804,600 | 3MB | 644,017 |
| pnb | Western Panjabi | 68MB | 7,757,785 | 45MB | 5,221,168 |
| ps | Pashto | 404MB | 49,643,597 | 286MB | 35,345,424 |
| pt | Portuguese | 159GB | 24,770,395,312 | 71GB | 11,190,148,216 |
| qu | Quechua | 322KB | 40,691 | 230KB | 29,108 |
| rm | Romansh | 3KB | 512 | 3KB | 429 |
| ro | Romanian | 37GB | 5,629,438,576 | 15GB | 2,387,230,734 |
| rue | Rusyn | 247B | 14 | 247B | 14 |
| ru | Russian | 1201GB | 89,568,364,811 | 542GB | 41,194,052,384 |
| sah | Sakha | 57MB | 2,600,989 | 39MB | 1,944,651 |
| sa | Sanskrit | 72MB | 3,288,786 | 43MB | 1,998,089 |
| scn | Sicilian | 4KB | 712 | 3KB | 516 |
| sco | Scots | 1KB | 523 | 1KB | 282 |
| sd | Sindhi | 75MB | 8,937,427 | 50MB | 6,064,102 |
| sh | Serbian (Latin) | 13MB | 2,164,175 | 9MB | 1,461,045 |
| si | Sinhala | 1GB | 91,456,436 | 791MB | 47,770,919 |
| sk | Slovak | 14GB | 2,002,088,524 | 6GB | 865,456,498 |
| sl | Slovenian | 4GB | 610,843,131 | 1GB | 288,222,997 |
| so | Somali | 15KB | 849 | 13KB | 449 |
| sq | Albanian | 3GB | 493,861,192 | 1GB | 257,278,518 |
| sr | Serbian | 6GB | 574,460,746 | 3GB | 289,211,579 |
| su | Sundanese | 397KB | 54,420 | 274KB | 37,082 |
| sv | Swedish | 43GB | 6,542,433,732 | 19GB | 2,964,887,952 |
| sw | Swahili | 11MB | 1,853,022 | 7MB | 1,279,350 |
| ta | Tamil | 10GB | 438,489,984 | 5GB | 215,856,584 |
| te | Telugu | 3GB | 182,268,133 | 1GB | 73,193,605 |
| tg | Tajik | 985MB | 79,016,232 | 321MB | 26,069,632 |
| th | Thai | 62GB | 1,694,658,532 | 26GB | 635,230,676 |
| tk | Turkmen | 25MB | 2,693,720 | 20MB | 2,221,760 |
| tl | Filipino | 699MB | 115,471,760 | 383MB | 62,473,283 |
| tr | Turkish | 73GB | 8,763,467,387 | 33GB | 3,950,989,357 |
| tt | Tatar | 947MB | 68,793,924 | 424MB | 31,485,000 |
| tyv | Tuvinian | 9KB | 638 | 7KB | 542 |
| ug | Uyghur | 187MB | 12,786,741 | 123MB | 8,410,269 |
| uk | Ukrainian | 53GB | 4,014,675,914 | 28GB | 2,131,491,321 |
| ur | Urdu | 2GB | 354,937,986 | 1GB | 234,111,239 |
| uz | Uzbek | 56MB | 6,237,371 | 28MB | 3,327,595 |
| vec | Venetian | 37KB | 6,694 | 28KB | 5,139 |
| vi | Vietnamese | 87GB | 14,523,772,784 | 42GB | 7,011,404,625 |
| vls | West Flemish | 134B | 2 | 134B | 2 |
| vo | Volapük | 2MB | 426,052 | 2MB | 410,688 |
| war | Waray | 4MB | 750,162 | 4MB | 702,336 |
| wa | Walloon | 511KB | 93,163 | 329KB | 59,906 |
| wuu | Wu Chinese | 145KB | 9,130 | 69KB | 3,031 |
| xal | Kalmyk | 62KB | 5,495 | 62KB | 5,495 |
| xmf | Mingrelian | 16MB | 807,158 | 10MB | 510,700 |
| yi | Yiddish | 199MB | 18,699,112 | 93MB | 8,716,366 |
| yo | Yoruba | 229KB | 34,468 | 120KB | 17,487 |
| zh | Chinese | 500GB | 10,118,381,906 | 266GB | 3,898,987,727 |
</details>
## Dataset Creation
### Curation Rationale
OSCAR was constructed using [`Ungoliant`](https://github.com/oscar-corpus/ungoliant), a new pipeline derived from [goclassy](https://github.com/oscar-corpus/goclassy), itself being derived from [fastText's one](https://github.com/facebookresearch/fastText).
OSCAR 21.09 follows the [OSCAR Schema v1.1](https://oscar-corpus.com/post/oscar-schema-v1-1/), which adds metadata to each entry while staying backwards-compatible with OSCAR.
The order of operations is similar as in the goclassy pipeline, with optimisations regarding IO and a finer granlularity regarding multithreading.
`Ungoliant` is implemented in the [Rust programming language](https://rust-lang.org), and uses [rayon](https://github.com/rayon-rs/rayon) as its data parallelism strategy.
Threading is done at shard, record and sentence level, making the whole generation process much more efficient.
Filtering is done at line-level, removing lines shorter than 100 UTF-8 codepoints. While invalid UTF-8 characters are detected, they are not removed, but rather replaced with the [Replacement character](https://en.wikipedia.org/wiki/Special_(Unicode_block)#Replacement_character).
After all files are proccesed the deduplicated versions are constructed and everything is then splitted in shards and compressed.
### Source Data
#### Initial Data Collection and Normalization
[Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies.
Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics.
To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR, the **February 2021** snapshot was used. It is composed by 64 000 compressed text files containing documents and their headers.
#### Who are the source language producers?
The data comes from multiple web pages in a large variety of languages.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models.
## Considerations for Using the Data
### Social Impact of Dataset
OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures.
### Discussion of Biases
OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models.
### Other Known Limitations
The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571).
## Additional Information
### Dataset Curators
The corpus was put together by [Julien Abadji](https://ujj.space), [Pedro Ortiz Suarez](https://portizs.eu/), [Benoît Sagot](http://pauillac.inria.fr/~sagot/), and [Laurent Romary](https://cv.archives-ouvertes.fr/laurentromary), during work done at [Inria](https://www.inria.fr/en), particularly at the [ALMAnaCH team](https://team.inria.fr/almanach/).
### Licensing Information
These data are released under this licensing scheme
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR
This work is published from: France.
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
```
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
author = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Beno{\^i}t Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{\"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.},
language = {en}
}
@ARTICLE{caswell-etal-2021-quality,
author = {{Caswell}, Isaac and {Kreutzer}, Julia and {Wang}, Lisa and {Wahab}, Ahsan and {van Esch}, Daan and {Ulzii-Orshikh}, Nasanbayar and {Tapo}, Allahsera and {Subramani}, Nishant and {Sokolov}, Artem and {Sikasote}, Claytone and {Setyawan}, Monang and {Sarin}, Supheakmungkol and {Samb}, Sokhar and {Sagot}, Beno{\^\i}t and {Rivera}, Clara and {Rios}, Annette and {Papadimitriou}, Isabel and {Osei}, Salomey and {Ortiz Su{\'a}rez}, Pedro Javier and {Orife}, Iroro and {Ogueji}, Kelechi and {Niyongabo}, Rubungo Andre and {Nguyen}, Toan Q. and {M{\"u}ller}, Mathias and {M{\"u}ller}, Andr{\'e} and {Hassan Muhammad}, Shamsuddeen and {Muhammad}, Nanda and {Mnyakeni}, Ayanda and {Mirzakhalov}, Jamshidbek and {Matangira}, Tapiwanashe and {Leong}, Colin and {Lawson}, Nze and {Kudugunta}, Sneha and {Jernite}, Yacine and {Jenny}, Mathias and {Firat}, Orhan and {Dossou}, Bonaventure F.~P. and {Dlamini}, Sakhile and {de Silva}, Nisansa and {{\c{C}}abuk Ball{\i}}, Sakine and {Biderman}, Stella and {Battisti}, Alessia and {Baruwa}, Ahmed and {Bapna}, Ankur and {Baljekar}, Pallavi and {Abebe Azime}, Israel and {Awokoya}, Ayodele and {Ataman}, Duygu and {Ahia}, Orevaoghene and {Ahia}, Oghenefego and {Agrawal}, Sweta and {Adeyemi}, Mofetoluwa},
title = "{Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence},
year = 2021,
month = mar,
eid = {arXiv:2103.12028},
pages = {arXiv:2103.12028},
archivePrefix = {arXiv},
eprint = {2103.12028},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210312028C},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
}
```
### Contributions
Thanks to [@pjox](https://github.com/pjox), [@Uinelj](https://github.com/Uinelj) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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|
detection-datasets/coco | detection-datasets | 2023-03-15T15:11:53Z | 278 | 9 | [
"task_categories:object-detection",
"language:en",
"region:us"
]
| [
"object-detection"
]
| 2022-10-04T08:13:16Z | ---
task_categories:
- object-detection
language:
- en
--- | [
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|
open-llm-leaderboard/details_TheBloke__Platypus2-70B-Instruct-GPTQ | open-llm-leaderboard | 2023-09-01T08:40:25Z | 278 | 0 | [
"region:us"
]
| null | 2023-09-01T08:39:28Z | ---
pretty_name: Evaluation run of TheBloke/Platypus2-70B-Instruct-GPTQ
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/Platypus2-70B-Instruct-GPTQ](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GPTQ)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheBloke__Platypus2-70B-Instruct-GPTQ\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-01T08:39:03.285201](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Platypus2-70B-Instruct-GPTQ/blob/main/results_2023-09-01T08%3A39%3A03.285201.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6985296232204664,\n\
\ \"acc_stderr\": 0.03125037426870383,\n \"acc_norm\": 0.7020835749710057,\n\
\ \"acc_norm_stderr\": 0.031223245232596956,\n \"mc1\": 0.4455324357405141,\n\
\ \"mc1_stderr\": 0.017399335280140354,\n \"mc2\": 0.6253657801165746,\n\
\ \"mc2_stderr\": 0.01474854589221215\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6919795221843004,\n \"acc_stderr\": 0.013491429517292038,\n\
\ \"acc_norm\": 0.712457337883959,\n \"acc_norm_stderr\": 0.013226719056266129\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6863174666401115,\n\
\ \"acc_stderr\": 0.004630407476835178,\n \"acc_norm\": 0.8755228042222665,\n\
\ \"acc_norm_stderr\": 0.003294504807555233\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\
\ \"acc_stderr\": 0.042446332383532286,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.042446332383532286\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03317672787533157,\n\
\ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03317672787533157\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.71,\n\
\ \"acc_stderr\": 0.04560480215720683,\n \"acc_norm\": 0.71,\n \
\ \"acc_norm_stderr\": 0.04560480215720683\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7471698113207547,\n \"acc_stderr\": 0.026749899771241214,\n\
\ \"acc_norm\": 0.7471698113207547,\n \"acc_norm_stderr\": 0.026749899771241214\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.031164899666948614,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.031164899666948614\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n\
\ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\
\ \"acc_stderr\": 0.03583901754736411,\n \"acc_norm\": 0.6705202312138728,\n\
\ \"acc_norm_stderr\": 0.03583901754736411\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.04755129616062947,\n\
\ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.04755129616062947\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909281,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909281\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6680851063829787,\n \"acc_stderr\": 0.03078373675774565,\n\
\ \"acc_norm\": 0.6680851063829787,\n \"acc_norm_stderr\": 0.03078373675774565\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n\
\ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\
\ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6137931034482759,\n \"acc_stderr\": 0.04057324734419036,\n\
\ \"acc_norm\": 0.6137931034482759,\n \"acc_norm_stderr\": 0.04057324734419036\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4656084656084656,\n \"acc_stderr\": 0.02569032176249384,\n \"\
acc_norm\": 0.4656084656084656,\n \"acc_norm_stderr\": 0.02569032176249384\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5396825396825397,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.5396825396825397,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8064516129032258,\n \"acc_stderr\": 0.022475258525536057,\n \"\
acc_norm\": 0.8064516129032258,\n \"acc_norm_stderr\": 0.022475258525536057\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5467980295566502,\n \"acc_stderr\": 0.03502544650845872,\n \"\
acc_norm\": 0.5467980295566502,\n \"acc_norm_stderr\": 0.03502544650845872\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\"\
: 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8787878787878788,\n \"acc_stderr\": 0.025485498373343237,\n\
\ \"acc_norm\": 0.8787878787878788,\n \"acc_norm_stderr\": 0.025485498373343237\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8585858585858586,\n \"acc_stderr\": 0.02482590979334334,\n \"\
acc_norm\": 0.8585858585858586,\n \"acc_norm_stderr\": 0.02482590979334334\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9481865284974094,\n \"acc_stderr\": 0.01599622932024412,\n\
\ \"acc_norm\": 0.9481865284974094,\n \"acc_norm_stderr\": 0.01599622932024412\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7025641025641025,\n \"acc_stderr\": 0.023177408131465942,\n\
\ \"acc_norm\": 0.7025641025641025,\n \"acc_norm_stderr\": 0.023177408131465942\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114982,\n \
\ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114982\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7815126050420168,\n \"acc_stderr\": 0.02684151432295894,\n \
\ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.02684151432295894\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.47019867549668876,\n \"acc_stderr\": 0.040752249922169775,\n \"\
acc_norm\": 0.47019867549668876,\n \"acc_norm_stderr\": 0.040752249922169775\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.908256880733945,\n \"acc_stderr\": 0.012376323409137116,\n \"\
acc_norm\": 0.908256880733945,\n \"acc_norm_stderr\": 0.012376323409137116\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5972222222222222,\n \"acc_stderr\": 0.03344887382997866,\n \"\
acc_norm\": 0.5972222222222222,\n \"acc_norm_stderr\": 0.03344887382997866\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9068627450980392,\n \"acc_stderr\": 0.020397853969427,\n \"acc_norm\"\
: 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969427\n },\n\
\ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\
\ 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065494,\n \"\
acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065494\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7982062780269058,\n\
\ \"acc_stderr\": 0.026936111912802277,\n \"acc_norm\": 0.7982062780269058,\n\
\ \"acc_norm_stderr\": 0.026936111912802277\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752596,\n\
\ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752596\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035216,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035216\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8282208588957055,\n \"acc_stderr\": 0.02963471727237103,\n\
\ \"acc_norm\": 0.8282208588957055,\n \"acc_norm_stderr\": 0.02963471727237103\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5714285714285714,\n\
\ \"acc_stderr\": 0.04697113923010213,\n \"acc_norm\": 0.5714285714285714,\n\
\ \"acc_norm_stderr\": 0.04697113923010213\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n\
\ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9017094017094017,\n\
\ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\
\ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8659003831417624,\n\
\ \"acc_stderr\": 0.012185528166499978,\n \"acc_norm\": 0.8659003831417624,\n\
\ \"acc_norm_stderr\": 0.012185528166499978\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7687861271676301,\n \"acc_stderr\": 0.02269865716785571,\n\
\ \"acc_norm\": 0.7687861271676301,\n \"acc_norm_stderr\": 0.02269865716785571\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.646927374301676,\n\
\ \"acc_stderr\": 0.01598420454526858,\n \"acc_norm\": 0.646927374301676,\n\
\ \"acc_norm_stderr\": 0.01598420454526858\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.024288619466046105,\n\
\ \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.024288619466046105\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.77491961414791,\n\
\ \"acc_stderr\": 0.023720088516179027,\n \"acc_norm\": 0.77491961414791,\n\
\ \"acc_norm_stderr\": 0.023720088516179027\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8271604938271605,\n \"acc_stderr\": 0.02103851777015737,\n\
\ \"acc_norm\": 0.8271604938271605,\n \"acc_norm_stderr\": 0.02103851777015737\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5673758865248227,\n \"acc_stderr\": 0.029555454236778852,\n \
\ \"acc_norm\": 0.5673758865248227,\n \"acc_norm_stderr\": 0.029555454236778852\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5860495436766623,\n\
\ \"acc_stderr\": 0.012579699631289262,\n \"acc_norm\": 0.5860495436766623,\n\
\ \"acc_norm_stderr\": 0.012579699631289262\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7132352941176471,\n \"acc_stderr\": 0.027472274473233818,\n\
\ \"acc_norm\": 0.7132352941176471,\n \"acc_norm_stderr\": 0.027472274473233818\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7565359477124183,\n \"acc_stderr\": 0.01736247376214661,\n \
\ \"acc_norm\": 0.7565359477124183,\n \"acc_norm_stderr\": 0.01736247376214661\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\
\ \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n\
\ \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7795918367346939,\n \"acc_stderr\": 0.02653704531214529,\n\
\ \"acc_norm\": 0.7795918367346939,\n \"acc_norm_stderr\": 0.02653704531214529\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8706467661691543,\n\
\ \"acc_stderr\": 0.023729830881018526,\n \"acc_norm\": 0.8706467661691543,\n\
\ \"acc_norm_stderr\": 0.023729830881018526\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160875,\n\
\ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160875\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4455324357405141,\n\
\ \"mc1_stderr\": 0.017399335280140354,\n \"mc2\": 0.6253657801165746,\n\
\ \"mc2_stderr\": 0.01474854589221215\n }\n}\n```"
repo_url: https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GPTQ
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|arc:challenge|25_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hellaswag|10_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T08:39:03.285201.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T08:39:03.285201.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T08:39:03.285201.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T08:39:03.285201.parquet'
- config_name: results
data_files:
- split: 2023_09_01T08_39_03.285201
path:
- results_2023-09-01T08:39:03.285201.parquet
- split: latest
path:
- results_2023-09-01T08:39:03.285201.parquet
---
# Dataset Card for Evaluation run of TheBloke/Platypus2-70B-Instruct-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GPTQ
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/Platypus2-70B-Instruct-GPTQ](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TheBloke__Platypus2-70B-Instruct-GPTQ",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-01T08:39:03.285201](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Platypus2-70B-Instruct-GPTQ/blob/main/results_2023-09-01T08%3A39%3A03.285201.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"mc1_stderr": 0.017399335280140354,
"mc2": 0.6253657801165746,
"mc2_stderr": 0.01474854589221215
},
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"acc_norm": 0.712457337883959,
"acc_norm_stderr": 0.013226719056266129
},
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},
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"harness|truthfulqa:mc|0": {
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}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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]
|
open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B | open-llm-leaderboard | 2023-09-19T02:18:15Z | 278 | 0 | [
"region:us"
]
| null | 2023-09-14T06:34:49Z | ---
pretty_name: Evaluation run of AIDC-ai-business/Marcoroni-70B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [AIDC-ai-business/Marcoroni-70B](https://huggingface.co/AIDC-ai-business/Marcoroni-70B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-19T02:16:50.789886](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B/blob/main/results_2023-09-19T02-16-50.789886.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.23992448312110085,\n\
\ \"acc_stderr\": 0.031078389352549952,\n \"acc_norm\": 0.24054395860756556,\n\
\ \"acc_norm_stderr\": 0.03108725267744147,\n \"mc1\": 1.0,\n \
\ \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n\
\ },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.24744027303754265,\n\
\ \"acc_stderr\": 0.012610352663292673,\n \"acc_norm\": 0.2790102389078498,\n\
\ \"acc_norm_stderr\": 0.013106784883601346\n },\n \"harness|hellaswag|10\"\
: {\n \"acc\": 0.2621987651862179,\n \"acc_stderr\": 0.004389312748012152,\n\
\ \"acc_norm\": 0.2671778530173272,\n \"acc_norm_stderr\": 0.004415816696303084\n\
\ },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.19,\n\
\ \"acc_stderr\": 0.03942772444036624,\n \"acc_norm\": 0.19,\n \
\ \"acc_norm_stderr\": 0.03942772444036624\n },\n \"harness|hendrycksTest-anatomy|5\"\
: {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.0335567721631314,\n\
\ \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.0335567721631314\n\
\ },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.21710526315789475,\n\
\ \"acc_stderr\": 0.033550453048829226,\n \"acc_norm\": 0.21710526315789475,\n\
\ \"acc_norm_stderr\": 0.033550453048829226\n },\n \"harness|hendrycksTest-business_ethics|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \
\ },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.20754716981132076,\n\
\ \"acc_stderr\": 0.02495991802891127,\n \"acc_norm\": 0.20754716981132076,\n\
\ \"acc_norm_stderr\": 0.02495991802891127\n },\n \"harness|hendrycksTest-college_biology|5\"\
: {\n \"acc\": 0.2638888888888889,\n \"acc_stderr\": 0.03685651095897532,\n\
\ \"acc_norm\": 0.2638888888888889,\n \"acc_norm_stderr\": 0.03685651095897532\n\
\ },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\":\
\ 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.19,\n\
\ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\"\
: 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n\
\ \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.18497109826589594,\n \"acc_stderr\": 0.029605623981771204,\n\
\ \"acc_norm\": 0.18497109826589594,\n \"acc_norm_stderr\": 0.029605623981771204\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.22549019607843138,\n\
\ \"acc_stderr\": 0.041583075330832865,\n \"acc_norm\": 0.22549019607843138,\n\
\ \"acc_norm_stderr\": 0.041583075330832865\n },\n \"harness|hendrycksTest-computer_security|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.2723404255319149,\n\
\ \"acc_stderr\": 0.029101290698386705,\n \"acc_norm\": 0.2723404255319149,\n\
\ \"acc_norm_stderr\": 0.029101290698386705\n },\n \"harness|hendrycksTest-econometrics|5\"\
: {\n \"acc\": 0.2807017543859649,\n \"acc_stderr\": 0.042270544512322,\n\
\ \"acc_norm\": 0.2807017543859649,\n \"acc_norm_stderr\": 0.042270544512322\n\
\ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\
: 0.25517241379310346,\n \"acc_stderr\": 0.03632984052707842,\n \"\
acc_norm\": 0.25517241379310346,\n \"acc_norm_stderr\": 0.03632984052707842\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.24603174603174602,\n \"acc_stderr\": 0.022182037202948365,\n \"\
acc_norm\": 0.24603174603174602,\n \"acc_norm_stderr\": 0.022182037202948365\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2777777777777778,\n\
\ \"acc_stderr\": 0.04006168083848876,\n \"acc_norm\": 0.2777777777777778,\n\
\ \"acc_norm_stderr\": 0.04006168083848876\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.2,\n \"acc_stderr\": 0.022755204959542932,\n \"acc_norm\": 0.2,\n\
\ \"acc_norm_stderr\": 0.022755204959542932\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.22167487684729065,\n \"acc_stderr\": 0.029225575892489607,\n\
\ \"acc_norm\": 0.22167487684729065,\n \"acc_norm_stderr\": 0.029225575892489607\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.21,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\"\
: 0.21,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.2545454545454545,\n \"acc_stderr\": 0.0340150671524904,\n\
\ \"acc_norm\": 0.2545454545454545,\n \"acc_norm_stderr\": 0.0340150671524904\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.20707070707070707,\n \"acc_stderr\": 0.02886977846026705,\n \"\
acc_norm\": 0.20707070707070707,\n \"acc_norm_stderr\": 0.02886977846026705\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.2849740932642487,\n \"acc_stderr\": 0.03257714077709661,\n\
\ \"acc_norm\": 0.2849740932642487,\n \"acc_norm_stderr\": 0.03257714077709661\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.23333333333333334,\n \"acc_stderr\": 0.021444547301560486,\n\
\ \"acc_norm\": 0.23333333333333334,\n \"acc_norm_stderr\": 0.021444547301560486\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.22592592592592592,\n \"acc_stderr\": 0.025497532639609542,\n \
\ \"acc_norm\": 0.22592592592592592,\n \"acc_norm_stderr\": 0.025497532639609542\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.19747899159663865,\n \"acc_stderr\": 0.025859164122051467,\n\
\ \"acc_norm\": 0.19747899159663865,\n \"acc_norm_stderr\": 0.025859164122051467\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.23841059602649006,\n \"acc_stderr\": 0.0347918557259966,\n \"\
acc_norm\": 0.23841059602649006,\n \"acc_norm_stderr\": 0.0347918557259966\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.21651376146788992,\n \"acc_stderr\": 0.017658710594443145,\n \"\
acc_norm\": 0.21651376146788992,\n \"acc_norm_stderr\": 0.017658710594443145\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.1712962962962963,\n \"acc_stderr\": 0.025695341643824685,\n \"\
acc_norm\": 0.1712962962962963,\n \"acc_norm_stderr\": 0.025695341643824685\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\
\ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.25738396624472576,\n \"acc_stderr\": 0.028458820991460302,\n\
\ \"acc_norm\": 0.25738396624472576,\n \"acc_norm_stderr\": 0.028458820991460302\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.2914798206278027,\n\
\ \"acc_stderr\": 0.030500283176545902,\n \"acc_norm\": 0.2914798206278027,\n\
\ \"acc_norm_stderr\": 0.030500283176545902\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.24427480916030533,\n \"acc_stderr\": 0.037683359597287434,\n\
\ \"acc_norm\": 0.24427480916030533,\n \"acc_norm_stderr\": 0.037683359597287434\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2809917355371901,\n \"acc_stderr\": 0.04103203830514511,\n \"\
acc_norm\": 0.2809917355371901,\n \"acc_norm_stderr\": 0.04103203830514511\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\
\ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\
\ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.22699386503067484,\n \"acc_stderr\": 0.032910995786157686,\n\
\ \"acc_norm\": 0.22699386503067484,\n \"acc_norm_stderr\": 0.032910995786157686\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.04287858751340456,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.04287858751340456\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.1941747572815534,\n \"acc_stderr\": 0.03916667762822584,\n\
\ \"acc_norm\": 0.1941747572815534,\n \"acc_norm_stderr\": 0.03916667762822584\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.25213675213675213,\n\
\ \"acc_stderr\": 0.02844796547623101,\n \"acc_norm\": 0.25213675213675213,\n\
\ \"acc_norm_stderr\": 0.02844796547623101\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.26181353767560667,\n\
\ \"acc_stderr\": 0.015720838678445266,\n \"acc_norm\": 0.26181353767560667,\n\
\ \"acc_norm_stderr\": 0.015720838678445266\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.02317629820399201,\n\
\ \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.02317629820399201\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.25251396648044694,\n\
\ \"acc_stderr\": 0.014530330201468645,\n \"acc_norm\": 0.25251396648044694,\n\
\ \"acc_norm_stderr\": 0.014530330201468645\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.025261691219729487,\n\
\ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.025261691219729487\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2508038585209003,\n\
\ \"acc_stderr\": 0.024619771956697165,\n \"acc_norm\": 0.2508038585209003,\n\
\ \"acc_norm_stderr\": 0.024619771956697165\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.22530864197530864,\n \"acc_stderr\": 0.02324620264781975,\n\
\ \"acc_norm\": 0.22530864197530864,\n \"acc_norm_stderr\": 0.02324620264781975\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.2765957446808511,\n \"acc_stderr\": 0.026684564340461004,\n \
\ \"acc_norm\": 0.2765957446808511,\n \"acc_norm_stderr\": 0.026684564340461004\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.25358539765319427,\n\
\ \"acc_stderr\": 0.011111715336101136,\n \"acc_norm\": 0.25358539765319427,\n\
\ \"acc_norm_stderr\": 0.011111715336101136\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.02352924218519311,\n\
\ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.02352924218519311\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.24673202614379086,\n \"acc_stderr\": 0.017440820367402493,\n \
\ \"acc_norm\": 0.24673202614379086,\n \"acc_norm_stderr\": 0.017440820367402493\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.19090909090909092,\n\
\ \"acc_stderr\": 0.03764425585984927,\n \"acc_norm\": 0.19090909090909092,\n\
\ \"acc_norm_stderr\": 0.03764425585984927\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.18775510204081633,\n \"acc_stderr\": 0.02500025603954621,\n\
\ \"acc_norm\": 0.18775510204081633,\n \"acc_norm_stderr\": 0.02500025603954621\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23383084577114427,\n\
\ \"acc_stderr\": 0.029929415408348384,\n \"acc_norm\": 0.23383084577114427,\n\
\ \"acc_norm_stderr\": 0.029929415408348384\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.25301204819277107,\n\
\ \"acc_stderr\": 0.03384429155233134,\n \"acc_norm\": 0.25301204819277107,\n\
\ \"acc_norm_stderr\": 0.03384429155233134\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.26900584795321636,\n \"acc_stderr\": 0.0340105262010409,\n\
\ \"acc_norm\": 0.26900584795321636,\n \"acc_norm_stderr\": 0.0340105262010409\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 1.0,\n \"mc1_stderr\"\
: 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n }\n}\n```"
repo_url: https://huggingface.co/AIDC-ai-business/Marcoroni-70B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|arc:challenge|25_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|arc:challenge|25_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|arc:challenge|25_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|arc:challenge|25_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hellaswag|10_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hellaswag|10_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hellaswag|10_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hellaswag|10_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-14T06-34-33.473104.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-14T06-34-33.473104.parquet'
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- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-19T02-16-50.789886.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T02-16-50.789886.parquet'
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- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T02-16-50.789886.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T02-16-50.789886.parquet'
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- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T02-16-50.789886.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T02-16-50.789886.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T02-16-50.789886.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-09-19T02-16-50.789886.parquet'
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- split: latest
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-09-19T02-16-50.789886.parquet'
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- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-19T02-16-50.789886.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-19T02-16-50.789886.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-14T19-48-28.878729.parquet'
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-19T01-46-19.012527.parquet'
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-14T19-48-28.878729.parquet'
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-19T01-46-19.012527.parquet'
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
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- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-19T01-46-19.012527.parquet'
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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data_files:
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data_files:
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data_files:
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data_files:
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path:
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data_files:
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path:
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data_files:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-19T02-16-50.789886.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-14T06-34-33.473104.parquet'
- split: 2023_09_14T19_48_28.878729
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-14T19-48-28.878729.parquet'
- split: 2023_09_19T01_46_19.012527
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-19T01-46-19.012527.parquet'
- split: 2023_09_19T02_16_50.789886
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-19T02-16-50.789886.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-19T02-16-50.789886.parquet'
- config_name: results
data_files:
- split: 2023_09_14T06_34_33.473104
path:
- results_2023-09-14T06-34-33.473104.parquet
- split: 2023_09_14T19_48_28.878729
path:
- results_2023-09-14T19-48-28.878729.parquet
- split: 2023_09_19T01_46_19.012527
path:
- results_2023-09-19T01-46-19.012527.parquet
- split: 2023_09_19T02_16_50.789886
path:
- results_2023-09-19T02-16-50.789886.parquet
- split: latest
path:
- results_2023-09-19T02-16-50.789886.parquet
---
# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/AIDC-ai-business/Marcoroni-70B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [AIDC-ai-business/Marcoroni-70B](https://huggingface.co/AIDC-ai-business/Marcoroni-70B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-19T02:16:50.789886](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B/blob/main/results_2023-09-19T02-16-50.789886.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"mc1_stderr": 0.0,
"mc2": NaN,
"mc2_stderr": NaN
},
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},
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"harness|truthfulqa:mc|0": {
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}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
Geonmo/midjourney-prompts-only | Geonmo | 2023-10-25T09:12:51Z | 278 | 1 | [
"region:us"
]
| null | 2023-10-25T09:10:06Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 495800699
num_examples: 3492544
download_size: 334934157
dataset_size: 495800699
---
# Dataset Card for "midjourney-prompts-only"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
5cp/tmp_imdb_ft | 5cp | 2023-11-14T21:23:17Z | 278 | 0 | [
"region:us"
]
| null | 2023-11-14T21:04:22Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 21120329
num_examples: 782
- name: test
num_bytes: 23158476
num_examples: 858
download_size: 736300
dataset_size: 44278805
---
# Dataset Card for "tmp_imdb_ft"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
ms_terms | null | 2022-11-03T16:08:00Z | 277 | 3 | [
"task_categories:translation",
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]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- af
- am
- ar
- as
- az
- be
- bg
- bn
- bs
- ca
- chr
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fil
- fr
- ga
- gd
- gl
- gu
- guc
- ha
- he
- hi
- hr
- hu
- hy
- id
- ig
- is
- it
- iu
- ja
- ka
- kk
- km
- kn
- knn
- ko
- ku
- ky
- lb
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- lv
- mi
- mk
- ml
- mn
- mr
- ms
- mt
- nb
- ne
- nl
- nn
- ory
- pa
- pl
- prs
- pst
- pt
- qu
- quc
- ro
- ru
- rw
- sd
- si
- sk
- sl
- sq
- sr
- st
- sv
- swh
- ta
- te
- tg
- th
- ti
- tk
- tn
- tr
- tt
- ug
- uk
- ur
- uz
- vi
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- xh
- yo
- zh
- zu
language_bcp47:
- bn-IN
- bs-Latn
- es-MX
- fr-CA
- ms-BN
- pt-BR
- sr-BH
- sr-Latn
- zh-Hant-HK
- zh-Hant-TW
license:
- ms-pl
multilinguality:
- multilingual
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: MsTerms
dataset_info:
features:
- name: entry_id
dtype: string
- name: term_source
dtype: string
- name: pos
dtype: string
- name: definition
dtype: string
- name: term_target
dtype: string
splits:
- name: train
num_bytes: 6995497
num_examples: 33738
download_size: 0
dataset_size: 6995497
---
# Dataset Card for [ms_terms]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
[Microsoft Terminology Collection](https://www.microsoft.com/en-us/language/terminology)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Microsoft Terminology Collection can be used to develop localized versions of applications that integrate with Microsoft products. It can also be used to integrate Microsoft terminology into other terminology collections or serve as a base IT glossary for language development in the nearly 100 languages available. Terminology is provided in .tbx format, an industry standard for terminology exchange.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Nearly 100 Languages.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@leoxzhao](https://github.com/leoxzhao), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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|
opus_dogc | null | 2022-11-03T16:07:43Z | 277 | 0 | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ca",
"language:es",
"license:cc0-1.0",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- ca
- es
license:
- cc0-1.0
multilinguality:
- translation
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OPUS DOGC
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- ca
- es
config_name: tmx
splits:
- name: train
num_bytes: 1258924464
num_examples: 4763575
download_size: 331724078
dataset_size: 1258924464
---
# Dataset Card for OPUS DOGC
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/DOGC.php
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OPUS DOGC is a collection of documents from the Official Journal of the Government of Catalonia, in Catalan and Spanish languages, provided by Antoni Oliver Gonzalez from the Universitat Oberta de Catalunya.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is multilingual with parallel text in:
- Catalan
- Spanish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
A data instance contains the following fields:
- `ca`: the Catalan text
- `es`: the aligned Spanish text
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Dataset is in the Public Domain under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/).
### Citation Information
```
@inproceedings{tiedemann-2012-parallel,
title = "Parallel Data, Tools and Interfaces in {OPUS}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf",
pages = "2214--2218",
abstract = "This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.",
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. | [
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|
sepedi_ner | null | 2023-01-25T14:44:06Z | 277 | 1 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:nso",
"license:other",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- nso
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Sepedi NER Corpus
license_details: Creative Commons Attribution 2.5 South Africa License
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': OUT
'1': B-PERS
'2': I-PERS
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: sepedi_ner
splits:
- name: train
num_bytes: 3378134
num_examples: 7117
download_size: 22077376
dataset_size: 3378134
---
# Dataset Card for Sepedi NER Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Sepedi Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/328)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Martin Puttkammer](mailto:[email protected])
### Dataset Summary
The Sepedi Ner Corpus is a Sepedi dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Sepedi language. The dataset uses CoNLL shared task annotation standards.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Sesotho sa Leboa (Sepedi).
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
```
{'id': '0',
'ner_tags': [0, 0, 0, 0, 0],
'tokens': ['Maikemišetšo', 'a', 'websaete', 'ya', 'ditirelo']
}
```
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity.
### Data Splits
The data was not split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - sepedi.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data is based on South African government domain and was crawled from gov.za websites.
#### Who are the source language producers?
The data was produced by writers of South African government websites - gov.za
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated during the NCHLT text resource development project.
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa).
See: [more information](http://www.nwu.ac.za/ctext)
### Licensing Information
The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode)
### Citation Information
```
@inproceedings{sepedi_ner_corpus,
author = {D.J. Prinsloo and
Roald Eiselen},
title = {NCHLT Sepedi Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.},
year = {2016},
url = {https://repo.sadilar.org/handle/20.500.12185/328},
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset. | [
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|
ttc4900 | null | 2023-01-25T14:54:33Z | 277 | 2 | [
"task_categories:text-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:tr",
"license:unknown",
"news-category-classification",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- tr
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: TTC4900 - A Benchmark Data for Turkish Text Categorization
tags:
- news-category-classification
dataset_info:
features:
- name: category
dtype:
class_label:
names:
'0': siyaset
'1': dunya
'2': ekonomi
'3': kultur
'4': saglik
'5': spor
'6': teknoloji
- name: text
dtype: string
config_name: ttc4900
splits:
- name: train
num_bytes: 10640831
num_examples: 4900
download_size: 10627541
dataset_size: 10640831
---
# Dataset Card for TTC4900: A Benchmark Data for Turkish Text Categorization
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [TTC4900 Homepage](https://www.kaggle.com/savasy/ttc4900)
- **Repository:** [TTC4900 Repository](https://github.com/savasy/TurkishTextClassification)
- **Paper:** [A Comparison of Different Approaches to Document Representation in Turkish Language](https://dergipark.org.tr/en/pub/sdufenbed/issue/38975/456349)
- **Point of Contact:** [Savaş Yıldırım](mailto:[email protected])
### Dataset Summary
The data set is taken from [kemik group](http://www.kemik.yildiz.edu.tr/)
The data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth.
We named TTC4900 by mimicking the name convention of TTC 3600 dataset shared by the study ["A Knowledge-poor Approach to Turkish Text Categorization with a Comparative Analysis, Proceedings of CICLING 2014, Springer LNCS, Nepal, 2014"](https://link.springer.com/chapter/10.1007/978-3-642-54903-8_36)
If you use the dataset in a paper, please refer https://www.kaggle.com/savasy/ttc4900 as footnote and cite one of the papers as follows:
- A Comparison of Different Approaches to Document Representation in Turkish Language, SDU Journal of Natural and Applied Science, Vol 22, Issue 2, 2018
- A comparative analysis of text classification for Turkish language, Pamukkale University Journal of Engineering Science Volume 25 Issue 5, 2018
- A Knowledge-poor Approach to Turkish Text Categorization with a Comparative Analysis, Proceedings of CICLING 2014, Springer LNCS, Nepal, 2014.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Turkish.
## Dataset Structure
### Data Instances
A text classification dataset with 7 different news category.
Here is an example from the dataset:
```
{
"category": 0, # politics/siyaset
"text": "paris teki infaz imralı ile başlayan sürece bir darbe mi elif_çakır ın sunduğu söz_bitmeden in bugünkü konuğu gazeteci melih altınok oldu programdan satıbaşları imralı ile görüşmeler hangi aşamada bundan sonra ne olacak hangi kesimler sürece engel oluyor psikolojik mayınlar neler türk solu bu dönemde evrensel sorumluluğunu yerine getirebiliyor mu elif_çakır sordu melih altınok söz_bitmeden de yanıtladı elif_çakır pkk nın silahsızlandırılmasına yönelik olarak öcalan ile görüşme sonrası 3 kadının infazı enteresan çünkü kurucu isimlerden birisi sen nasıl okudun bu infazı melih altınok herkesin ciddi anlamda şüpheleri var şu an yürüttüğümüz herşey bir delile dayanmadığı için komple teorisinden ibaret kalacak ama şöyle bir durum var imralı görüşmelerin ilk defa bir siyasi iktidar tarafından açıkça söylendiği bir dönem ardından geliyor bu sürecin gerçekleşmemesini isteyen kesimler yaptırmıştır dedi"
}
```
### Data Fields
- **category** : Indicates to which category the news text belongs.
(Such as "politics", "world", "economy", "culture", "health", "sports", "technology".)
- **text** : Contains the text of the news.
### Data Splits
It is not divided into Train set and Test set.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth.
#### Who are the source language producers?
Turkish online news sites.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was created by [Savaş Yıldırım](https://github.com/savasy)
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{doi:10.5505/pajes.2018.15931,
author = {Yıldırım, Savaş and Yıldız, Tuğba},
title = {A comparative analysis of text classification for Turkish language},
journal = {Pamukkale Univ Muh Bilim Derg},
volume = {24},
number = {5},
pages = {879-886},
year = {2018},
doi = {10.5505/pajes.2018.15931},
note ={doi: 10.5505/pajes.2018.15931},
URL = {https://dx.doi.org/10.5505/pajes.2018.15931},
eprint = {https://dx.doi.org/10.5505/pajes.2018.15931}
}
```
### Contributions
Thanks to [@yavuzKomecoglu](https://github.com/yavuzKomecoglu) for adding this dataset. | [
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|
youtube_caption_corrections | null | 2023-01-25T15:03:42Z | 277 | 4 | [
"task_categories:other",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"token-classification-of-text-errors",
"region:us"
]
| [
"other",
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
- text-generation
- fill-mask
task_ids:
- slot-filling
pretty_name: YouTube Caption Corrections
tags:
- token-classification-of-text-errors
dataset_info:
features:
- name: video_ids
dtype: string
- name: default_seq
sequence: string
- name: correction_seq
sequence: string
- name: diff_type
sequence:
class_label:
names:
'0': NO_DIFF
'1': CASE_DIFF
'2': PUNCUATION_DIFF
'3': CASE_AND_PUNCUATION_DIFF
'4': STEM_BASED_DIFF
'5': DIGIT_DIFF
'6': INTRAWORD_PUNC_DIFF
'7': UNKNOWN_TYPE_DIFF
'8': RESERVED_DIFF
splits:
- name: train
num_bytes: 355978939
num_examples: 10769
download_size: 222479455
dataset_size: 355978939
---
# Dataset Card for YouTube Caption Corrections
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/2dot71mily/youtube_captions_corrections
- **Repository:** https://github.com/2dot71mily/youtube_captions_corrections
- **Paper:** [N/A]
- **Leaderboard:** [N/A]
- **Point of Contact:** Emily McMilin
### Dataset Summary
This dataset is built from pairs of YouTube captions where both an auto-generated and a manually-corrected caption are available for a single specified language. It currently only in English, but scripts at repo support other languages. The motivation for creating it was from viewing errors in auto-generated captions at a recent virtual conference, with the hope that there could be some way to help correct those errors.
The dataset in the repo at https://github.com/2dot71mily/youtube_captions_corrections records in a non-destructive manner all the differences between an auto-generated and a manually-corrected caption for thousands of videos. The dataset here focuses on the subset of those differences which are mutual and have the same size in token length difference, which means it excludes token insertion or deletion differences between the two captions. Therefore dataset here remains a non-destructive representation of the original auto-generated captions, but excludes some of the differences that are found in the manually-corrected captions.
### Supported Tasks and Leaderboards
- `token-classification`: The tokens in `default_seq` are from the auto-generated YouTube captions. If `diff_type` is labeled greater than `0` at a given index, then the associated token in same index in the `default_seq` was found to be different to the token in the manually-corrected YouTube caption, and therefore we assume it is an error. A model can be trained to learn when there are errors in the auto-generated captions.
- `slot-filling`: The `correction_seq` is sparsely populated with tokens from the manually-corrected YouTube captions in the locations where there was found to be a difference to the token in the auto-generated YouTube captions. These 'incorrect' tokens in the `default_seq` can be masked in the locations where `diff_type` is labeled greater than `0`, so that a model can be trained to hopefully find a better word to fill in, rather than the 'incorrect' one.
End to end, the models could maybe first identify and then replace (with suitable alternatives) errors in YouTube and other auto-generated captions that are lacking manual corrections
### Languages
English
## Dataset Structure
### Data Instances
If `diff_type` is labeled greater than `0` at a given index, then the associated token in same index in the `default_seq` was found to have a difference to the token in the manually-corrected YouTube caption. The `correction_seq` is sparsely populated with tokens from the manually-corrected YouTube captions at those locations of differences.
`diff_type` labels for tokens are as follows:
0: No difference
1: Case based difference, e.g. `hello` vs `Hello`
2: Punctuation difference, e.g. `hello` vs `hello`
3: Case and punctuation difference, e.g. `hello` vs `Hello,`
4: Word difference with same stem, e.g. `thank` vs `thanked`
5: Digit difference, e.g. `2` vs `two`
6: Intra-word punctuation difference, e.g. `autogenerated` vs `auto-generated`
7: Unknown type of difference, e.g. `laughter` vs `draft`
8: Reserved for unspecified difference
{
'video_titles': '_QUEXsHfsA0',
'default_seq': ['you', 'see', "it's", 'a', 'laughter', 'but', 'by', 'the', 'time', 'you', 'see', 'this', 'it', "won't", 'be', 'so', 'we', 'have', 'a', 'big']
'correction_seq': ['', 'see,', '', '', 'draft,', '', '', '', '', '', 'read', 'this,', '', '', 'be.', 'So', '', '', '', '']
'diff_type': [0, 2, 0, 0, 7, 0, 0, 0, 0, 0, 7, 2, 0, 0, 2, 1, 0, 0, 0, 0]
}
### Data Fields
- 'video_ids': Unique ID used by YouTube for each video. Can paste into `https://www.youtube.com/watch?v=<{video_ids}` to see video
- 'default_seq': Tokenized auto-generated YouTube captions for the video
- 'correction_seq': Tokenized manually-corrected YouTube captions only at those locations, where there is a difference between the auto-generated and manually-corrected captions
- 'diff_type': A value greater than `0` at every token where there is a difference between the auto-generated and manually-corrected captions
### Data Splits
No data splits
## Dataset Creation
### Curation Rationale
It was created after viewing errors in auto-generated captions at a recent virtual conference, with the hope that there could be some way to help correct those errors.
### Source Data
#### Initial Data Collection and Normalization
All captions are requested via `googleapiclient` and `youtube_transcript_api` at the `channel_id` and language granularity, using scripts written at https://github.com/2dot71mily/youtube_captions_corrections.
The captions are tokenized on spaces and the manually-corrected sequence has here been reduced to only include differences between it and the auto-generated sequence.
#### Who are the source language producers?
Auto-generated scripts are from YouTube and the manually-corrected scripts are from creators, and any support they may have (e.g. community or software support)
### Annotations
#### Annotation process
Scripts at repo, https://github.com/2dot71mily/youtube_captions_corrections take a diff of the two captions and use this to create annotations.
#### Who are the annotators?
YouTube creators, and any support they may have (e.g. community or software support)
### Personal and Sensitive Information
All content publicly available on YouTube
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Emily McMilin
### Licensing Information
MIT License
### Citation Information
https://github.com/2dot71mily/youtube_captions_corrections
### Contributions
Thanks to [@2dot71mily](https://github.com/2dot71mily) for adding this dataset. | [
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]
|
Fraser/dream-coder | Fraser | 2022-04-25T10:49:02Z | 277 | 3 | [
"language:en",
"license:mit",
"program-synthesis",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
thumbnail: "https://huggingface.co/datasets/Fraser/dream-coder/resolve/main/img.png"
tags:
- program-synthesis
license: "mit"
datasets:
- program-synthesis
---
# Program Synthesis Data
Generated program synthesis datasets used to train [dreamcoder](https://github.com/ellisk42/ec).
Currently just supports text & list data.
| [
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|
NYTK/HuSST | NYTK | 2023-03-27T09:54:13Z | 277 | 1 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:text-scoring",
"annotations_creators:found",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|other",
"language:hu",
"license:bsd-2-clause",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
- expert-generated
language:
- hu
license:
- bsd-2-clause
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- extended|other
task_categories:
- text-classification
task_ids:
- sentiment-classification
- sentiment-scoring
- text-scoring
pretty_name: HuSST
---
# Dataset Card for HuSST
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Language](#language)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
[HuSST dataset](https://github.com/nytud/HuSST)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
[lnnoemi](mailto:[email protected])
### Dataset Summary
This is the dataset card for the Hungarian version of the Stanford Sentiment Treebank. This dataset which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit [HuLU](hulu.nlp.nytud.hu). The corpus was created by translating and re-annotating the original SST (Roemmele et al., 2011).
### Supported Tasks and Leaderboards
'sentiment classification'
'sentiment scoring'
### Language
The BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.
## Dataset Structure
### Data Instances
For each instance, there is an id, a sentence and a sentiment label.
An example:
```
{
"Sent_id": "dev_0",
"Sent": "Nos, a Jason elment Manhattanbe és a Pokolba kapcsán, azt hiszem, az elkerülhetetlen folytatások ötletlistájáról kihúzhatunk egy űrállomást 2455-ben (hé, ne lődd le a poént).",
"Label": "neutral"
}
```
### Data Fields
- Sent_id: unique id of the instances;
- Sent: the sentence, translation of an instance of the SST dataset;
- Label: "negative", "neutral", or "positive".
### Data Splits
HuSST has 3 splits: *train*, *validation* and *test*.
| Dataset split | Number of instances in the split |
|---------------|----------------------------------|
| train | 9344 |
| validation | 1168 |
| test | 1168 |
The test data is distributed without the labels. To evaluate your model, please [contact us](mailto:[email protected]), or check [HuLU's website](hulu.nlp.nytud.hu) for an automatic evaluation (this feature is under construction at the moment).
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The data is a translation of the content of the SST dataset (only the whole sentences were used). Each sentence was translated by a human translator. Each translation was manually checked and further refined by another annotator.
### Annotations
#### Annotation process
The translated sentences were annotated by three human annotators with one of the following labels: negative, neutral and positive. Each sentence was then curated by a fourth annotator (the 'curator'). The final label is the decision of the curator based on the three labels of the annotators.
#### Who are the annotators?
The translators were native Hungarian speakers with English proficiency. The annotators were university students with some linguistic background.
## Additional Information
### Licensing Information
### Citation Information
If you use this resource or any part of its documentation, please refer to:
Ligeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Vadász, T. (2022) HuLU: magyar nyelvű benchmark adatbázis
kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. pp. 431–446.
```
@inproceedings{ligetinagy2022hulu,
title={HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából},
author={Ligeti-Nagy, N. and Ferenczi, G. and Héja, E. and Jelencsik-Mátyus, K. and Laki, L. J. and Vadász, N. and Yang, Z. Gy. and Vadász, T.},
booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
year={2022},
pages = {431--446}
}
```
and to:
Socher et al. (2013), Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 1631--1642.
```
@inproceedings{socher-etal-2013-recursive,
title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
author = "Socher, Richard and
Perelygin, Alex and
Wu, Jean and
Chuang, Jason and
Manning, Christopher D. and
Ng, Andrew and
Potts, Christopher",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D13-1170",
pages = "1631--1642",
}
```
### Contributions
Thanks to [lnnoemi](https://github.com/lnnoemi) for adding this dataset. | [
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|
PlanTL-GOB-ES/SQAC | PlanTL-GOB-ES | 2023-10-12T23:35:38Z | 277 | 7 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:es",
"license:cc-by-sa-4.0",
"arxiv:1606.05250",
"region:us"
]
| [
"question-answering"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- es
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Spanish Question Answering Corpus (SQAC)
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# SQAC (Spanish Question-Answering Corpus)
## Dataset Description
SQAC is an extractive QA dataset for the Spanish language.
- **Paper:** [MarIA: Spanish Language Models](https://upcommons.upc.edu/bitstream/handle/2117/367156/6405-5863-1-PB%20%281%29.pdf?sequence=1)
- **Point of Contact:** [email protected]
- **Leaderboard:** [EvalEs] (https://plantl-gob-es.github.io/spanish-benchmark/)
### Dataset Summary
Contains 6,247 contexts and 18,817 questions with their respective answers, 1 to 5 for each fragment.
The sources of the contexts are:
* Encyclopedic articles from the [Spanish Wikipedia](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode).
* News articles from [Wikinews](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/).
* Newswire and literature text from the [AnCora corpus](http://clic.ub.edu/corpus/en), used under [CC-by licence](https://creativecommons.org/licenses/by/4.0/legalcode).
### Supported Tasks
Extractive-QA
### Languages
- Spanish (es)
### Directory Structure
- README.md
- SQAC.py
- dev.json
- test.json
- train.json
## Dataset Structure
### Data Instances
<pre>
{
'id': '6cf3dcd6-b5a3-4516-8f9e-c5c1c6b66628',
'title': 'Historia de Japón',
'context': 'La historia de Japón (日本の歴史 o 日本史, Nihon no rekishi / Nihonshi?) es la sucesión de hechos acontecidos dentro del archipiélago japonés. Algunos de estos hechos aparecen aislados e influenciados por la naturaleza geográfica de Japón como nación insular, en tanto que otra serie de hechos, obedece a influencias foráneas como en el caso del Imperio chino, el cual definió su idioma, su escritura y, también, su cultura política. Asimismo, otra de las influencias foráneas fue la de origen occidental, lo que convirtió al país en una nación industrial, ejerciendo con ello una esfera de influencia y una expansión territorial sobre el área del Pacífico. No obstante, dicho expansionismo se detuvo tras la Segunda Guerra Mundial y el país se posicionó en un esquema de nación industrial con vínculos a su tradición cultural.',
'question': '¿Qué influencia convirtió Japón en una nación industrial?',
'answers': {
'text': ['la de origen occidental'],
'answer_start': [473]
}
}
</pre>
### Data Fields
<pre>
{
id: str
title: str
context: str
question: str
answers: {
answer_start: [int]
text: [str]
}
}
</pre>
### Data Splits
| Split | Size |
| ------------- | ------------- |
| `train` | 15,036 |
| `dev` | 1,864 |
| `test` | 1.910 |
## Content analysis
### Number of articles, paragraphs and questions
* Number of articles: 3,834
* Number of contexts: 6,247
* Number of questions: 18,817
* Number of sentences: 48,026
* Questions/Context ratio: 3.01
* Sentences/Context ratio: 7.70
### Number of tokens
* Total tokens in context: 1,561,616
* Average tokens/context: 250
* Total tokens in questions: 203,235
* Average tokens/question: 10.80
* Total tokens in answers: 90,307
* Average tokens/answer: 4.80
### Lexical variation
46.38% of the words in the Question can be found in the Context.
### Question type
| Question | Count | % |
|----------|-------:|---:|
| qué | 6,381 | 33.91 % |
| quién/es | 2,952 | 15.69 % |
| cuál/es | 2,034 | 10.81 % |
| cómo | 1,949 | 10.36 % |
| dónde | 1,856 | 9.86 % |
| cuándo | 1,639 | 8.71 % |
| cuánto | 1,311 | 6.97 % |
| cuántos | 495 |2.63 % |
| adónde | 100 | 0.53 % |
| cuánta | 49 | 0.26 % |
| no question mark | 43 | 0.23 % |
| cuántas | 19 | 0.10 % |
## Dataset Creation
### Curation Rationale
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines from SQUAD 1.0 [(Rajpurkar, Pranav et al.)](http://arxiv.org/abs/1606.05250).
### Source Data
#### Initial Data Collection and Normalization
The source data are scraped articles from Wikinews, the Spanish Wikipedia and the AnCora corpus.
- [Spanish Wikipedia](https://es.wikipedia.org)
- [Spanish Wikinews](https://es.wikinews.org/)
- [AnCora corpus](http://clic.ub.edu/corpus/en)
#### Who are the source language producers?
Contributors to the aforementioned sites.
### Annotations
#### Annotation process
We commissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQUAD 1.0 [(Rajpurkar, Pranav et al.)](http://arxiv.org/abs/1606.05250).
#### Who are the annotators?
Native language speakers.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This corpus contributes to the development of language models in Spanish.
### Discussion of Biases
No postprocessing steps were applied to mitigate potential social biases.
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]).
For further information, send an email to ([email protected]).
This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
### Licensing information
This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License.
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Citation Information
```
@article{maria,
author = {Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquin Silveira-Ocampo and Casimiro Pio Carrino and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Aitor Gonzalez-Agirre and Marta Villegas},
title = {MarIA: Spanish Language Models},
journal = {Procesamiento del Lenguaje Natural},
volume = {68},
number = {0},
year = {2022},
issn = {1989-7553},
url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405},
pages = {39--60}
}
```
### Contributions
[N/A]
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]
|
TRoboto/names | TRoboto | 2022-01-29T16:33:25Z | 277 | 1 | [
"license:cc-by-4.0",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
project: Maha
license: cc-by-4.0
---
## Dataset Summary
It includes list of Arabic names with meaning and origin of most names
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|
huggingartists/viktor-tsoi | huggingartists | 2022-10-25T09:49:55Z | 277 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/viktor-tsoi"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.189002 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f9d03b2a6c45897724e74fab6a1aa86c.500x500x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/viktor-tsoi">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Виктор Цой (Viktor Tsoi)</div>
<a href="https://genius.com/artists/viktor-tsoi">
<div style="text-align: center; font-size: 14px;">@viktor-tsoi</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/viktor-tsoi).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/viktor-tsoi")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|118| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/viktor-tsoi")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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|
jegormeister/dutch-snli | jegormeister | 2023-10-02T19:06:35Z | 277 | 0 | [
"language:nl",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- nl
---
This is a translated version of SNLI in Dutch. The translation was performed using Google Translate. | [
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|
microsoft/LCC_python | microsoft | 2023-06-21T03:13:06Z | 277 | 4 | [
"region:us"
]
| null | 2023-06-21T03:12:37Z | ---
dataset_info:
features:
- name: gt
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 1761900743
num_examples: 100000
- name: validation
num_bytes: 146577328
num_examples: 10000
- name: test
num_bytes: 149430294
num_examples: 10000
download_size: 703086720
dataset_size: 2057908365
---
# Dataset Card for "LCC_python"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
open-llm-leaderboard/details_EleutherAI__pythia-1b-deduped | open-llm-leaderboard | 2023-09-23T08:00:05Z | 277 | 0 | [
"region:us"
]
| null | 2023-08-17T23:45:47Z | ---
pretty_name: Evaluation run of EleutherAI/pythia-1b-deduped
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_EleutherAI__pythia-1b-deduped\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T07:59:54.225479](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__pythia-1b-deduped/blob/main/results_2023-09-23T07-59-54.225479.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0016778523489932886,\n\
\ \"em_stderr\": 0.0004191330178826894,\n \"f1\": 0.047140310402684724,\n\
\ \"f1_stderr\": 0.001227508776398318,\n \"acc\": 0.27364192695789125,\n\
\ \"acc_stderr\": 0.008468429816373534\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.0004191330178826894,\n\
\ \"f1\": 0.047140310402684724,\n \"f1_stderr\": 0.001227508776398318\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.011372251705837756,\n \
\ \"acc_stderr\": 0.002920666198788757\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5359116022099447,\n \"acc_stderr\": 0.014016193433958312\n\
\ }\n}\n```"
repo_url: https://huggingface.co/EleutherAI/pythia-1b-deduped
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|arc:challenge|25_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T07_59_54.225479
path:
- '**/details_harness|drop|3_2023-09-23T07-59-54.225479.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T07-59-54.225479.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T07_59_54.225479
path:
- '**/details_harness|gsm8k|5_2023-09-23T07-59-54.225479.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T07-59-54.225479.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hellaswag|10_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:26:17.449047.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:26:17.449047.parquet'
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_management_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:26:17.449047.parquet'
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path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:26:17.449047.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_truthfulqa_mc_0
data_files:
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path:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T14:26:17.449047.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T07_59_54.225479
path:
- '**/details_harness|winogrande|5_2023-09-23T07-59-54.225479.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T07-59-54.225479.parquet'
- config_name: results
data_files:
- split: 2023_07_19T14_26_17.449047
path:
- results_2023-07-19T14:26:17.449047.parquet
- split: 2023_09_23T07_59_54.225479
path:
- results_2023-09-23T07-59-54.225479.parquet
- split: latest
path:
- results_2023-09-23T07-59-54.225479.parquet
---
# Dataset Card for Evaluation run of EleutherAI/pythia-1b-deduped
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/EleutherAI/pythia-1b-deduped
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_EleutherAI__pythia-1b-deduped",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T07:59:54.225479](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__pythia-1b-deduped/blob/main/results_2023-09-23T07-59-54.225479.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0016778523489932886,
"em_stderr": 0.0004191330178826894,
"f1": 0.047140310402684724,
"f1_stderr": 0.001227508776398318,
"acc": 0.27364192695789125,
"acc_stderr": 0.008468429816373534
},
"harness|drop|3": {
"em": 0.0016778523489932886,
"em_stderr": 0.0004191330178826894,
"f1": 0.047140310402684724,
"f1_stderr": 0.001227508776398318
},
"harness|gsm8k|5": {
"acc": 0.011372251705837756,
"acc_stderr": 0.002920666198788757
},
"harness|winogrande|5": {
"acc": 0.5359116022099447,
"acc_stderr": 0.014016193433958312
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_eachadea__vicuna-7b-1.1 | open-llm-leaderboard | 2023-09-22T23:37:24Z | 277 | 0 | [
"region:us"
]
| null | 2023-08-18T11:57:10Z | ---
pretty_name: Evaluation run of eachadea/vicuna-7b-1.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [eachadea/vicuna-7b-1.1](https://huggingface.co/eachadea/vicuna-7b-1.1) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_eachadea__vicuna-7b-1.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T23:37:12.210643](https://huggingface.co/datasets/open-llm-leaderboard/details_eachadea__vicuna-7b-1.1/blob/main/results_2023-09-22T23-37-12.210643.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.11388422818791946,\n\
\ \"em_stderr\": 0.00325324428862373,\n \"f1\": 0.16976719798657605,\n\
\ \"f1_stderr\": 0.003380156230610554,\n \"acc\": 0.38244753834582057,\n\
\ \"acc_stderr\": 0.009528517622122097\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.11388422818791946,\n \"em_stderr\": 0.00325324428862373,\n\
\ \"f1\": 0.16976719798657605,\n \"f1_stderr\": 0.003380156230610554\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05534495830174375,\n \
\ \"acc_stderr\": 0.006298221796179588\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7095501183898973,\n \"acc_stderr\": 0.012758813448064607\n\
\ }\n}\n```"
repo_url: https://huggingface.co/eachadea/vicuna-7b-1.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|arc:challenge|25_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T23_37_12.210643
path:
- '**/details_harness|drop|3_2023-09-22T23-37-12.210643.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T23-37-12.210643.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T23_37_12.210643
path:
- '**/details_harness|gsm8k|5_2023-09-22T23-37-12.210643.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T23-37-12.210643.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hellaswag|10_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:22:46.451039.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-18T12:22:46.451039.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-18T12:22:46.451039.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T23_37_12.210643
path:
- '**/details_harness|winogrande|5_2023-09-22T23-37-12.210643.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T23-37-12.210643.parquet'
- config_name: results
data_files:
- split: 2023_07_18T12_22_46.451039
path:
- results_2023-07-18T12:22:46.451039.parquet
- split: 2023_09_22T23_37_12.210643
path:
- results_2023-09-22T23-37-12.210643.parquet
- split: latest
path:
- results_2023-09-22T23-37-12.210643.parquet
---
# Dataset Card for Evaluation run of eachadea/vicuna-7b-1.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/eachadea/vicuna-7b-1.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [eachadea/vicuna-7b-1.1](https://huggingface.co/eachadea/vicuna-7b-1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_eachadea__vicuna-7b-1.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T23:37:12.210643](https://huggingface.co/datasets/open-llm-leaderboard/details_eachadea__vicuna-7b-1.1/blob/main/results_2023-09-22T23-37-12.210643.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.11388422818791946,
"em_stderr": 0.00325324428862373,
"f1": 0.16976719798657605,
"f1_stderr": 0.003380156230610554,
"acc": 0.38244753834582057,
"acc_stderr": 0.009528517622122097
},
"harness|drop|3": {
"em": 0.11388422818791946,
"em_stderr": 0.00325324428862373,
"f1": 0.16976719798657605,
"f1_stderr": 0.003380156230610554
},
"harness|gsm8k|5": {
"acc": 0.05534495830174375,
"acc_stderr": 0.006298221796179588
},
"harness|winogrande|5": {
"acc": 0.7095501183898973,
"acc_stderr": 0.012758813448064607
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
msr_genomics_kbcomp | null | 2023-01-25T14:40:48Z | 276 | 0 | [
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"genomics-knowledge-base-bompletion",
"region:us"
]
| [
"other"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: MsrGenomicsKbcomp
tags:
- genomics-knowledge-base-bompletion
dataset_info:
features:
- name: GENE1
dtype: string
- name: relation
dtype:
class_label:
names:
'0': Positive_regulation
'1': Negative_regulation
'2': Family
- name: GENE2
dtype: string
splits:
- name: train
num_bytes: 256789
num_examples: 12160
- name: test
num_bytes: 58116
num_examples: 2784
- name: validation
num_bytes: 27457
num_examples: 1315
download_size: 0
dataset_size: 342362
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](https://msropendata.com/datasets/80b4f6e8-5d7c-4abc-9c79-2e51dfedd791)
- **Repository:** [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](NCI-PID-PubMed Genomics Knowledge Base Completion Dataset)
- **Paper:** [Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text](https://www.aclweb.org/anthology/P16-1136/)
- **Point of Contact:** [Kristina Toutanova](mailto:[email protected])
### Dataset Summary
The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text” (Toutanova, Lin, Yih, Poon, and Quirk, 2016). More details can be found in the included README.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
NCI-PID-PubMed Genomics Knowledge Base Completion Dataset
This dataset includes a database of regulation relationships among genes and corresponding textual mentions of pairs of genes in PubMed article abstracts.
The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome. This dataset was used in the paper "Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text".
FILE FORMAT DETAILS
The files train.txt, valid.txt, and test.text contain the training, development, and test set knowledge base (database of regulation relationships) triples used in.
The file text.txt contains the textual triples derived from PubMed via entity linking and processing with Literome. The textual mentions were used for knowledge base completion in.
The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set).
The format is:
| GENE1 | relation | GENE2 |
Example:
ABL1 Positive_regulation CDK2
The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set).
### Data Instances
[More Information Needed]
### Data Fields
The format is:
| GENE1 | relation | GENE2 |
### Data Splits
[More Information Needed]
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
[More Information Needed]
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
[More Information Needed]
### Dataset Curators
The dataset was initially created by Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon and Chris Quirk, during work done at Microsoft Research.
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{toutanova-etal-2016-compositional,
title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text",
author = "Toutanova, Kristina and
Lin, Victoria and
Yih, Wen-tau and
Poon, Hoifung and
Quirk, Chris",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P16-1136",
doi = "10.18653/v1/P16-1136",
pages = "1434--1444",
}
```
### Contributions
Thanks to [@manandey](https://github.com/manandey) for adding this dataset. | [
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|
py_ast | null | 2022-11-18T21:40:05Z | 276 | 3 | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:fill-mask",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:code",
"license:bsd-2-clause",
"license:mit",
"code-modeling",
"code-generation",
"region:us"
]
| [
"text2text-generation",
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
pretty_name: PyAst
annotations_creators:
- machine-generated
language_creators:
- found
language:
- code
license:
- bsd-2-clause
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
- text-generation
- fill-mask
task_ids: []
paperswithcode_id: null
tags:
- code-modeling
- code-generation
dataset_info:
features:
- name: ast
sequence:
- name: type
dtype: string
- name: value
dtype: string
- name: children
sequence: int32
config_name: ast
splits:
- name: train
num_bytes: 1870790180
num_examples: 100000
- name: test
num_bytes: 907514993
num_examples: 50000
download_size: 526642289
dataset_size: 2778305173
---
# Dataset Card for [py_ast]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **homepage**: [py150](https://www.sri.inf.ethz.ch/py150)
- **Paper**: [Probabilistic Model for Code with Decision Trees](https://www.semanticscholar.org/paper/Probabilistic-model-for-code-with-decision-trees-Raychev-Bielik/62e176977d439aac2e2d7eca834a7a99016dfcaf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset consists of parsed ASTs that were used to train and evaluate the DeepSyn tool.
The Python programs are collected from GitHub repositories
by removing duplicate files, removing project forks (copy of another existing repository),
keeping only programs that parse and have at most 30'000 nodes in the AST and
we aim to remove obfuscated files
### Supported Tasks and Leaderboards
Code Representation, Unsupervised Learning
### Languages
Python
## Dataset Structure
### Data Instances
A typical datapoint contains an AST of a python program, parsed.
The main key is `ast` wherein every program's AST is stored.
Each children would have,
`type` which will formulate the type of the node.
`children` which enumerates if a given node has children(non-empty list).
`value`, if the given node has any hardcoded value(else "N/A").
An example would be,
'''
[ {"type":"Module","children":[1,4]},{"type":"Assign","children":[2,3]},{"type":"NameStore","value":"x"},{"type":"Num","value":"7"}, {"type":"Print","children":[5]}, {"type":"BinOpAdd","children":[6,7]}, {"type":"NameLoad","value":"x"}, {"type":"Num","value":"1"} ]
'''
### Data Fields
- `ast`: a list of dictionaries, wherein every dictionary is a node in the Abstract Syntax Tree.
- `type`: explains the type of the node.
- `children`: list of nodes which are children under the given
- `value`: hardcoded value, if the node holds an hardcoded value.
### Data Splits
The data is split into a training and test set.
The final split sizes are as follows:
| | train | validation |
|------------------|--------:|------------:|
| py_ast examples | 100000 | 50000 |
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Raychev, V., Bielik, P., and Vechev, M
### Licensing Information
MIT, BSD and Apache
### Citation Information
@InProceedings{OOPSLA ’16, ACM,
title = {Probabilistic Model for Code with Decision Trees.},
authors={Raychev, V., Bielik, P., and Vechev, M.},
year={2016}
}
```
@inproceedings{10.1145/2983990.2984041,
author = {Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
title = {Probabilistic Model for Code with Decision Trees},
year = {2016},
isbn = {9781450344449},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2983990.2984041},
doi = {10.1145/2983990.2984041},
booktitle = {Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications},
pages = {731–747},
numpages = {17},
keywords = {Code Completion, Decision Trees, Probabilistic Models of Code},
location = {Amsterdam, Netherlands},
series = {OOPSLA 2016}
}
```
### Contributions
Thanks to [@reshinthadithyan](https://github.com/reshinthadithyan) for adding this dataset. | [
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|
smartdata | null | 2023-01-25T14:44:26Z | 276 | 1 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: SmartData
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-DATE
'2': I-DATE
'3': B-DISASTER_TYPE
'4': I-DISASTER_TYPE
'5': B-DISTANCE
'6': I-DISTANCE
'7': B-DURATION
'8': I-DURATION
'9': B-LOCATION
'10': I-LOCATION
'11': B-LOCATION_CITY
'12': I-LOCATION_CITY
'13': B-LOCATION_ROUTE
'14': I-LOCATION_ROUTE
'15': B-LOCATION_STOP
'16': I-LOCATION_STOP
'17': B-LOCATION_STREET
'18': I-LOCATION_STREET
'19': B-NUMBER
'20': I-NUMBER
'21': B-ORGANIZATION
'22': I-ORGANIZATION
'23': B-ORGANIZATION_COMPANY
'24': I-ORGANIZATION_COMPANY
'25': B-ORG_POSITION
'26': I-ORG_POSITION
'27': B-PERSON
'28': I-PERSON
'29': B-TIME
'30': I-TIME
'31': B-TRIGGER
'32': I-TRIGGER
config_name: smartdata-v3_20200302
splits:
- name: train
num_bytes: 2124312
num_examples: 1861
- name: test
num_bytes: 266529
num_examples: 230
- name: validation
num_bytes: 258681
num_examples: 228
download_size: 18880782
dataset_size: 2649522
---
# Dataset Card for SmartData
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.dfki.de/web/forschung/projekte-publikationen/publikationen-uebersicht/publikation/9427/
- **Repository:** https://github.com/DFKI-NLP/smartdata-corpus
- **Paper:** https://www.dfki.de/fileadmin/user_upload/import/9427_lrec_smartdata_corpus.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
DFKI SmartData Corpus is a dataset of 2598 German-language documents
which has been annotated with fine-grained geo-entities, such as streets,
stops and routes, as well as standard named entity types. It has also
been annotated with a set of 15 traffic- and industry-related n-ary
relations and events, such as Accidents, Traffic jams, Acquisitions,
and Strikes. The corpus consists of newswire texts, Twitter messages,
and traffic reports from radio stations, police and railway companies.
It allows for training and evaluating both named entity recognition
algorithms that aim for fine-grained typing of geo-entities, as well
as n-ary relation extraction systems.
### Supported Tasks and Leaderboards
NER
### Languages
German
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- id: an identifier for the article the text came from
- tokens: a list of string tokens for the text of the article
- ner_tags: a corresponding list of NER tags in the BIO format
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CC-BY 4.0
### Citation Information
```
@InProceedings{SCHIERSCH18.85,
author = {Martin Schiersch and Veselina Mironova and Maximilian Schmitt and Philippe Thomas and Aleksandra Gabryszak and Leonhard Hennig},
title = "{A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events}",
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year = {2018},
month = {May 7-12, 2018},
address = {Miyazaki, Japan},
editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
publisher = {European Language Resources Association (ELRA)},
isbn = {979-10-95546-00-9},
language = {english}
}
```
### Contributions
Thanks to [@aseifert](https://github.com/aseifert) for adding this dataset. | [
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|
telugu_books | null | 2022-11-03T16:07:57Z | 276 | 2 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:te",
"license:unknown",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- te
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: TeluguBooks
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 315076011
num_examples: 25794
download_size: 0
dataset_size: 315076011
---
# Dataset Card for [telugu_books]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:**
[Telugu Books](https://www.kaggle.com/sudalairajkumar/telugu-nlp)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is created by scraping telugu novels from teluguone.com this dataset can be used for nlp tasks like topic modeling, word embeddings, transfer learning etc
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
TE - Telugu
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- Text: Sentence from a novel
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Anusha Motamarri
### Annotations
#### Annotation process
Anusha Motamarri
#### Who are the annotators?
Anusha Motamarri
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@vinaykudari](https://github.com/vinaykudari) for adding this dataset. | [
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|
GEM/RiSAWOZ | GEM | 2022-10-24T15:30:01Z | 276 | 5 | [
"task_categories:conversational",
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:zh",
"license:cc-by-4.0",
"dialog-response-generation",
"region:us"
]
| [
"conversational"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- zh
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: RiSAWOZ
tags:
- dialog-response-generation
---
# Dataset Card for GEM/RiSAWOZ
## Dataset Description
- **Homepage:** https://terryqj0107.github.io/RiSAWOZ_webpage
- **Repository:** https://github.com/terryqj0107/RiSAWOZ
- **Paper:** https://aclanthology.org/2020.emnlp-main.67
- **Leaderboard:** N/A
- **Point of Contact:** Deyi Xiong
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/RiSAWOZ).
### Dataset Summary
RiSAWOZ is a Chinese dialog dataset. It can be used to study various dialogue tasks, such as Dialogue State Tracking, Dialogue Context-to-Text Generation, Coreference Resolution and Unified Generative Ellipsis and Coreference Resolution.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/RiSAWOZ')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/RiSAWOZ).
#### website
[Website](https://terryqj0107.github.io/RiSAWOZ_webpage)
#### paper
[ACL Anthology](https://aclanthology.org/2020.emnlp-main.67)
#### authors
Jun Quan (Soochow University, Suzhou, China), Shian Zhang (Soochow University, Suzhou, China), Qian Cao(Soochow University, Suzhou, China), Zizhong Li (Tianjin University, Tianjin, China), Deyi Xiong (Tianjin University, Tianjin, China)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](https://terryqj0107.github.io/RiSAWOZ_webpage)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/terryqj0107/RiSAWOZ)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2020.emnlp-main.67)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{quan-etal-2020-risawoz,
title = "{R}i{SAWOZ}: A Large-Scale Multi-Domain {W}izard-of-{O}z Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling",
author = "Quan, Jun and
Zhang, Shian and
Cao, Qian and
Li, Zizhong and
Xiong, Deyi",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.67",
pages = "930--940",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Deyi Xiong
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
[email protected]
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
Only Mandarin Chinese is covered in this dataset.
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Mandarin Chinese`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-4.0: Creative Commons Attribution 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
RiSAWOZ can be used to support the study under various dialogue tasks, such as Natural Language Understanding, Dialogue State Tracking, Dialogue Context-to-Text Generation, Coreference Resolution and Unified Generative Ellipsis and Coreference Resolution.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Generate system response given dialogue context across multiple domains.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Soochow University and Tianjin University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Jun Quan (Soochow University, Suzhou, China), Shian Zhang (Soochow University, Suzhou, China), Qian Cao(Soochow University, Suzhou, China), Zizhong Li (Tianjin University, Tianjin, China), Deyi Xiong (Tianjin University, Tianjin, China)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
the National Key Research and Development Project
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Tianhao Shen (Tianjin University, Tianjin, China), Chaobin You (Tianjin University, Tianjin, China), Deyi Xiong (Tianjin University, Tianjin, China)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- gem_id (string): GEM-RiSAWOZ-{split}-{id}
- dialogue_id (string): dialogue ID
- goal (string): natural language descriptions of the user goal
- domains (list of strings): domains mentioned in current dialogue session
- dialogue (list of dicts): dialog turns and corresponding annotations. Each turn includes:
- turn_id (int): turn ID
- turn_domain (list of strings): domain mentioned in current turn
- user_utterance (string): user utterance
- system_utterance (string): system utterance
- belief_state (dict): dialogue state, including:
- inform slot-values (dict): the slots and corresponding values informed until current turn
- turn_inform (dict): the slots and corresponding values informed in current turn
- turn request (dict): the slots requested in current turn
- user_actions (list of lists): user dialogue acts in current turn
- user_actions (list of lists): system dialogue acts in current turn
- db_results (list of strings): database search results
- segmented_user_utterance (string): word segmentation result of user utterance
- segmented_system_utterance (string): word segmentation result of system utterance
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
[
{
"dialogue_id": "attraction_goal_4-63###6177",
"goal": "attraction_goal_4-63: 你是苏州人，但不怎么出去玩。你朋友来苏州找你，你准备带他逛逛“水乡古镇”，你希望客服给你推荐个消费水平“中等”的地方。然后你要问清楚这地方“是否地铁直达”、“特点”、“门票价格”这些信息。最后，你要感谢客服的帮助，然后说再见。",
"domains": [
"旅游景点"
],
"dialogue": [
{
"turn_id": 0,
"turn_domain": [
"旅游景点"
],
"user_utterance": "你好，我是苏州人，但是不怎么出去玩，我朋友来苏州找我了，我准备带他逛逛水乡古镇，你能帮我推荐一下吗？",
"system_utterance": "推荐您去周庄古镇。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "水乡 古镇"
},
"turn_inform": {
"旅游景点-景点类型": "水乡 古镇"
},
"turn request": []
},
"user_actions": [
[
"Inform",
"旅游景点",
"景点类型",
"水乡 古镇"
],
[
"Greeting",
"旅游景点",
"",
""
]
],
"system_actions": [
[
"Recommend",
"旅游景点",
"名称",
"周庄 古镇"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为8",
"{'名称': '周庄古镇', '区域': '昆山', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '偏贵', '是否地铁直达': '否', '门票价格': '100元', '电话号码': '400-8282900', '地址': '苏州市昆山市周庄镇全福路43号', '评分': 4.5, '开放时间': '07:30-18:50', '特点': '小桥流水与人家，双桥水巷摇橹船，还有沈万三的足迹待你寻访'}",
"{'名称': '同里古镇', '区域': '吴江', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '偏贵', '是否地铁直达': '否', '门票价格': '100元', '电话号码': '0512-63325728', '地址': '苏州市吴江区同里古镇', '评分': 4.5, '开放时间': '07:30-17:30', '特点': '五湖环抱的江南水乡古镇，走三桥，参观小巧精致的园林退思园。'}",
"{'名称': '山塘街', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-65314467', '地址': '苏州市姑苏区古城西北', '评分': 4.5, '开放时间': '08:30-21:00', '特点': '欣赏苏州老城风貌，参观会馆等古迹，乘游船，拍摄美丽的夜景。'}",
"{'名称': '平江路', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-69163907', '地址': '江苏省苏州市姑苏区白塔东路65号', '评分': 4.5, '开放时间': '全天', '特点': '漫步保存完整的古街区，看小桥流水古宅，逛文艺小店，吃美食。'}",
"{'名称': '木渎古镇', '区域': '吴中区', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '便宜', '是否地铁直达': '否', '门票价格': '免费', '电话号码': '0512-66514042', '地址': '苏州市吴中区木渎镇山塘街188号', '评分': 4.4, '开放时间': '08:30-17:00', '特点': '以园林为特色的古镇，游严家花园等古典园林，坐船看小桥流水。'}",
"{'名称': '甪直古镇', '区域': '吴中区', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '便宜', '是否地铁直达': '否', '门票价格': '免费', '电话号码': '0512-66191668', '地址': '苏州市吴中区甪直镇晓市路21号', '评分': 4.3, '开放时间': '07:30-17:30', '特点': '甪直古镇有2500多年历史，甪直境内水流纵横，桥梁密布，有五湖之厅、六泽之冲之称。'}",
"{'名称': '千灯古镇', '区域': '昆山', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '便宜', '是否地铁直达': '否', '门票价格': '免费', '电话号码': '0512-57472155', '地址': '苏州市昆山市千灯古镇尚书路1号', '评分': 4.3, '开放时间': '08:00-17:00', '特点': '千灯古镇，距今已有2500多年的历史，古镇白墙黑瓦，昆韵盎然。'}",
"{'名称': '锦溪古镇', '区域': '昆山', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '中等', '是否地铁直达': '否', '门票价格': '65元', '电话号码': '0512-57224669', '地址': '苏州市昆山市锦溪镇邵甸港路18号', '评分': 4.4, '开放时间': '08:00-17:00', '特点': '锦溪古镇位于昆山南郊的淀山湖畔，是一座有千年历史的江南水乡。'}"
],
"segmented_user_utterance": "你好 ， 我 是 苏州人 ， 但是 不怎么 出去玩 ， 我 朋友 来 苏州 找 我 了 ， 我 准备 带 他 逛逛 水乡 古镇 ， 你 能 帮 我 推荐 一下 吗 ？",
"segmented_system_utterance": "推荐 您 去 周庄 古镇 。"
},
{
"turn_id": 1,
"turn_domain": [
"旅游景点"
],
"user_utterance": "我希望这个地方消费水平适中的，这个是中等的吗？",
"system_utterance": "这个不是，我又重新帮您找了一家山塘街。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "水乡 古镇",
"旅游景点-消费": "中等"
},
"turn_inform": {
"旅游景点-消费": "中等"
},
"turn request": []
},
"user_actions": [
[
"Inform",
"旅游景点",
"消费",
"中等"
]
],
"system_actions": [
[
"Recommend",
"旅游景点",
"名称",
"山塘 街"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为3",
"{'名称': '山塘街', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-65314467', '地址': '苏州市姑苏区古城西北', '评分': 4.5, '开放时间': '08:30-21:00', '特点': '欣赏苏州老城风貌，参观会馆等古迹，乘游船，拍摄美丽的夜景。'}",
"{'名称': '平江路', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-69163907', '地址': '江苏省苏州市姑苏区白塔东路65号', '评分': 4.5, '开放时间': '全天', '特点': '漫步保存完整的古街区，看小桥流水古宅，逛文艺小店，吃美食。'}",
"{'名称': '锦溪古镇', '区域': '昆山', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '中等', '是否地铁直达': '否', '门票价格': '65元', '电话号码': '0512-57224669', '地址': '苏州市昆山市锦溪镇邵甸港路18号', '评分': 4.4, '开放时间': '08:00-17:00', '特点': '锦溪古镇位于昆山南郊的淀山湖畔，是一座有千年历史的江南水乡。'}"
],
"segmented_user_utterance": "我 希望 这个 地方 消费水平 适中 的 ， 这个 是 中等 的 吗 ？",
"segmented_system_utterance": "这个 不是 ， 我 又 重新 帮 您 找 了 一家 山塘 街 。"
},
{
"turn_id": 2,
"turn_domain": [
"旅游景点"
],
"user_utterance": "听这地方应该挺好玩，他们家特点是什么？",
"system_utterance": "特点是欣赏苏州老城风貌，参观会馆等古迹，乘游船，拍摄美丽的夜景。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "水乡 古镇",
"旅游景点-消费": "中等",
"旅游景点-名称": "山塘 街"
},
"turn_inform": {
"旅游景点-名称": "山塘 街"
},
"turn request": [
"特点"
]
},
"user_actions": [
[
"Inform",
"旅游景点",
"名称",
"山塘 街"
],
[
"Request",
"旅游景点",
"特点",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"特点",
"欣赏 苏州 老城 风貌 ， 参观 会馆 等 古迹 ， 乘 游船 ， 拍摄 美丽 的 夜景"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '山塘街', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-65314467', '地址': '苏州市姑苏区古城西北', '评分': 4.5, '开放时间': '08:30-21:00', '特点': '欣赏苏州老城风貌，参观会馆等古迹，乘游船，拍摄美丽的夜景。'}"
],
"segmented_user_utterance": "听 这 地方 应该 挺好玩 ， 他们 家 特点 是 什么 ？",
"segmented_system_utterance": "特点 是 欣赏 苏州 老城 风貌 ， 参观 会馆 等 古迹 ， 乘 游船 ， 拍摄 美丽 的 夜景 。"
},
{
"turn_id": 3,
"turn_domain": [
"旅游景点"
],
"user_utterance": "听起来很不错，他们家门票是多少？",
"system_utterance": "门票免费。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "水乡 古镇",
"旅游景点-消费": "中等",
"旅游景点-名称": "山塘 街"
},
"turn_inform": {},
"turn request": [
"门票价格"
]
},
"user_actions": [
[
"Request",
"旅游景点",
"门票价格",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"门票价格",
"免费"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '山塘街', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-65314467', '地址': '苏州市姑苏区古城西北', '评分': 4.5, '开放时间': '08:30-21:00', '特点': '欣赏苏州老城风貌，参观会馆等古迹，乘游船，拍摄美丽的夜景。'}"
],
"segmented_user_utterance": "听 起来 很 不错 ， 他们 家 门票 是 多少 ？",
"segmented_system_utterance": "门票 免费 。"
},
{
"turn_id": 4,
"turn_domain": [
"旅游景点"
],
"user_utterance": "还行，那他们家是否有地铁直达呢？",
"system_utterance": "嗯嗯，可以地铁直达。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "水乡 古镇",
"旅游景点-消费": "中等",
"旅游景点-名称": "山塘 街"
},
"turn_inform": {},
"turn request": [
"是否地铁直达"
]
},
"user_actions": [
[
"Request",
"旅游景点",
"是否地铁直达",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"是否地铁直达",
"是"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '山塘街', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-65314467', '地址': '苏州市姑苏区古城西北', '评分': 4.5, '开放时间': '08:30-21:00', '特点': '欣赏苏州老城风貌，参观会馆等古迹，乘游船，拍摄美丽的夜景。'}"
],
"segmented_user_utterance": "还 行 ， 那 他们 家 是否 有 地铁 直达 呢 ？",
"segmented_system_utterance": "嗯 嗯 ， 可以 地铁 直达 。"
},
{
"turn_id": 5,
"turn_domain": [
"通用"
],
"user_utterance": "谢谢你的帮助，再见！",
"system_utterance": "再见。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "水乡 古镇",
"旅游景点-消费": "中等",
"旅游景点-名称": "山塘 街"
},
"turn_inform": {},
"turn request": []
},
"user_actions": [
[
"Bye",
"通用",
"",
""
]
],
"system_actions": [
[
"Bye",
"通用",
"",
""
]
],
"db_results": [],
"segmented_user_utterance": "谢谢 你 的 帮助 ， 再见 ！",
"segmented_system_utterance": "再见 。"
}
]
},
{
"dialogue_id": "attraction_goal_3-33_v2###7307",
"goal": "attraction_goal_3-33: 你和朋友一起来苏州出差，顺便想去“苏州乐园森林水世界”逛一逛。因此，你要询问客服这个地方“是否地铁直达”、“评分”、“特点”这些信息。最后，你向客服表示感谢，结束对话。",
"domains": [
"旅游景点"
],
"dialogue": [
{
"turn_id": 0,
"turn_domain": [
"旅游景点"
],
"user_utterance": "你好，我和朋友一起来苏州出差，顺便想去苏州乐园森林水世界玩玩，能否帮我看看这个地方是否有地铁直达呢？",
"system_utterance": "稍等，很抱歉，没有地铁能直接到这个景点呢。",
"belief_state": {
"inform slot-values": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn_inform": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn request": [
"是否地铁直达"
]
},
"user_actions": [
[
"Inform",
"旅游景点",
"名称",
"苏州 乐园 森林 水 世界"
],
[
"Request",
"旅游景点",
"是否地铁直达",
""
],
[
"Greeting",
"旅游景点",
"",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"是否地铁直达",
"否"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '苏州乐园森林水世界', '区域': '高新区', '景点类型': '游乐场', '最适合人群': '情侣约会', '消费': '偏贵', '是否地铁直达': '否', '门票价格': '198元', '电话号码': '0512-68717107', '地址': '苏州市高新区阳山东路以西，山神湾路以东', '评分': 4.4, '开放时间': '09:00-17:00', '特点': '各项水上娱乐项目皆具森林特色，被赋予森林主题，让人身处浩瀚森林又能享受到海洋般的舒爽水游乐。'}"
],
"segmented_user_utterance": "你好 ， 我 和 朋友 一 起来 苏州 出差 ， 顺便 想 去 苏州 乐园 森林 水 世界 玩玩 ， 能否 帮 我 看看 这个 地方 是否 有 地铁 直达 呢 ？",
"segmented_system_utterance": "稍 等 ， 很 抱歉 ， 没有 地铁 能 直接 到 这个 景点 呢 。"
},
{
"turn_id": 1,
"turn_domain": [
"旅游景点"
],
"user_utterance": "你再帮我看看景点的评分好吗？",
"system_utterance": "这个景点的评分是4.4分。",
"belief_state": {
"inform slot-values": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn_inform": {},
"turn request": [
"评分"
]
},
"user_actions": [
[
"Request",
"旅游景点",
"评分",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"评分",
"4.4"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '苏州乐园森林水世界', '区域': '高新区', '景点类型': '游乐场', '最适合人群': '情侣约会', '消费': '偏贵', '是否地铁直达': '否', '门票价格': '198元', '电话号码': '0512-68717107', '地址': '苏州市高新区阳山东路以西，山神湾路以东', '评分': 4.4, '开放时间': '09:00-17:00', '特点': '各项水上娱乐项目皆具森林特色，被赋予森林主题，让人身处浩瀚森林又能享受到海洋般的舒爽水游乐。'}"
],
"segmented_user_utterance": "你 再 帮 我 看看 景点 的 评分 好 吗 ？",
"segmented_system_utterance": "这个 景点 的 评分 是 4.4 分 。"
},
{
"turn_id": 2,
"turn_domain": [
"旅游景点"
],
"user_utterance": "这个景点有啥特点呢？",
"system_utterance": "这个景点的特点是各项水上娱乐项目皆具森林特色，被赋予森林主题，让人身处浩瀚森林又能享受到海洋般的舒爽水游乐。",
"belief_state": {
"inform slot-values": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn_inform": {},
"turn request": [
"特点"
]
},
"user_actions": [
[
"Request",
"旅游景点",
"特点",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"特点",
"各项 水上 娱乐 项目 皆 具 森林 特色 ， 被 赋予 森林 主题 ， 让 人 身处 浩瀚 森林 又 能 享受 到 海洋 般的 舒爽 水 游乐 。"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '苏州乐园森林水世界', '区域': '高新区', '景点类型': '游乐场', '最适合人群': '情侣约会', '消费': '偏贵', '是否地铁直达': '否', '门票价格': '198元', '电话号码': '0512-68717107', '地址': '苏州市高新区阳山东路以西，山神湾路以东', '评分': 4.4, '开放时间': '09:00-17:00', '特点': '各项水上娱乐项目皆具森林特色，被赋予森林主题，让人身处浩瀚森林又能享受到海洋般的舒爽水游乐。'}"
],
"segmented_user_utterance": "这个 景点 有 啥 特点 呢 ？",
"segmented_system_utterance": "这个 景点 的 特点 是 各项 水上 娱乐 项目 皆 具 森林 特色 ， 被 赋予 森林 主题 ， 让 人 身处 浩瀚 森林 又 能 享受 到 海洋 般的 舒爽 水 游乐 。"
},
{
"turn_id": 3,
"turn_domain": [
"通用"
],
"user_utterance": "太好了，我觉得我会很喜欢这个地方，哈哈。",
"system_utterance": "这个景点挺不错的，就是没有地铁需要您乘坐其它交通工具了。",
"belief_state": {
"inform slot-values": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn_inform": {},
"turn request": []
},
"user_actions": [
[
"General",
"通用",
"",
""
]
],
"system_actions": [
[
"General",
"通用",
"",
""
]
],
"db_results": [],
"segmented_user_utterance": "太好了 ， 我 觉得 我会 很 喜欢 这个 地方 ， 哈哈 。",
"segmented_system_utterance": "这个 景点 挺不错 的 ， 就是 没有 地铁 需要 您 乘坐 其它 交通工具 了 。"
},
{
"turn_id": 4,
"turn_domain": [
"通用"
],
"user_utterance": "我打出租车去吧，就是多花点车费，没事。",
"system_utterance": "请问您还有其他问题吗？",
"belief_state": {
"inform slot-values": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn_inform": {},
"turn request": []
},
"user_actions": [
[
"General",
"通用",
"",
""
]
],
"system_actions": [
[
"General",
"通用",
"",
""
]
],
"db_results": [],
"segmented_user_utterance": "我 打 出租车 去 吧 ， 就是 多花点 车费 ， 没事 。",
"segmented_system_utterance": "请问 您 还有 其他 问题 吗 ？"
},
{
"turn_id": 5,
"turn_domain": [
"通用"
],
"user_utterance": "没有了。谢谢你，再见。",
"system_utterance": "再见。",
"belief_state": {
"inform slot-values": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn_inform": {},
"turn request": []
},
"user_actions": [
[
"Bye",
"通用",
"",
""
]
],
"system_actions": [
[
"Bye",
"通用",
"",
""
]
],
"db_results": [],
"segmented_user_utterance": "没有 了 。 谢谢 你 ， 再见 。",
"segmented_system_utterance": "再见 。"
}
]
}
]
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- Train:
- dialogues: 10000
- turns: 134580
- Avg. turns: 13.5
- Valid:
- dialogues: 600
- turns: 8116
- Avg. turns: 13.5
- Test:
- dialogues: 600
- turn: 9286
- Avg. turns: 15.5
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
the most complex example (i.e. with the most domains):
```
{
"dialogue_id": "attraction_restaurant_hotel_goal_2-69_v2###8355",
"goal": "attraction_restaurant_hotel_goal_2-69: 公司派你去苏州出差，你准备抽空旅游一下。第一，你想去一个“山水景区”，消费水平最好是“偏贵”的，你需要问清楚这地方“开放时间”、“门票价格”这些信息。第二，你要找个地方住，让客服给你推荐一家价位“中等”的“商务出行”酒店，并且询问这家酒店的“停车场”、“电话号码”这些信息。第三，你还想拔草美食，问客服有没有价位“中等”的“江浙菜”餐厅推荐，然后了解这家餐厅的“是否地铁直达”、“人均消费”这些信息。最后你要感谢客服的帮助，并说再见。",
"domains": [
"旅游景点",
"餐厅",
"酒店"
],
"dialogue": [
{
"turn_id": 0,
"turn_domain": [
"旅游景点"
],
"user_utterance": "你好，来苏州出差，准备抽空旅游一下。我想去山水景区，消费水平偏贵点，帮忙推荐一个。",
"system_utterance": "你好，向您推荐金鸡湖景区，感受一下苏州现代化的一面。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵"
},
"turn_inform": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵"
},
"turn request": []
},
"user_actions": [
[
"Inform",
"旅游景点",
"景点类型",
"山水 景区"
],
[
"Inform",
"旅游景点",
"消费",
"偏贵"
],
[
"Greeting",
"旅游景点",
"",
""
],
[
"General",
"旅游景点",
"",
""
]
],
"system_actions": [
[
"Greeting",
"通用",
"",
""
],
[
"General",
"通用",
"",
""
],
[
"Recommend",
"旅游景点",
"名称",
"金鸡湖 景区"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为2",
"{'名称': '金鸡湖景区', '区域': '工业园区', '景点类型': '山水景区', '最适合人群': '情侣约会', '消费': '偏贵', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '400-7558558', '地址': '苏州市工业园区星港街158号', '评分': 4.5, '开放时间': '全天', '特点': '看东方之门等高楼，坐摩天轮，乘船夜游，感受苏州现代化的一面。'}",
"{'名称': '沙家浜风景区', '区域': '常熟', '景点类型': '山水景区', '最适合人群': '家庭亲子', '消费': '偏贵', '是否地铁直达': '否', '门票价格': '110元', '电话号码': '0512-52500000', '地址': '苏州市常熟市沙家浜镇', '评分': 4.5, '开放时间': '08:00-16:30', '特点': '京剧样板戏《沙家浜》的发源地，访剧中场景，坐船深入芦苇荡。'}"
],
"segmented_user_utterance": "你好 ， 来 苏州 出差 ， 准备 抽空 旅游 一下 。 我 想 去 山水 景区 ， 消费水平 偏 贵点 ， 帮忙 推荐 一个 。",
"segmented_system_utterance": "你好 ， 向 您 推荐 金鸡湖 景区 ， 感受一下 苏州 现代化 的 一面 。"
},
{
"turn_id": 1,
"turn_domain": [
"旅游景点"
],
"user_utterance": "这里开放时间？需要安排一下。",
"system_utterance": "景区是全天开放的，可以尽情游览！",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区"
},
"turn_inform": {
"旅游景点-名称": "金鸡湖 景区"
},
"turn request": [
"开放时间"
]
},
"user_actions": [
[
"Inform",
"旅游景点",
"名称",
"金鸡湖 景区"
],
[
"Request",
"旅游景点",
"开放时间",
""
],
[
"General",
"旅游景点",
"",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"开放时间",
"全天"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '金鸡湖景区', '区域': '工业园区', '景点类型': '山水景区', '最适合人群': '情侣约会', '消费': '偏贵', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '400-7558558', '地址': '苏州市工业园区星港街158号', '评分': 4.5, '开放时间': '全天', '特点': '看东方之门等高楼，坐摩天轮，乘船夜游，感受苏州现代化的一面。'}"
],
"segmented_user_utterance": "这里 开放 时间 ？ 需要 安排 一下 。",
"segmented_system_utterance": "景区 是 全天 开放 的 ， 可以 尽情 游览 ！"
},
{
"turn_id": 2,
"turn_domain": [
"旅游景点"
],
"user_utterance": "门票多少钱？",
"system_utterance": "这里是免费景区。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区"
},
"turn_inform": {},
"turn request": [
"门票价格"
]
},
"user_actions": [
[
"Request",
"旅游景点",
"门票价格",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"门票价格",
"免费"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '金鸡湖景区', '区域': '工业园区', '景点类型': '山水景区', '最适合人群': '情侣约会', '消费': '偏贵', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '400-7558558', '地址': '苏州市工业园区星港街158号', '评分': 4.5, '开放时间': '全天', '特点': '看东方之门等高楼，坐摩天轮，乘船夜游，感受苏州现代化的一面。'}"
],
"segmented_user_utterance": "门票 多少 钱 ？",
"segmented_system_utterance": "这里 是 免费 景区 。"
},
{
"turn_id": 3,
"turn_domain": [
"酒店"
],
"user_utterance": "哦，好的。还有酒店，帮我推荐个价位中等的商务酒店！",
"system_utterance": "推荐苏州金鸡湖李公堤瑞贝庭公寓酒店，在金鸡湖景区附近。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行"
},
"turn_inform": {
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行"
},
"turn request": []
},
"user_actions": [
[
"Inform",
"酒店",
"价位",
"中等"
],
[
"Inform",
"酒店",
"酒店类型",
"商务 出行"
],
[
"General",
"酒店",
"",
""
]
],
"system_actions": [
[
"Recommend",
"酒店",
"名称",
"苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
],
[
"General",
"通用",
"",
""
]
],
"db_results": [
"数据库检索结果：成功匹配个数为16",
"{'名称': '苏州慢享主题酒店', '区域': '姑苏区', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '收费', '房费': '266元', '地址': '苏州姑苏区景德路26-64号', '电话号码': '0512-67570999', '评分': 4.7}",
"{'名称': '苏州慢享主题酒店', '区域': '姑苏区', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '收费', '房费': '278元', '地址': '苏州姑苏区景德路26-64号', '电话号码': '0512-67570999', '评分': 4.7}",
"{'名称': '美锦酒店', '区域': '高新区', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '308元', '地址': '苏州高新区滨河路999号花样年喜年生活广场5栋1层', '电话号码': '0512-66053331', '评分': 4.8}",
"{'名称': '美锦酒店', '区域': '高新区', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '349元', '地址': '苏州高新区滨河路999号花样年喜年生活广场5栋1层', '电话号码': '0512-66053331', '评分': 4.8}",
"{'名称': '苏州金鸡湖李公堤瑞贝庭公寓酒店', '区域': '工业园区', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '438元', '地址': '苏州工业园区李公堤三期E区商业街9幢', '电话号码': '0512-69995666', '评分': 4.6}",
"{'名称': '苏州金鸡湖李公堤瑞贝庭公寓酒店', '区域': '工业园区', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '438元', '地址': '苏州工业园区李公堤三期E区商业街9幢', '电话号码': '0512-69995666', '评分': 4.6}",
"{'名称': '苏州途乐酒店公寓', '区域': '工业园区', '星级': '2', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '收费', '房费': '486元', '地址': '苏州工业园区苏州丰隆城市中心T1楼', '电话号码': '151-5149-7911', '评分': 4.6}",
"{'名称': '苏州途乐酒店公寓', '区域': '工业园区', '星级': '2', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '收费', '房费': '486元', '地址': '苏州工业园区苏州丰隆城市中心T1楼', '电话号码': '151-5149-7911', '评分': 4.6}",
"{'名称': '万悦酒店', '区域': '吴中区', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '346元', '地址': '苏州吴中区金山路47-2号', '电话号码': '0512-83808380', '评分': 4.5}",
"{'名称': '万悦酒店', '区域': '吴中区', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '338元', '地址': '苏州吴中区金山路47-2号', '电话号码': '0512-83808380', '评分': 4.5}",
"{'名称': '周庄多瓦台临河客栈', '区域': '昆山', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '收费', '房费': '279元', '地址': '昆山周庄镇东浜村75号', '电话号码': '181-3619-1632', '评分': 4.8}",
"{'名称': '周庄多瓦台临河客栈', '区域': '昆山', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '收费', '房费': '279元', '地址': '昆山周庄镇东浜村75号', '电话号码': '181-3619-1632', '评分': 4.8}",
"{'名称': '常熟长江路亚朵酒店', '区域': '常熟', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '312元', '地址': '常熟长江路268号', '电话号码': '0512-51661666', '评分': 4.8}",
"{'名称': '常熟长江路亚朵酒店', '区域': '常熟', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '312元', '地址': '常熟长江路268号', '电话号码': '0512-51661666', '评分': 4.8}",
"{'名称': '维也纳酒店', '区域': '常熟', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '322元', '地址': '常熟碧溪镇金港路8号', '电话号码': '0512-52018188', '评分': 4.8}",
"{'名称': '维也纳酒店', '区域': '常熟', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '340元', '地址': '常熟碧溪镇金港路8号', '电话号码': '0512-52018188', '评分': 4.8}"
],
"segmented_user_utterance": "哦 ， 好 的 。 还有 酒店 ， 帮 我 推荐 个 价位 中等 的 商务酒店 ！",
"segmented_system_utterance": "推荐 苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店 ， 在 金鸡湖 景区 附近 。"
},
{
"turn_id": 4,
"turn_domain": [
"酒店"
],
"user_utterance": "你想得真周到!这家酒店有停车场吗？",
"system_utterance": "酒店配备有一个免费的停车场。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行",
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn_inform": {
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn request": [
"停车场"
]
},
"user_actions": [
[
"Inform",
"酒店",
"名称",
"苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
],
[
"Request",
"酒店",
"停车场",
""
]
],
"system_actions": [
[
"Inform",
"酒店",
"停车场",
"免费"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为2",
"{'名称': '苏州金鸡湖李公堤瑞贝庭公寓酒店', '区域': '工业园区', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '438元', '地址': '苏州工业园区李公堤三期E区商业街9幢', '电话号码': '0512-69995666', '评分': 4.6}",
"{'名称': '苏州金鸡湖李公堤瑞贝庭公寓酒店', '区域': '工业园区', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '438元', '地址': '苏州工业园区李公堤三期E区商业街9幢', '电话号码': '0512-69995666', '评分': 4.6}"
],
"segmented_user_utterance": "你 想 得 真 周到 ! 这家 酒店 有 停车场 吗 ？",
"segmented_system_utterance": "酒店 配备 有 一个 免费 的 停车场 。"
},
{
"turn_id": 5,
"turn_domain": [
"酒店"
],
"user_utterance": "酒店的电话告诉我一下。",
"system_utterance": "电话号码：0512-69995666。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行",
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn_inform": {},
"turn request": [
"电话号码"
]
},
"user_actions": [
[
"Request",
"酒店",
"电话号码",
""
]
],
"system_actions": [
[
"Inform",
"酒店",
"电话号码",
"0512 - 69995666"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为2",
"{'名称': '苏州金鸡湖李公堤瑞贝庭公寓酒店', '区域': '工业园区', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '438元', '地址': '苏州工业园区李公堤三期E区商业街9幢', '电话号码': '0512-69995666', '评分': 4.6}",
"{'名称': '苏州金鸡湖李公堤瑞贝庭公寓酒店', '区域': '工业园区', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '438元', '地址': '苏州工业园区李公堤三期E区商业街9幢', '电话号码': '0512-69995666', '评分': 4.6}"
],
"segmented_user_utterance": "酒店 的 电话 告诉 我 一下 。",
"segmented_system_utterance": "电话号码 ： 0512 - 69995666 。"
},
{
"turn_id": 6,
"turn_domain": [
"餐厅"
],
"user_utterance": "嗯，我还想拔草美食。有没有价位中等的江浙菜餐厅推荐一个。",
"system_utterance": "推荐老枫庭。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"餐厅-菜系": "江浙 菜",
"餐厅-价位": "中等",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行",
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn_inform": {
"餐厅-菜系": "江浙 菜",
"餐厅-价位": "中等"
},
"turn request": []
},
"user_actions": [
[
"Inform",
"餐厅",
"菜系",
"江浙 菜"
],
[
"Inform",
"餐厅",
"价位",
"中等"
],
[
"General",
"餐厅",
"",
""
]
],
"system_actions": [
[
"Recommend",
"餐厅",
"名称",
"老枫庭"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为3",
"{'名称': '老枫庭', '区域': '吴中区', '菜系': '江浙菜', '价位': '中等', '是否地铁直达': '否', '人均消费': '94元', '地址': '木渎灵天路55号芭提雅休闲娱乐广场5号楼', '电话号码': '0512-66277557', '评分': 4.6, '营业时间': '周一至周日 11:00-13:30 17:00-20:30', '推荐菜': '松鼠桂鱼'}",
"{'名称': '同里老家', '区域': '吴江', '菜系': '江浙菜', '价位': '中等', '是否地铁直达': '否', '人均消费': '87元', '地址': '同里镇环湖西路318号', '电话号码': '0512-63339777', '评分': 4.5, '营业时间': '周一至周日 10:00-22:00', '推荐菜': '太湖三白'}",
"{'名称': '周庄花间堂桔梗餐厅', '区域': '昆山', '菜系': '江浙菜', '价位': '中等', '是否地铁直达': '否', '人均消费': '98元', '地址': '周庄镇中市街110号景点迷楼北10米', '电话号码': '138-1289-9961', '评分': 4.5, '营业时间': '周一至周日 07:00-20:30', '推荐菜': '银鱼炒蛋'}"
],
"segmented_user_utterance": "嗯 ， 我 还 想 拔草 美食 。 有没有 价位 中等 的 江浙 菜 餐厅 推荐 一个 。",
"segmented_system_utterance": "推荐 老枫庭 。"
},
{
"turn_id": 7,
"turn_domain": [
"餐厅"
],
"user_utterance": "这家餐厅地铁直达吗？",
"system_utterance": "抱歉，餐厅没有地铁直达!",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"餐厅-菜系": "江浙 菜",
"餐厅-价位": "中等",
"餐厅-名称": "老枫庭",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行",
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn_inform": {
"餐厅-名称": "老枫庭"
},
"turn request": [
"是否地铁直达"
]
},
"user_actions": [
[
"Inform",
"餐厅",
"名称",
"老枫庭"
],
[
"Request",
"餐厅",
"是否地铁直达",
""
]
],
"system_actions": [
[
"Inform",
"餐厅",
"是否地铁直达",
"否"
],
[
"General",
"通用",
"",
""
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '老枫庭', '区域': '吴中区', '菜系': '江浙菜', '价位': '中等', '是否地铁直达': '否', '人均消费': '94元', '地址': '木渎灵天路55号芭提雅休闲娱乐广场5号楼', '电话号码': '0512-66277557', '评分': 4.6, '营业时间': '周一至周日 11:00-13:30 17:00-20:30', '推荐菜': '松鼠桂鱼'}"
],
"segmented_user_utterance": "这家 餐厅 地铁 直达 吗 ？",
"segmented_system_utterance": "抱歉 ， 餐厅 没有 地铁 直达 !"
},
{
"turn_id": 8,
"turn_domain": [
"餐厅"
],
"user_utterance": "人均消费呢？",
"system_utterance": "老枫庭人均消费在94元左右。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"餐厅-菜系": "江浙 菜",
"餐厅-价位": "中等",
"餐厅-名称": "老枫庭",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行",
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn_inform": {},
"turn request": [
"人均消费"
]
},
"user_actions": [
[
"Request",
"餐厅",
"人均消费",
""
]
],
"system_actions": [
[
"Inform",
"餐厅",
"人均消费",
"老枫庭"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '老枫庭', '区域': '吴中区', '菜系': '江浙菜', '价位': '中等', '是否地铁直达': '否', '人均消费': '94元', '地址': '木渎灵天路55号芭提雅休闲娱乐广场5号楼', '电话号码': '0512-66277557', '评分': 4.6, '营业时间': '周一至周日 11:00-13:30 17:00-20:30', '推荐菜': '松鼠桂鱼'}"
],
"segmented_user_utterance": "人均 消费 呢 ？",
"segmented_system_utterance": "老枫庭 人均 消费 在 94 元 左右 。"
},
{
"turn_id": 9,
"turn_domain": [
"通用"
],
"user_utterance": "好，所有问题都解决了，谢谢你的帮助！再见！",
"system_utterance": "不客气！这是我应该做的！再见！",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"餐厅-菜系": "江浙 菜",
"餐厅-价位": "中等",
"餐厅-名称": "老枫庭",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行",
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn_inform": {},
"turn request": []
},
"user_actions": [
[
"Bye",
"通用",
"",
""
],
[
"General",
"通用",
"",
""
]
],
"system_actions": [
[
"Bye",
"通用",
"",
""
],
[
"General",
"通用",
"",
""
]
],
"db_results": [],
"segmented_user_utterance": "好 ， 所有 问题 都 解决 了 ， 谢谢 你 的 帮助 ！ 再见 ！",
"segmented_system_utterance": "不 客气 ！ 这 是 我 应该 做 的 ！ 再见 ！"
}
]
}
```
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
RiSAWOZ is the first large-scale multi-domain Chinese Wizard-of-Oz dataset with rich semantic annotations.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The corpus contains rich semantic annotations, such as ellipsis and coreference, in addition to traditional dialogue annotations (dialogue states, dialogue acts, etc.), which can be used in various tasks in dialogue system.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Natural Language Understanding, Dialogue State Tracking, Dialogue Context-to-Text Generation, Coreference Resolution, Unified Generative Ellipsis and Coreference Resolution
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
[Website](https://terryqj0107.github.io/RiSAWOZ_webpage)
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
- In task-oriented dialogue system, the Natural Language Understanding (NLU) module aims to convert the user utterance into the representation that computer can understand, which includes intent and dialogue act (slot & value) detection.
- Dialogue State Tracking (DST) is a core component in task-oriented dialogue systems, which extracts dialogue states (user goals) embedded in dialogue context. It has progressed toward open-vocabulary or generation-based DST where state-of-the-art models can generate dialogue states from dialogue context directly.
- Context-to-Text Generation: encoding dialogue context to decode system response.
- Coreference Resolution: predict coreference clusters where all mentions are referring to the same entity for each dialogue.
- Unified Generative Ellipsis and Coreference Resolution: generating omitted or referred expressions from the dialogue context.
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Natural Language Understanding, Dialogue State Tracking, Dialogue Context-to-Text Generation, Coreference Resolution, Unified Generative Ellipsis and Coreference Resolution
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
- Natural Language Understanding:
- F1 score: F1 score of user intent.
- Dialogue State Tracking:
- Joint Accuracy: accuracy of turn-level dialogue states.
- Dialogue Context-to-Text Generation:
- inform rate: measures the percentage that the output contains the appropriate entity the user asks for.
- success rate: estimates the proportion that all the requested attributes have been answered.
- BLEU: the BLEU score of generated system response.
- Combined Score: (inform + success) ∗ 0.5 + BLEU as an overall
quality.
- Coreference Resolution:
- MUC F1 Score: a link-based metric. Mentions in the same entity/cluster are considered “linked”. MUC penalizes the missing links and incorrect links, each with the same weight.
- B3 F1 Score: a mention-based metric.The evaluation score depends on the fraction of the correct mentions included in the response entities (i.e. entities created by the system).
- CEAFφ4 F1 Score: a metric which assumes each key entity should only be mapped to one response entity, and vice versa. It aligns the key entities (clusters) with the response entities in the best way, and compute scores from that alignment.
- Average F1 Score: an average F1 score of the above three metrics.
- Unified Generative Ellipsis and Coreference Resolution:
- Exact Match Rate: measures whether the generated utterances exactly match the ground-truth utterances.
- BLEU: the BLEU score of generated utterances
- Resolution F1: comparing machine-generated words with ground-truth words only from the ellipsis/coreference part of user utterances.
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
see "Definitions of other metrics"
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
same as our dataset
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
Joint Accuracy, Inform Rate, Success Rate, BLEU Score and Combined Score on MultiWOZ and CrossWOZ dataset.
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Gather human-to-human dialog in Chinese.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Generate system response given dialogue context across multiple domains.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Other crowdworker platform`
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
domains: Attraction, Restaurant, Hotel, Flight, Train, Weather, Movie, TV, Computer, Car, Hospital, Courses
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
hybrid
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
Rule-based and manual selection criteria
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
crowd-sourced
#### Number of Raters
<!-- info: What is the number of raters -->
<!-- scope: telescope -->
51<n<100
#### Rater Qualifications
<!-- info: Describe the qualifications required of an annotator. -->
<!-- scope: periscope -->
Chinese native speaker
#### Raters per Training Example
<!-- info: How many annotators saw each training example? -->
<!-- scope: periscope -->
3
#### Raters per Test Example
<!-- info: How many annotators saw each test example? -->
<!-- scope: periscope -->
3
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
- dialogue_id (string): dialogue ID
- goal (string): natural language descriptions of the user goal
- domains (list of strings): domains mentioned in current dialogue session
- turn_id (int): turn ID
- turn_domain (list of strings): domain mentioned in current turn
- belief_state (dict): dialogue state, including:
- inform slot-values (dict): the slots and corresponding values informed until current turn
- turn_inform (dict): the slots and corresponding values informed in current turn
- turn request (dict): the slots requested in current turn
- user_actions (list of lists): user dialogue acts in current turn
- user_actions (list of lists): system dialogue acts in current turn
- db_results (list of strings): database search results
- segmented_user_utterance (string): word segmentation result of user utterance
- segmented_system_utterance (string): word segmentation result of system utterance
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
unknown
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
Annotators agree using the dataset for research purpose.
#### Other Consented Downstream Use
<!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? -->
<!-- scope: microscope -->
Any
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
The slots and values as well as utterances do not contain any personal information.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
yes
#### Maintenance Plan Details
<!-- info: Describe the original dataset's maintenance plan. -->
<!-- scope: microscope -->
Building a leaderboard webpage to trace and display the latest results on the [dataset](https://terryqj0107.github.io/RiSAWOZ_webpage/)
#### Maintainer Contact Information
<!-- info: Provide contact information of a person responsible for the dataset maintenance -->
<!-- scope: periscope -->
Deyi Xiong ([email protected])
#### Any Contestation Mechanism?
<!-- info: Does the maintenance plan include a contestation mechanism allowing individuals to request removal fo content? -->
<!-- scope: periscope -->
contact maintainer
#### Contestation Form Link
<!-- info: Provide the form link or contact information -->
<!-- scope: periscope -->
Deyi Xiong ([email protected])
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
RiSAWOZ is the first large-scale multi-domain Chinese Wizard-of-Oz dataset with rich semantic annotations.
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
yes
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
None
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
None
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Using the trained model on domains that are not included in the 12 domains selected for this dataset.
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
Designing models that leverage unknown bias in the dataset to optimize specific metrics.
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|
GEM/RotoWire_English-German | GEM | 2022-10-24T15:30:03Z | 276 | 2 | [
"task_categories:table-to-text",
"annotations_creators:automatically-created",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"language:de",
"license:cc-by-4.0",
"data-to-text",
"region:us"
]
| [
"table-to-text"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- automatically-created
language_creators:
- unknown
language:
- en
- de
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: RotoWire_English-German
tags:
- data-to-text
---
# Dataset Card for GEM/RotoWire_English-German
## Dataset Description
- **Homepage:** https://sites.google.com/view/wngt19/dgt-task
- **Repository:** https://github.com/neulab/dgt
- **Paper:** https://www.aclweb.org/anthology/D19-5601/
- **Leaderboard:** N/A
- **Point of Contact:** Hiroaki Hayashi
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/RotoWire_English-German).
### Dataset Summary
This dataset is a data-to-text dataset in the basketball domain. The input are tables in a fixed format with statistics about a game (in English) and the target is a German translation of the originally English description. The translations were done by professional translators with basketball experience. The dataset can be used to evaluate the cross-lingual data-to-text capabilities of a model with complex inputs.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/RotoWire_English-German')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/RotoWire_English-German).
#### website
[Website](https://sites.google.com/view/wngt19/dgt-task)
#### paper
[ACL Anthology](https://www.aclweb.org/anthology/D19-5601/)
#### authors
Graham Neubig (Carnegie Mellon University), Hiroaki Hayashi (Carnegie Mellon University)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](https://sites.google.com/view/wngt19/dgt-task)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/neulab/dgt)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://www.aclweb.org/anthology/D19-5601/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{hayashi-etal-2019-findings,
title = "Findings of the Third Workshop on Neural Generation and Translation",
author = "Hayashi, Hiroaki and
Oda, Yusuke and
Birch, Alexandra and
Konstas, Ioannis and
Finch, Andrew and
Luong, Minh-Thang and
Neubig, Graham and
Sudoh, Katsuhito",
booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5601",
doi = "10.18653/v1/D19-5601",
pages = "1--14",
abstract = "This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Hiroaki Hayashi
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
[email protected]
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
yes
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`, `German`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-4.0: Creative Commons Attribution 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Foster the research on document-level generation technology and contrast the methods for different types of inputs.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Describe a basketball game given its box score table (and possibly a summary in a foreign language).
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Carnegie Mellon University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Graham Neubig (Carnegie Mellon University), Hiroaki Hayashi (Carnegie Mellon University)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Graham Neubig
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Hiroaki Hayashi (Carnegie Mellon University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `id` (`string`): The identifier from the original dataset.
- `gem_id` (`string`): The identifier from GEMv2.
- `day` (`string`): Date of the game (Format: `MM_DD_YY`)
- `home_name` (`string`): Home team name.
- `home_city` (`string`): Home team city name.
- `vis_name` (`string`): Visiting (Away) team name.
- `vis_city` (`string`): Visiting team (Away) city name.
- `home_line` (`Dict[str, str]`): Home team statistics (e.g., team free throw percentage).
- `vis_line` (`Dict[str, str]`): Visiting team statistics (e.g., team free throw percentage).
- `box_score` (`Dict[str, Dict[str, str]]`): Box score table. (Stat_name to [player ID to stat_value].)
- `summary_en` (`List[string]`): Tokenized target summary in English.
- `sentence_end_index_en` (`List[int]`): Sentence end indices for `summary_en`.
- `summary_de` (`List[string]`): Tokenized target summary in German.
- `sentence_end_index_de` (`List[int]`): ): Sentence end indices for `summary_de`.
- (Unused) `detok_summary_org` (`string`): Original summary provided by RotoWire dataset.
- (Unused) `summary` (`List[string]`): Tokenized summary of `detok_summary_org`.
- (Unused) `detok_summary` (`string`): Detokenized (with organizer's detokenizer) summary of `summary`.
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
- Structured data are directly imported from the original RotoWire dataset.
- Textual data (English, German) are associated to each sample.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
'id': '11_02_16-Jazz-Mavericks-TheUtahJazzdefeatedthe',
'gem_id': 'GEM-RotoWire_English-German-train-0'
'day': '11_02_16',
'home_city': 'Utah',
'home_name': 'Jazz',
'vis_city': 'Dallas',
'vis_name': 'Mavericks',
'home_line': {
'TEAM-FT_PCT': '58', ...
},
'vis_line': {
'TEAM-FT_PCT': '80', ...
},
'box_score': {
'PLAYER_NAME': {
'0': 'Harrison Barnes', ...
}, ...
'summary_en': ['The', 'Utah', 'Jazz', 'defeated', 'the', 'Dallas', 'Mavericks', ...],
'sentence_end_index_en': [16, 52, 100, 137, 177, 215, 241, 256, 288],
'summary_de': ['Die', 'Utah', 'Jazz', 'besiegten', 'am', 'Mittwoch', 'in', 'der', ...],
'sentence_end_index_de': [19, 57, 107, 134, 170, 203, 229, 239, 266],
'detok_summary_org': "The Utah Jazz defeated the Dallas Mavericks 97 - 81 ...",
'detok_summary': "The Utah Jazz defeated the Dallas Mavericks 97-81 ...",
'summary': ['The', 'Utah', 'Jazz', 'defeated', 'the', 'Dallas', 'Mavericks', ...],
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- Train
- Validation
- Test
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
- English summaries are provided sentence-by-sentence to professional German translators with basketball knowledge to obtain sentence-level German translations.
- Split criteria follows the original RotoWire dataset.
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
- The (English) summary length in the training set varies from 145 to 650 words, with an average of 323 words.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
The use of two modalities (data, foreign text) to generate a document-level text summary.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
yes
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The potential use of two modalities (data, foreign text) as input.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
- Translation
- Data-to-text verbalization
- Aggregation of the two above.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
- Added GEM ID in each sample.
- Normalize the number of players in each sample with "N/A" for consistent data loading.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
- [Challenges in Data-to-Document Generation](https://aclanthology.org/D17-1239)
- [Data-to-Text Generation with Content Selection and Planning](https://ojs.aaai.org//index.php/AAAI/article/view/4668)
- [Findings of the Third Workshop on Neural Generation and Translation](https://aclanthology.org/D19-5601)
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
- Data-to-text
- Neural machine translation (NMT)
- Document-level generation and translation (DGT)
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
- Textual accuracy towards the gold-standard summary.
- Content faithfulness to the input structured data.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `ROUGE`, `Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
Model-based measures proposed by (Wiseman et al., 2017):
- Relation Generation
- Content Selection
- Content Ordering
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
To evaluate the fidelity of the generated content to the input data.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
N/A.
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
See Table 2 to 7 of (https://aclanthology.org/D19-5601) for previous results for this dataset.
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
A random subset of RotoWire dataset was chosen for German translation annotation.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Foster the research on document-level generation technology and contrast the methods for different types of inputs.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
RotoWire
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Created for the dataset`
#### Creation Process
<!-- info: If created for the dataset, describe the creation process. -->
<!-- scope: microscope -->
Professional German language translators were hired to translate basketball summaries from a subset of RotoWire dataset.
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
Translators are familiar with basketball terminology.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
Basketball (NBA) game summaries.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Sentence-level translations were aligned back to the original English summary sentences.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
automatically created
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
Sentence-end indices for the tokenized summaries. Sentence boundaries can help users accurately identify aligned sentences in both languages, as well as allowing an accurate evaluation that involves sentence boundaries (ROUGE-L).
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated through automated script
#### Quality Control Details
<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
Token and number overlaps between pairs of aligned sentences are measured.
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
Reusing by citing the original papers:
- Sam Wiseman, Stuart M. Shieber, Alexander M. Rush:
Challenges in Data-to-Document Generation. EMNLP 2017.
- Hiroaki Hayashi, Yusuke Oda, Alexandra Birch, Ioannis Konstas, Andrew Finch, Minh-Thang Luong, Graham Neubig, Katsuhito Sudoh. Findings of the Third Workshop on Neural Generation and Translation. WNGT 2019.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
- English text in this dataset is from Rotowire, originally written by writers at Rotowire.com that are likely US-based.
- German text is produced by professional translators proficient in both English and German.
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
- Structured data contain real National Basketball Association player and organization names.
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
Potential overlap of box score tables between splits. This was extensively studied and pointed out by [1].
[1]: Thomson, Craig, Ehud Reiter, and Somayajulu Sripada. "SportSett: Basketball-A robust and maintainable data-set for Natural Language Generation." Proceedings of the Workshop on Intelligent Information Processing and Natural Language Generation. 2020.
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Users may interact with a trained model to learn about a NBA game in a textual manner. On generated texts, they may observe factual errors that contradicts the actual data that the model conditions on.
Factual errors include wrong statistics of a player (e.g., 3PT), non-existent injury information.
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
Publishing the generated text as is. Even if the model achieves high scores on the evaluation metrics, there is a risk of factual errors mentioned above.
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|
GEM/Taskmaster | GEM | 2022-10-24T15:30:09Z | 276 | 1 | [
"task_categories:conversational",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"dialog-response-generation",
"arxiv:2012.12458",
"region:us"
]
| [
"conversational"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: Taskmaster
tags:
- dialog-response-generation
---
# Dataset Card for GEM/Taskmaster
## Dataset Description
- **Homepage:** https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020
- **Repository:** https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020
- **Paper:** https://arxiv.org/abs/2012.12458
- **Leaderboard:** N/A
- **Point of Contact:** Karthik Krishnamoorthi
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/Taskmaster).
### Dataset Summary
This is a large task-oriented dialog dataset in which a model has to produce the response. The input contains the context and a structured representation of what the model is supposed to generate. The input is already pre-formatted as string, turning this into a pure text-to-text problem.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/Taskmaster')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/Taskmaster).
#### website
[Github](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020)
#### paper
[Arxiv](https://arxiv.org/abs/2012.12458)
#### authors
Google researchers
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[Arxiv](https://arxiv.org/abs/2012.12458)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@article{byrne2020tickettalk,
title={TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems},
author={Byrne, Bill and Krishnamoorthi, Karthik and Ganesh, Saravanan and Kale, Mihir Sanjay},
journal={arXiv preprint arXiv:2012.12458},
year={2020}
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Karthik Krishnamoorthi
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
[email protected]
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
NA
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
NA
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-4.0: Creative Commons Attribution 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Dialogues
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
a movie ticketing dialog dataset with 23,789 annotated conversations.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`other`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
NA
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Google researchers
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Google
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Tosin Adewumi (Luleå University of Technology)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `gem_id`: The unique example id
- `context`: The context of the conversation
- `target`: A string representing the target
-`references`: A List representing the target(s)
-`conversation_id`: A unique ID of the conversation
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
NA
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
NA
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{'context': "<PR>get_movie_attribute<PRAN>rating.movie<PRAV>rated R<C><U>I wanna see a movie<A>where are you?<U>spring hills kansas<PN>find_theaters<PAN>location<PAV>spring hills kansas<PR>find_theaters<PRAN>name.theater<PRAV>AMC Holiday Theater<PRAV>Cinemark Downtown<A>there are 2 theaters near you, the AMC Holiday Theater and Cinemark Downtown. Did you know which movie you'd like to see?<U>funny one please<PN>find_movies<PAN>location<PAV>spring hills kansas<PR>find_movies<PRAN>name.movie<PRAV>Not My Problem<PRAV>Family Jewels<PN>get_movie_attribute<PAN>name.movie<PAV>Not My Problem<PAN>attribute<PAV>name.genre<PR>get_movie_attribute<PRAN>name.genre<PRAV>comedy<PN>get_movie_attribute<PAN>name.movie<PAV>Not My Problem<PAN>attribute<PAV>name.person<PR>get_movie_attribute<PRAN>name.person<PRAV>Matt Damon<PN>get_movie_attribute<PAN>name.movie<PAV>Not My Problem<PAN>attribute<PAV>name.person<PR>get_movie_attribute<PRAN>name.person<PRAV>Noah Schnapp<PN>get_movie_attribute<PAN>name.movie<PAV>Family Jewels<PAN>attribute<PAV>name.genre<PR>get_movie_attribute<PRAN>name.genre<PRAV>romantic comedy<PN>get_movie_attribute<PAN>name.movie<PAV>Family Jewels<PAN>attribute<PAV>name.person<PR>get_movie_attribute<PRAN>name.person<PRAV>Melissa McCarthy<PN>get_movie_attribute<PAN>name.movie<PAV>Family Jewels<PAN>attribute<PAV>name.person<PR>get_movie_attribute<PRAN>name.person<PRAV>Ryan Reynolds<A>There's the comedy film called Not My Problem starring Matt Damon and Noah Schnapp. There's also a romantic comedy called Family Jewels starring Melissa McCarthy and Ryan Reynolds.<U>what ratings are there?<PN>get_movie_attribute<PAN>name.movie<PAV>Not My Problem<PAN>attribute<PAV>rating.movie<PR>get_movie_attribute<PRAN>rating.movie<PRAV>rated PG-13<PN>get_movie_attribute<PAN>name.movie<PAV>Family Jewels<PAN>attribute<PAV>rating.movie",
'conversation_id': 'dlg-d1f52e7e-c34c-4e85-b406-85ed138b5068',
'gem_id': 'Taskmaster-train-0',
'references': ['Not My Problem is rated PG-13 and Family Jewels is rated R.'],
'target': 'Not My Problem is rated PG-13 and Family Jewels is rated R.'}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
-`train`: 187182 examples
-`dev`: 23406 examples
-`test`: 23316 examples
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
NA
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
NA
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
Dialogue generation that makes sense
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
NA
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
NA
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
gem_id field was added to the 3 data splits
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
NA
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
BLEU: 60
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
automatic evaluation
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
NA
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
NA
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
NA
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
a movie ticketing dialog dataset with 23,789 annotated conversations.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Participatory experiment`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
NA
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
Ticketing
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
NA
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
It's based on ticketing without personal information
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
NA
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
NA
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
NA
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
NA
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
NA
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-0.2508193850517273,
-0.3521646559238434
]
|
GEM/cs_restaurants | GEM | 2022-10-24T15:30:14Z | 276 | 1 | [
"task_categories:conversational",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:cs",
"license:cc-by-sa-4.0",
"dialog-response-generation",
"region:us"
]
| [
"conversational"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- cs
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: cs_restaurants
tags:
- dialog-response-generation
---
# Dataset Card for GEM/cs_restaurants
## Dataset Description
- **Homepage:** n/a
- **Repository:** https://github.com/UFAL-DSG/cs_restaurant_dataset
- **Paper:** https://aclanthology.org/W19-8670/
- **Leaderboard:** N/A
- **Point of Contact:** Ondrej Dusek
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/cs_restaurants).
### Dataset Summary
The Czech Restaurants dataset is a task oriented dialog dataset in which a model needs to verbalize a response that a service agent could provide which is specified through a series of dialog acts. The dataset originated as a translation of an English dataset to test the generation capabilities of an NLG system on a highly morphologically rich language like Czech.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/cs_restaurants')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/cs_restaurants).
#### website
n/a
#### paper
[Github](https://aclanthology.org/W19-8670/)
#### authors
Ondrej Dusek and Filip Jurcicek
## Dataset Overview
### Where to find the Data and its Documentation
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/UFAL-DSG/cs_restaurant_dataset)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[Github](https://aclanthology.org/W19-8670/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{cs_restaurants,
address = {Tokyo, Japan},
title = {Neural {Generation} for {Czech}: {Data} and {Baselines}},
shorttitle = {Neural {Generation} for {Czech}},
url = {https://www.aclweb.org/anthology/W19-8670/},
urldate = {2019-10-18},
booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)},
author = {Dušek, Ondřej and Jurčíček, Filip},
month = oct,
year = {2019},
pages = {563--574},
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Ondrej Dusek
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
[email protected]
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
No breakdown of dialects is provided.
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Czech`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
Six professional translators produced the outputs
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The dataset was created to test neural NLG systems in Czech and their ability to deal with rich morphology.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Producing a text expressing the given intent/dialogue act and all and only the attributes specified in the input meaning representation.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Charles University, Prague
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Ondrej Dusek and Filip Jurcicek
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
This research was supported by the Charles University project PRIMUS/19/SCI/10 and by the Ministry of Education, Youth and Sports of the Czech Republic under the grant agreement LK11221. This work used using language resources distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of the Czech Republic (project LM2015071).
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Simon Mille wrote the initial data card and Yacine Jernite the data loader. Sebastian Gehrmann migrated the data card and loader to the v2 format.
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
The data is stored in a JSON or CSV format, with identical contents. The data has 4 fields:
* `da`: the input meaning representation/dialogue act (MR)
* `delex_da`: the input MR, delexicalized -- all slot values are replaced with placeholders, such as `X-name`
* `text`: the corresponding target natural language text (reference)
* `delex_text`: the target text, delexicalized (delexicalization is applied regardless of inflection)
In addition, the data contains a JSON file with all possible inflected forms for all slot values in the dataset (`surface_forms.json`).
Each slot -> value entry contains a list of inflected forms for the given value, with the base form (lemma), the inflected form, and
a [morphological tag](https://ufal.mff.cuni.cz/pdt/Morphology_and_Tagging/Doc/hmptagqr.html).
The same MR is often repeated multiple times with different synonymous reference texts.
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The data originated as a translation and localization of [Wen et al.'s SF restaurant](https://www.aclweb.org/anthology/D15-1199/) NLG dataset.
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
The input MRs were collected from [Wen et al.'s SF restaurant](https://www.aclweb.org/anthology/D15-1199/) NLG data
and localized by randomly replacing slot values (using a list of Prague restaurant names, neighborhoods etc.).
The generated slot values were then automatically replaced in reference texts in the data.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
"input": "inform_only_match(food=Turkish,name='Švejk Restaurant',near='Charles Bridge',price_range=cheap)",
"target": "Našla jsem pouze jednu levnou restauraci poblíž Karlova mostu , kde podávají tureckou kuchyni , Švejk Restaurant ."
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
| Property | Value |
|--------------------------------|-------|
| Total instances | 5,192 |
| Unique MRs | 2,417 |
| Unique delexicalized instances | 2,752 |
| Unique delexicalized MRs | 248 |
The data is split in a roughly 3:1:1 proportion into training, development and test sections, making sure no delexicalized MR
appears in two different parts. On the other hand, most DA types/intents are represented in all data parts.
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The creators ensured that after delexicalization of the meaning representation there was no overlap between training and test.
The data is split at a 3:1:1 rate between training, validation, and test.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
This is one of a few non-English data-to-text datasets, in a well-known domain, but covering a morphologically rich language that is harder to generate since named entities need to be inflected. This makes it harder to apply common techniques such as delexicalization or copy mechanisms.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
yes
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The dialog acts in this dataset are much more varied than the e2e dataset which is the closest in style.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
surface realization
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
yes
#### Split Information
<!-- info: Describe how the new splits were created -->
<!-- scope: periscope -->
5 challenge sets for the Czech Restaurants dataset were added to the GEM evaluation suite.
1. Data shift: We created subsets of the training and development sets of 500 randomly selected inputs each.
2. Scrambling: We applied input scrambling on a subset of 500 randomly selected test instances; the order of the input dialogue acts was randomly reassigned.
3. We identified different subsets of the test set that we could compare to each other so that we would have a better understanding of the results. There are currently two selections that we have made:
The first comparison is based on input size: the number of predicates differs between different inputs, ranging from 1 to 5.
The table below provides an indication of the distribution of inputs with a particular length.
It is clear from the table that this distribution is not balanced, and comparisions between items should be done with caution.
Particularly for input size 4 and 5, there may not be enough data to draw reliable conclusions.
| Input length | Number of inputs |
|--------------|------------------|
| 1 | 183 |
| 2 | 267 |
| 3 | 297 |
| 4 | 86 |
| 5 | 9 |
The second comparison is based on the type of act. Again we caution against comparing the different groups that have relatively few items.
It is probably OK to compare `inform` and `?request`, but the other acts are all low-frequent.
| Act | Frequency |
|-------------------|-----------|
| ?request | 149 |
| inform | 609 |
| ?confirm | 22 |
| inform_only_match | 16 |
| inform_no_match | 34 |
| ?select | 12 |
#### Split Motivation
<!-- info: What aspects of the model's generation capacities were the splits created to test? -->
<!-- scope: periscope -->
Generalization and robustness.
### Getting Started with the Task
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
- utterance: something a system or user may say in a turn
- meaning representation: a representation of meaning that the system should be in accordance with. The specific type of MR in this dataset are dialog acts which describe what a dialog system should do, e.g., inform a user about a value.
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Surface realization
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `ROUGE`, `METEOR`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
This dataset uses the suite of word-overlap-based automatic metrics from the E2E NLG Challenge (BLEU, NIST, ROUGE-L, METEOR, and CIDEr). In addition, the slot error rate is measured.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The dataset was created to test neural NLG systems in Czech and their ability to deal with rich morphology.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Producing a text expressing the given intent/dialogue act and all and only the attributes specified in the input MR.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Created for the dataset`
#### Creation Process
<!-- info: If created for the dataset, describe the creation process. -->
<!-- scope: microscope -->
Six professional translators translated the underlying dataset with the following instructions:
- Each utterance should be translated by itself
- fluent spoken-style Czech should be produced
- Facts should be preserved
- If possible, synonyms should be varied to create diverse utterances
- Entity names should be inflected as necessary
- the reader of the generated text should be addressed using formal form and self-references should use the female form.
The translators did not have access to the meaning representation.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
It was not explicitly stated but we can safely assume that the translators agreed to this use of their data.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
This dataset does not include any information about individuals.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
The dataset may help improve NLG methods for morphologically rich languages beyond Czech.
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
yes
#### Links and Summaries of Analysis Work
<!-- info: Provide links to and summaries of works analyzing these biases. -->
<!-- scope: microscope -->
To ensure consistency of translation, the data always uses formal/polite address for the user, and uses the female form for first-person self-references (as if the dialogue agent producing the sentences was female). This prevents data sparsity and ensures consistent results for systems trained on the dataset, but does not represent all potential situations arising in Czech.
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
The test set may lead users to over-estimate the performance of their NLG systems with respect to their generalisability, because there are no unseen restaurants or addresses in the test set. This is something we will look into for future editions of the GEM shared task.
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|
corypaik/coda | corypaik | 2022-10-20T16:57:23Z | 276 | 2 | [
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2110.08182",
"region:us"
]
| [
"text-scoring"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
language_bcp47:
- en-US
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: CoDa
paperswithcode_id: coda
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-scoring
task_ids:
- text-scoring-other-distribution-prediction
---
# Dataset Card for CoDa
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [nala-cub/coda](https://github.com/nala-cub/coda)
- **Paper:** [The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color](https://arxiv.org/abs/2110.08182)
- **Point of Contact:** [Cory Paik]([email protected])
### Dataset Summary
*The Color Dataset* (CoDa) is a probing dataset to evaluate the representation of visual properties in language models. CoDa consists of color distributions for 521 common objects, which are split into 3 groups. We denote these groups as Single, Multi, and Any, which represents the typical object of each group.
The default configuration of CoDa uses 10 CLIP-style templates (e.g. "A photo of a [object]"), and 10 cloze-style templates (e.g. "Everyone knows most [object] are
[color]." )
### Supported Tasks and Leaderboards
This version of the dataset consists of the filtered and templated examples as cloze style questions. See the [GitHub](https://github.com/nala-cub/coda) repo for the raw data (e.g. unfiltered annotations) as well as example usage with GPT-2, RoBERTa, ALBERT, and CLIP.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en-US`.
## Dataset Structure
### Data Instances
An example looks like this:
```json
{
"text": "All rulers are [MASK].",
"label": [
0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352,
0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636,
0.301363647
],
"template_group": 1,
"template_idx": 0,
"class_id": "/m/0hdln",
"display_name": "Ruler",
"object_group": 2,
"ngram": "ruler"
}
```
### Data Fields
- `text`: The templated example. What this is depends on the value of `template_group`.
- `template_group=0`: A CLIP style example. There are no `[MASK]` tokens in these examples.
- `template_group=1`: A cloze style example. Note that all templates have `[MASK]` as the last word, but in most cases, the period should be included.
- `label`: A list of probability values for the 11 colors. Note that these are sorted by the alphabetic order of the 11 colors (black, blue, brown, gray, green, orange, pink, purple, red, white, yellow).
- `template_group`: Type of template, `0` corresponds to A CLIP style template (`clip-imagenet`), and `1` corresponds to A cloze style templates (`text-masked`).
- `template_idx`: The index of the template out of all templates
- `class_id`: The Corresponding [OpenImages v6](https://storage.googleapis.com/openimages/web/index.html) `ClassID`.
- `display_name`: The Corresponding [OpenImages v6](https://storage.googleapis.com/openimages/web/index.html) `DisplayName`.
- `object_group`: Object Group, values correspond to `Single`, `Multi`, and `Any`.
- `ngram`: Corresponding n-gram used for lookups.
### Data Splits
Object Splits:
| Group | All | Train | Valid | Test |
| ------ | --- | ----- | ----- | ---- |
| Single | 198 | 118 | 39 | 41 |
| Multi | 208 | 124 | 41 | 43 |
| Any | 115 | 69 | 23 | 23 |
| Total | 521 | 311 | 103 | 107 |
Example Splits:
| Group | All | Train | Valid | Test |
| ------ | ----- | ----- | ----- | ---- |
| Single | 3946 | 2346 | 780 | 820 |
| Multi | 4146 | 2466 | 820 | 860 |
| Any | 2265 | 1352 | 460 | 453 |
| Total | 10357 | 6164 | 2060 | 2133 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CoDa is licensed under the Apache 2.0 license.
### Citation Information
```
@misc{paik2021world,
title={The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color},
author={Cory Paik and Stéphane Aroca-Ouellette and Alessandro Roncone and Katharina Kann},
year={2021},
eprint={2110.08182},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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|
sil-ai/bloom-lm | sil-ai | 2022-10-21T12:13:50Z | 276 | 22 | [
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| null | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
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license:
- cc-by-4.0
- cc-by-nc-4.0
- cc-by-nd-4.0
- cc-by-sa-4.0
- cc-by-nc-nd-4.0
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_ids:
- language-modeling
paperswithcode_id: null
pretty_name: BloomLM
extra_gated_prompt: |-
One more step before getting this dataset. This dataset is open access and available only for non-commercial use (except for portions of the dataset labeled with a `cc-by-sa` license). A "license" field paired with each of the dataset entries/samples specifies the Creative Commons license for that entry/sample.
These [Creative Commons licenses](https://creativecommons.org/about/cclicenses/) specify that:
1. You cannot use the dataset for or directed toward commercial advantage or monetary compensation (except for those portions of the dataset labeled specifically with a `cc-by-sa` license. If you would like to ask about commercial uses of this dataset, please [email us](mailto:[email protected]).
2. Any public, non-commercial use of the data must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
3. For those portions of the dataset marked with an ND license, you cannot remix, transform, or build upon the material, and you may not distribute modified material.
In addition to the above implied by Creative Commons and when clicking "Access Repository" below, you agree:
1. Not to use the dataset for any use intended to or which has the effect of harming or enabling discrimination against individuals or groups based on legally protected characteristics or categories, including but not limited to discrimination against Indigenous People as outlined in Articles 2; 13-16; and 31 of the United Nations Declaration on the Rights of Indigenous People, 13 September 2007 and as subsequently amended and revised.
2. That your *contact information* (email address and username) can be shared with the model authors as well.
extra_gated_fields:
I have read the License and agree with its terms: checkbox
---
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
<!-- - [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions) -->
## Dataset Description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI email](mailto:[email protected])
- **Source Data:** [Bloom Library](https://bloomlibrary.org/)
 
## Dataset Summary
**Bloom** is free, open-source software and an associated website [Bloom Library](https://bloomlibrary.org/), app, and services developed by [SIL International](https://www.sil.org/). Bloom’s primary goal is to equip non-dominant language communities and their members to create the literature they want for their community and children. Bloom also serves organizations that help such communities develop literature and education or other aspects of community development.
This version of the Bloom Library data is developed specifically for the language modeling task. It includes data from 364 languages across 31 language families. There is a mean of 32 stories and median of 2 stories per language.
**Note**: If you speak one of these languages and can help provide feedback or corrections, please let us know!
**Note**: Although this data was used in the training of the [BLOOM model](https://huggingface.co/bigscience/bloom), this dataset only represents a small portion of the data used to train that model. Data from "Bloom Library" was combined with a large number of other datasets to train that model. "Bloom Library" is a project that existed prior to the BLOOM model, and is something separate. All that to say... We were using the "Bloom" name before it was cool. 😉
## Languages
Of the 500+ languages listed at BloomLibrary.org, there are 363 languages available in this dataset. Here are the corresponding ISO 639-3 codes:
aaa, abc, ada, adq, aeu, afr, agq, ags, ahk, aia, ajz, aka, ame, amh, amp, amu, ann, aph, awa, awb, azn, azo, bag, bam, baw, bax, bbk, bcc, bce, bec, bef, ben, bfd, bfm, bfn, bgf, bho, bhs, bis, bjn, bjr, bkc, bkh, bkm, bkx, bob, bod, boz, bqm, bra, brb, bri, brv, bss, bud, buo, bwt, bwx, bxa, bya, bze, bzi, cak, cbr, ceb, cgc, chd, chp, cim, clo, cmn, cmo, csw, cuh, cuv, dag, ddg, ded, deu, dig, dje, dmg, dnw, dtp, dtr, dty, dug, eee, ekm, enb, enc, eng, ewo, fas, fil, fli, fon, fra, fub, fuh, gal, gbj, gou, gsw, guc, guj, guz, gwc, hao, hat, hau, hbb, hig, hil, hin, hla, hna, hre, hro, idt, ilo, ind, ino, isu, ita, jgo, jmx, jpn, jra, kak, kam, kan, kau, kbq, kbx, kby, kek, ken, khb, khm, kik, kin, kir, kjb, kmg, kmr, kms, kmu, kor, kqr, krr, ksw, kur, kvt, kwd, kwu, kwx, kxp, kyq, laj, lan, lao, lbr, lfa, lgg, lgr, lhm, lhu, lkb, llg, lmp, lns, loh, lsi, lts, lug, luy, lwl, mai, mal, mam, mar, mdr, mfh, mfj, mgg, mgm, mgo, mgq, mhx, miy, mkz, mle, mlk, mlw, mmu, mne, mnf, mnw, mot, mqj, mrn, mry, msb, muv, mve, mxu, mya, myk, myx, mzm, nas, nco, nep, new, nge, ngn, nhx, njy, nla, nld, nlv, nod, nsk, nsn, nso, nst, nuj, nwe, nwi, nxa, nxl, nya, nyo, nyu, nza, odk, oji, oki, omw, ori, ozm, pae, pag, pan, pbt, pce, pcg, pdu, pea, pex, pis, pkb, pmf, pnz, por, psp, pwg, qub, quc, quf, quz, qve, qvh, qvm, qvo, qxh, rel, rnl, ron, roo, rue, rug, rus, san, saq, sat, sdk, sea, sgd, shn, sml, snk, snl, som, sot, sox, spa, sps, ssn, stk, swa, swh, sxb, syw, taj, tam, tbj, tdb, tdg, tdt, teo, tet, tgk, tha, the, thk, thl, thy, tio, tkd, tnl, tnn, tnp, tnt, tod, tom, tpi, tpl, tpu, tsb, tsn, tso, tuv, tuz, tvs, udg, unr, urd, uzb, ven, vie, vif, war, wbm, wbr, wms, wni, wnk, wtk, xho, xkg, xmd, xmg, xmm, xog, xty, yas, yav, ybb, ybh, ybi, ydd, yea, yet, yid, yin, ymp, zaw, zho, zlm, zuh, zul
## Dataset Statistics
Some of the languages included in the dataset just include 1 or a couple of "stories." These are not split between training, validation, and test. For those with higher numbers of available stories we include the following numbers of stories in each split:
| ISO 639-3 | Name | Train Stories | Validation Stories | Test Stories |
|:------------|:------------------------------|----------------:|---------------------:|---------------:|
| aeu | Akeu | 47 | 6 | 5 |
| afr | Afrikaans | 19 | 2 | 2 |
| ahk | Akha | 81 | 10 | 10 |
| aph | Athpariya | 28 | 4 | 3 |
| awa | Awadhi | 131 | 16 | 16 |
| ben | Bengali | 201 | 25 | 25 |
| bfn | Bunak | 11 | 1 | 1 |
| bho | Bhojpuri | 139 | 17 | 17 |
| bis | Bislama | 20 | 2 | 2 |
| bkm | Kom (Cameroon) | 15 | 2 | 1 |
| bkx | Baikeno | 8 | 1 | 1 |
| brb | Brao | 18 | 2 | 2 |
| bwx | Bu-Nao Bunu | 14 | 2 | 1 |
| bzi | Bisu | 53 | 7 | 6 |
| cak | Kaqchikel | 54 | 7 | 6 |
| cbr | Cashibo-Cacataibo | 11 | 1 | 1 |
| ceb | Cebuano | 335 | 42 | 41 |
| cgc | Kagayanen | 158 | 20 | 19 |
| cmo | Central Mnong | 16 | 2 | 2 |
| ddg | Fataluku | 14 | 2 | 1 |
| deu | German | 36 | 4 | 4 |
| dtp | Kadazan Dusun | 13 | 2 | 1 |
| dty | Dotyali | 138 | 17 | 17 |
| eng | English | 2107 | 263 | 263 |
| fas | Persian | 104 | 13 | 12 |
| fil | Filipino | 55 | 7 | 6 |
| fra | French | 323 | 40 | 40 |
| gal | Galolen | 11 | 1 | 1 |
| gwc | Gawri | 15 | 2 | 1 |
| hat | Haitian | 208 | 26 | 26 |
| hau | Hausa | 205 | 26 | 25 |
| hbb | Huba | 22 | 3 | 2 |
| hin | Hindi | 16 | 2 | 2 |
| idt | Idaté | 8 | 1 | 1 |
| ind | Indonesian | 208 | 26 | 25 |
| jmx | Western Juxtlahuaca Mixtec | 19 | 2 | 2 |
| jra | Jarai | 112 | 14 | 13 |
| kak | Kalanguya | 156 | 20 | 19 |
| kan | Kannada | 17 | 2 | 2 |
| kau | Kanuri | 36 | 5 | 4 |
| kek | Kekchí | 29 | 4 | 3 |
| khb | Lü | 25 | 3 | 3 |
| khm | Khmer | 28 | 4 | 3 |
| kik | Kikuyu | 8 | 1 | 1 |
| kir | Kirghiz | 306 | 38 | 38 |
| kjb | Q'anjob'al | 82 | 10 | 10 |
| kmg | Kâte | 16 | 2 | 1 |
| kor | Korean | 106 | 13 | 13 |
| krr | Krung | 24 | 3 | 3 |
| kwd | Kwaio | 19 | 2 | 2 |
| kwu | Kwakum | 16 | 2 | 2 |
| lbr | Lohorung | 8 | 1 | 1 |
| lhu | Lahu | 32 | 4 | 4 |
| lsi | Lashi | 21 | 3 | 2 |
| mai | Maithili | 144 | 18 | 18 |
| mal | Malayalam | 12 | 1 | 1 |
| mam | Mam | 108 | 13 | 13 |
| mar | Marathi | 8 | 1 | 1 |
| mgm | Mambae | 12 | 2 | 1 |
| mhx | Maru | 79 | 10 | 9 |
| mkz | Makasae | 16 | 2 | 2 |
| mya | Burmese | 31 | 4 | 3 |
| myk | Mamara Senoufo | 28 | 3 | 3 |
| nep | Nepali (macrolanguage) | 160 | 20 | 20 |
| new | Newari | 142 | 18 | 17 |
| nlv | Orizaba Nahuatl | 8 | 1 | 1 |
| nsn | Nehan | 9 | 1 | 1 |
| nwi | Southwest Tanna | 9 | 1 | 1 |
| nxa | Nauete | 12 | 1 | 1 |
| omw | South Tairora | 10 | 1 | 1 |
| pbt | Southern Pashto | 164 | 21 | 20 |
| pce | Ruching Palaung | 30 | 4 | 3 |
| pis | Pijin | 14 | 2 | 1 |
| por | Portuguese | 131 | 16 | 16 |
| quc | K'iche' | 80 | 10 | 9 |
| rus | Russian | 283 | 35 | 35 |
| sdk | Sos Kundi | 9 | 1 | 1 |
| snk | Soninke | 28 | 4 | 3 |
| spa | Spanish | 423 | 53 | 52 |
| swh | Swahili (individual language) | 58 | 7 | 7 |
| tam | Tamil | 13 | 2 | 1 |
| tdg | Western Tamang | 26 | 3 | 3 |
| tdt | Tetun Dili | 22 | 3 | 2 |
| tet | Tetum | 8 | 1 | 1 |
| tgk | Tajik | 24 | 3 | 2 |
| tha | Thai | 228 | 29 | 28 |
| the | Chitwania Tharu | 11 | 1 | 1 |
| thl | Dangaura Tharu | 148 | 19 | 18 |
| tnl | Lenakel | 10 | 1 | 1 |
| tnn | North Tanna | 9 | 1 | 1 |
| tpi | Tok Pisin | 161 | 20 | 20 |
| tpu | Tampuan | 24 | 3 | 2 |
| uzb | Uzbek | 24 | 3 | 2 |
| war | Waray (Philippines) | 16 | 2 | 2 |
| wbr | Wagdi | 10 | 1 | 1 |
| wni | Ndzwani Comorian | 12 | 2 | 1 |
| xkg | Kagoro | 16 | 2 | 1 |
| ybh | Yakha | 16 | 2 | 1 |
| zho | Chinese | 34 | 4 | 4 |
| zlm | Malay (individual language) | 8 | 1 | 1 |
| zul | Zulu | 19 | 2 | 2 |
## Dataset Structure
### Data Instances
The examples look like this for Hindi:
```
from datasets import load_dataset
# Specify the language code.
dataset = load_dataset("sil-ai/bloom-lm", 'hin')
# A data point consists of stories in the specified language code.
# To see a story:
print(dataset['train']['text'][0])
```
This would produce an output:
```
साबू ने एक कंकड़ को ठोकर मारी। कंकड़ लुढ़कता हुआ एक पेड़ के पास पहुँचा। पेड़ के तने पर मुलायम बाल थे। साबू ने छुए और ऊपर देखा, ऊपर, ऊपर और उससे भी ऊपर...दो आँखें नीचे देख रही थीं।
“हेलो, तुम कौन हो?” साबू को बड़ा अचम्भा हुआ।“हेलो, मैं जिराफ़ हूँ। मेरा नाम है जोजो। मैं तुम्हारे साथ खेल सकता हूँ। मेरी पीठ पर चढ़ जाओ, मैं तुम्हें घुमा के लाता हूँ।”
साबू जोजो की पीठ पर चढ़ गया और वे सड़क पर चल निकले। फिर पहाड़ी पर और शहर के बीचों बीच।
साबू खुशी से चिल्लाया, “जोजो दाएँ मुड़ो,
बाएँ मुड़ो और फिर दाएँ।” अब वे उसकी दोस्त मुन्नी के घर पहुँच गये।
आज मुन्नी का जन्मदिन था। साबू को जोजो पर सवारी करते देख बच्चों ने ताली बजायी।
जोजो ने गुब्बारे लटकाने में आन्टी की मदद करी क्योंकि वह इतना... लम्बा था।
कितना आसान था!
जोजो ने सब बच्चों को सवारी कराई।
उनके साथ बॉल भी खेली। बड़े मज़े की पार्टी थी।सब ने गाया, “हैप्पी बर्थ डे टु यू ।”
आन्टी ने मेज़ पर समोसे, गुलाब जामुन और आइसक्रीम सजाई।
जोजो को आइसक्रीम बहुत पसन्द आई। अंकल उसके लिये एक बाल्टी भर के आइसक्रीम लाये। जोजो ने पूरी बाल्टी ख़त्म कर दी। अब घर जाने का समय हो गया।
सब ने कहा, “बाय बाय जोजो, बाय बाय साबू।” साबू और जोजो घर लौटे।
```
Whereas if you wish to gather all the text for a language you may use this:
```
dataset['train']['text']
```
### Data Fields
The metadata fields below are available and the full dataset will be updated with per story metadata soon (in August 2022). As of now a majority of stories have metadata, but some are missing certain fields. In terms of licenses, all stories included in the current release are released under a Creative Commons license (even if the individual story metadata fields are missing).
- **text**: the text of the story/book, concatenated together from the different pages.
- **id**: id of the sample
- **title**: title of the book, e.g. "Going to Buy a Book".
- **license**: specific license used, e.g. "cc-by-sa" for "Creative Commons, by attribution, share-alike".
- **copyright**: copyright notice from the original book on bloomlibrary.org
- **pageCount**: page count from the metadata on the original book on bloomlibrary.org.
- **bookInstanceId**: unique ID for each book/translation assigned by Bloom. For example the Hindi version of 'Going to Buy a Book' is 'af86eefd-f69c-4e06-b8eb-e0451853aab9'.
- **bookLineage**: Unique bookInstanceIDs of _other_ Bloom books that this book is in some way based on. For example, the Hindi version in the example above is based on '056B6F11-4A6C-4942-B2BC-8861E62B03B3'. It's quite possible for this to be either empty, or have multiple entries. For example, the book 'Saboo y Jojo' with ID '5b232a5f-561d-4514-afe7-d6ed2f6a940f' is based on two others, ['056B6F11-4A6C-4942-B2BC-8861E62B03B3', '10a6075b-3c4f-40e4-94f3-593497f2793a']
- (coming soon) **contentLanguages**: Other languages this book may be available in. "Going to Buy a Book" is available in ['eng', 'kan', 'mar', 'pan', 'ben', 'guj', 'hin'] for example.
### Data Splits
All languages include a train, validation, and test split. However, for language having a small number of stories, certain of these splits maybe empty. In such cases, we recommend using any data for testing only or for zero-shot experiments.
## Changelog
- **25 August 2022** - add the remaining metadata, change data type of `pageCount` to int32
- **24 August 2022** - majority of metadata added back in to the filtered/ clean data
- **23 August 2022** - metadata temporarily removed to update to cleaner dataset | [
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|
asapp/slue | asapp | 2022-09-26T23:08:10Z | 276 | 2 | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:sentiment-analysis",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"license:cc-by-4.0",
"arxiv:2111.10367",
"region:us"
]
| [
"automatic-speech-recognition",
"audio-classification",
"text-classification",
"token-classification"
]
| 2022-09-19T18:07:59Z | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- cc0-1.0
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: slue
pretty_name: SLUE (Spoken Language Understanding Evaluation benchmark)
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- automatic-speech-recognition
- audio-classification
- text-classification
- token-classification
task_ids:
- sentiment-analysis
- named-entity-recognition
configs:
- voxpopuli
- voxceleb
---
# Dataset Card for SLUE
## Table of Contents
- [Dataset Card for SLUE](#dataset-card-for-slue)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Automatic Speech Recognition (ASR)](#automatic-speech-recognition-asr)
- [Named Entity Recognition (NER)](#named-entity-recognition-ner)
- [Sentiment Analysis (SA)](#sentiment-analysis-sa)
- [How-to-submit for your test set evaluation](#how-to-submit-for-your-test-set-evaluation)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [voxpopuli](#voxpopuli)
- [voxceleb](#voxceleb)
- [Data Fields](#data-fields)
- [voxpopuli](#voxpopuli-1)
- [voxceleb](#voxceleb-1)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [SLUE-VoxPopuli Dataset](#slue-voxpopuli-dataset)
- [SLUE-VoxCeleb Dataset](#slue-voxceleb-dataset)
- [Original License of OXFORD VGG VoxCeleb Dataset](#original-license-of-oxford-vgg-voxceleb-dataset)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://asappresearch.github.io/slue-toolkit](https://asappresearch.github.io/slue-toolkit)
- **Repository:** [https://github.com/asappresearch/slue-toolkit/](https://github.com/asappresearch/slue-toolkit/)
- **Paper:** [https://arxiv.org/pdf/2111.10367.pdf](https://arxiv.org/pdf/2111.10367.pdf)
- **Leaderboard:** [https://asappresearch.github.io/slue-toolkit/leaderboard_v0.2.html](https://asappresearch.github.io/slue-toolkit/leaderboard_v0.2.html)
- **Size of downloaded dataset files:** 1.95 GB
- **Size of the generated dataset:** 9.59 MB
- **Total amount of disk used:** 1.95 GB
### Dataset Summary
We introduce the Spoken Language Understanding Evaluation (SLUE) benchmark. The goals of our work are to
- Track research progress on multiple SLU tasks
- Facilitate the development of pre-trained representations by providing fine-tuning and eval sets for a variety of SLU tasks
- Foster the open exchange of research by focusing on freely available datasets that all academic and industrial groups can easily use.
For this benchmark, we provide new annotation of publicly available, natural speech data for training and evaluation. We also provide a benchmark suite including code to download and pre-process the SLUE datasets, train the baseline models, and evaluate performance on SLUE tasks. Refer to [Toolkit](https://github.com/asappresearch/slue-toolkit) and [Paper](https://arxiv.org/pdf/2111.10367.pdf) for more details.
### Supported Tasks and Leaderboards
#### Automatic Speech Recognition (ASR)
Although this is not a SLU task, ASR can help analyze the performance of downstream SLU tasks on the same domain. Additionally, pipeline approaches depend on ASR outputs, making ASR relevant to SLU. ASR is evaluated using word error rate (WER).
#### Named Entity Recognition (NER)
Named entity recognition involves detecting the named entities and their tags (types) in a given sentence. We evaluate performance using micro-averaged F1 and label-F1 scores. The F1 score evaluates an unordered list of named entity phrase and tag pairs predicted for each sentence. Only the tag predictions are considered for label-F1.
#### Sentiment Analysis (SA)
Sentiment analysis refers to classifying a given speech segment as having negative, neutral, or positive sentiment. We evaluate SA using macro-averaged (unweighted) recall and F1 scores.[More Information Needed]
#### How-to-submit for your test set evaluation
See here https://asappresearch.github.io/slue-toolkit/how-to-submit.html
### Languages
The language data in SLUE is in English.
## Dataset Structure
### Data Instances
#### voxpopuli
- **Size of downloaded dataset files:** 398.45 MB
- **Size of the generated dataset:** 5.81 MB
- **Total amount of disk used:** 404.26 MB
An example of 'train' looks as follows.
```
{'id': '20131007-0900-PLENARY-19-en_20131007-21:26:04_3',
'audio': {'path': '/Users/username/.cache/huggingface/datasets/downloads/extracted/e35757b0971ac7ff5e2fcdc301bba0364857044be55481656e2ade6f7e1fd372/slue-voxpopuli/fine-tune/20131007-0900-PLENARY-19-en_20131007-21:26:04_3.ogg',
'array': array([ 0.00132601, 0.00058881, -0.00052187, ..., 0.06857217,
0.07835515, 0.07845446], dtype=float32),
'sampling_rate': 16000},
'speaker_id': 'None',
'normalized_text': 'two thousand and twelve for instance the new brussels i regulation provides for the right for employees to sue several employers together and the right for employees to have access to courts in europe even if the employer is domiciled outside europe. the commission will',
'raw_text': '2012. For instance, the new Brussels I Regulation provides for the right for employees to sue several employers together and the right for employees to have access to courts in Europe, even if the employer is domiciled outside Europe. The Commission will',
'raw_ner': {'type': ['LOC', 'LOC', 'LAW', 'DATE'],
'start': [227, 177, 28, 0],
'length': [6, 6, 21, 4]},
'normalized_ner': {'type': ['LOC', 'LOC', 'LAW', 'DATE'],
'start': [243, 194, 45, 0],
'length': [6, 6, 21, 23]},
'raw_combined_ner': {'type': ['PLACE', 'PLACE', 'LAW', 'WHEN'],
'start': [227, 177, 28, 0],
'length': [6, 6, 21, 4]},
'normalized_combined_ner': {'type': ['PLACE', 'PLACE', 'LAW', 'WHEN'],
'start': [243, 194, 45, 0],
'length': [6, 6, 21, 23]}}
```
#### voxceleb
- **Size of downloaded dataset files:** 1.55 GB
- **Size of the generated dataset:** 3.78 MB
- **Total amount of disk used:** 1.55 GB
An example of 'train' looks as follows.
```
{'id': 'id10059_229vKIGbxrI_00004',
'audio': {'path': '/Users/felixwu/.cache/huggingface/datasets/downloads/extracted/400facb6d2f2496ebcd58a5ffe5fbf2798f363d1b719b888d28a29b872751626/slue-voxceleb/fine-tune_raw/id10059_229vKIGbxrI_00004.flac',
'array': array([-0.00442505, -0.00204468, 0.00628662, ..., 0.00158691,
0.00100708, 0.00033569], dtype=float32),
'sampling_rate': 16000},
'speaker_id': 'id10059',
'normalized_text': 'of god what is a creator the almighty that uh',
'sentiment': 'Neutral',
'start_second': 0.45,
'end_second': 4.52}
```
### Data Fields
#### voxpopuli
- `id`: a `string` id of an instance.
- `audio`: audio feature of the raw audio. It is a dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- `speaker_id`: a `string` of the speaker id.
- `raw_text`: a `string` feature that contains the raw transcription of the audio.
- `normalized_text`: a `string` feature that contains the normalized transcription of the audio which is **used in the standard evaluation**.
- `raw_ner`: the NER annotation of the `raw_text` using the same 18 NER classes as OntoNotes.
- `normalized_ner`: the NER annotation of the `normalized_text` using the same 18 NER classes as OntoNotes.
- `raw_combined_ner`: the NER annotation of the `raw_text` using our 7 NER classes (`WHEN`, `QUANT`, `PLACE`, `NORP`, `ORG`, `LAW`, `PERSON`).
- `normalized_combined_ner`: the NER annotation of the `normalized_text` using our 7 NER classes (`WHEN`, `QUANT`, `PLACE`, `NORP`, `ORG`, `LAW`, `PERSON`) which is **used in the standard evaluation**.
Each NER annotation is a dictionary containing three lists: `type`, `start`, and `length`. `type` is a list of the NER tag types. `start` is a list of the start character position of each named entity in the corresponding text. `length` is a list of the number of characters of each named entity.
#### voxceleb
- `id`: a `string` id of an instance.
- `audio`: audio feature of the raw audio. Please use `start_second` and `end_second` to crop the transcribed segment. For example, `dataset[0]["audio"]["array"][int(dataset[0]["start_second"] * dataset[0]["audio"]["sample_rate"]):int(dataset[0]["end_second"] * dataset[0]["audio"]["sample_rate"])]`. It is a dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- `speaker_id`: a `string` of the speaker id.
- `normalized_text`: a `string` feature that contains the transcription of the audio segment.
- `sentiment`: a `string` feature which can be `Negative`, `Neutral`, or `Positive`.
- `start_second`: a `float` feature that specifies the start second of the audio segment.
- `end_second`: a `float` feature that specifies the end second of the audio segment.
### Data Splits
| |train|validation|test|
|---------|----:|---------:|---:|
|voxpopuli| 5000| 1753|1842|
|voxceleb | 5777| 1454|3553|
Here we use the standard split names in Huggingface's datasets, so the `train` and `validation` splits are the original `fine-tune` and `dev` splits of SLUE datasets, respectively.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
#### SLUE-VoxPopuli Dataset
SLUE-VoxPopuli dataset contains a subset of VoxPopuli dataset and the copyright of this subset remains the same with the original license, CC0. See also European Parliament's legal notice (https://www.europarl.europa.eu/legal-notice/en/)
Additionally, we provide named entity annotation (normalized_ner and raw_ner column in .tsv files) and it is covered with the same license as CC0.
#### SLUE-VoxCeleb Dataset
SLUE-VoxCeleb Dataset contains a subset of OXFORD VoxCeleb dataset and the copyright of this subset remains the same Creative Commons Attribution 4.0 International license as below. Additionally, we provide transcription, sentiment annotation and timestamp (start, end) that follows the same license to OXFORD VoxCeleb dataset.
##### Original License of OXFORD VGG VoxCeleb Dataset
VoxCeleb1 contains over 100,000 utterances for 1,251 celebrities, extracted from videos uploaded to YouTube.
VoxCeleb2 contains over a million utterances for 6,112 celebrities, extracted from videos uploaded to YouTube.
The speakers span a wide range of different ethnicities, accents, professions and ages.
We provide Youtube URLs, associated face detections, and timestamps, as
well as cropped audio segments and cropped face videos from the
dataset. The copyright of both the original and cropped versions
of the videos remains with the original owners.
The data is covered under a Creative Commons
Attribution 4.0 International license (Please read the
license terms here. https://creativecommons.org/licenses/by/4.0/).
Downloading this dataset implies agreement to follow the same
conditions for any modification and/or
re-distribution of the dataset in any form.
Additionally any entity using this dataset agrees to the following conditions:
THIS DATASET IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
HOLDER BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Please cite [1,2] below if you make use of the dataset.
[1] J. S. Chung, A. Nagrani, A. Zisserman
VoxCeleb2: Deep Speaker Recognition
INTERSPEECH, 2018.
[2] A. Nagrani, J. S. Chung, A. Zisserman
VoxCeleb: a large-scale speaker identification dataset
INTERSPEECH, 2017
### Citation Information
```
@inproceedings{shon2022slue,
title={Slue: New benchmark tasks for spoken language understanding evaluation on natural speech},
author={Shon, Suwon and Pasad, Ankita and Wu, Felix and Brusco, Pablo and Artzi, Yoav and Livescu, Karen and Han, Kyu J},
booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7927--7931},
year={2022},
organization={IEEE}
}
```
### Contributions
Thanks to [@fwu-asapp](https://github.com/fwu-asapp) for adding this dataset. | [
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|
open-llm-leaderboard/details_liuxiang886__llama2-70B-qlora-gpt4 | open-llm-leaderboard | 2023-09-17T18:00:18Z | 276 | 0 | [
"region:us"
]
| null | 2023-08-17T23:56:18Z | ---
pretty_name: Evaluation run of liuxiang886/llama2-70B-qlora-gpt4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [liuxiang886/llama2-70B-qlora-gpt4](https://huggingface.co/liuxiang886/llama2-70B-qlora-gpt4)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_liuxiang886__llama2-70B-qlora-gpt4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T18:00:05.987903](https://huggingface.co/datasets/open-llm-leaderboard/details_liuxiang886__llama2-70B-qlora-gpt4/blob/main/results_2023-09-17T18-00-05.987903.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.4848993288590604,\n\
\ \"em_stderr\": 0.005118132215061967,\n \"f1\": 0.5715404781879219,\n\
\ \"f1_stderr\": 0.004685062097512246,\n \"acc\": 0.5587922375481174,\n\
\ \"acc_stderr\": 0.011536318547544595\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.4848993288590604,\n \"em_stderr\": 0.005118132215061967,\n\
\ \"f1\": 0.5715404781879219,\n \"f1_stderr\": 0.004685062097512246\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.288855193328279,\n \
\ \"acc_stderr\": 0.012484219800126664\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.8287292817679558,\n \"acc_stderr\": 0.010588417294962526\n\
\ }\n}\n```"
repo_url: https://huggingface.co/liuxiang886/llama2-70B-qlora-gpt4
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|arc:challenge|25_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T18_00_05.987903
path:
- '**/details_harness|drop|3_2023-09-17T18-00-05.987903.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T18-00-05.987903.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T18_00_05.987903
path:
- '**/details_harness|gsm8k|5_2023-09-17T18-00-05.987903.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T18-00-05.987903.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hellaswag|10_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T20:45:03.475580.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T20:45:03.475580.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T20:45:03.475580.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T18_00_05.987903
path:
- '**/details_harness|winogrande|5_2023-09-17T18-00-05.987903.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T18-00-05.987903.parquet'
- config_name: results
data_files:
- split: 2023_08_09T20_45_03.475580
path:
- results_2023-08-09T20:45:03.475580.parquet
- split: 2023_09_17T18_00_05.987903
path:
- results_2023-09-17T18-00-05.987903.parquet
- split: latest
path:
- results_2023-09-17T18-00-05.987903.parquet
---
# Dataset Card for Evaluation run of liuxiang886/llama2-70B-qlora-gpt4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/liuxiang886/llama2-70B-qlora-gpt4
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [liuxiang886/llama2-70B-qlora-gpt4](https://huggingface.co/liuxiang886/llama2-70B-qlora-gpt4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_liuxiang886__llama2-70B-qlora-gpt4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T18:00:05.987903](https://huggingface.co/datasets/open-llm-leaderboard/details_liuxiang886__llama2-70B-qlora-gpt4/blob/main/results_2023-09-17T18-00-05.987903.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.4848993288590604,
"em_stderr": 0.005118132215061967,
"f1": 0.5715404781879219,
"f1_stderr": 0.004685062097512246,
"acc": 0.5587922375481174,
"acc_stderr": 0.011536318547544595
},
"harness|drop|3": {
"em": 0.4848993288590604,
"em_stderr": 0.005118132215061967,
"f1": 0.5715404781879219,
"f1_stderr": 0.004685062097512246
},
"harness|gsm8k|5": {
"acc": 0.288855193328279,
"acc_stderr": 0.012484219800126664
},
"harness|winogrande|5": {
"acc": 0.8287292817679558,
"acc_stderr": 0.010588417294962526
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_fireballoon__baichuan-vicuna-chinese-7b | open-llm-leaderboard | 2023-09-17T14:20:23Z | 276 | 0 | [
"region:us"
]
| null | 2023-08-18T11:13:19Z | ---
pretty_name: Evaluation run of fireballoon/baichuan-vicuna-chinese-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [fireballoon/baichuan-vicuna-chinese-7b](https://huggingface.co/fireballoon/baichuan-vicuna-chinese-7b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_fireballoon__baichuan-vicuna-chinese-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T14:20:11.480532](https://huggingface.co/datasets/open-llm-leaderboard/details_fireballoon__baichuan-vicuna-chinese-7b/blob/main/results_2023-09-17T14-20-11.480532.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.22242030201342283,\n\
\ \"em_stderr\": 0.00425891841660003,\n \"f1\": 0.2740048238255038,\n\
\ \"f1_stderr\": 0.004278992735739422,\n \"acc\": 0.3619266227972807,\n\
\ \"acc_stderr\": 0.00976430949757211\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.22242030201342283,\n \"em_stderr\": 0.00425891841660003,\n\
\ \"f1\": 0.2740048238255038,\n \"f1_stderr\": 0.004278992735739422\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05534495830174375,\n \
\ \"acc_stderr\": 0.006298221796179566\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6685082872928176,\n \"acc_stderr\": 0.013230397198964655\n\
\ }\n}\n```"
repo_url: https://huggingface.co/fireballoon/baichuan-vicuna-chinese-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|arc:challenge|25_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T14_20_11.480532
path:
- '**/details_harness|drop|3_2023-09-17T14-20-11.480532.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T14-20-11.480532.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T14_20_11.480532
path:
- '**/details_harness|gsm8k|5_2023-09-17T14-20-11.480532.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T14-20-11.480532.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hellaswag|10_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-10T10:02:03.270696.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-10T10:02:03.270696.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-10T10:02:03.270696.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T14_20_11.480532
path:
- '**/details_harness|winogrande|5_2023-09-17T14-20-11.480532.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T14-20-11.480532.parquet'
- config_name: results
data_files:
- split: 2023_08_10T10_02_03.270696
path:
- results_2023-08-10T10:02:03.270696.parquet
- split: 2023_09_17T14_20_11.480532
path:
- results_2023-09-17T14-20-11.480532.parquet
- split: latest
path:
- results_2023-09-17T14-20-11.480532.parquet
---
# Dataset Card for Evaluation run of fireballoon/baichuan-vicuna-chinese-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/fireballoon/baichuan-vicuna-chinese-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [fireballoon/baichuan-vicuna-chinese-7b](https://huggingface.co/fireballoon/baichuan-vicuna-chinese-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_fireballoon__baichuan-vicuna-chinese-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T14:20:11.480532](https://huggingface.co/datasets/open-llm-leaderboard/details_fireballoon__baichuan-vicuna-chinese-7b/blob/main/results_2023-09-17T14-20-11.480532.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.22242030201342283,
"em_stderr": 0.00425891841660003,
"f1": 0.2740048238255038,
"f1_stderr": 0.004278992735739422,
"acc": 0.3619266227972807,
"acc_stderr": 0.00976430949757211
},
"harness|drop|3": {
"em": 0.22242030201342283,
"em_stderr": 0.00425891841660003,
"f1": 0.2740048238255038,
"f1_stderr": 0.004278992735739422
},
"harness|gsm8k|5": {
"acc": 0.05534495830174375,
"acc_stderr": 0.006298221796179566
},
"harness|winogrande|5": {
"acc": 0.6685082872928176,
"acc_stderr": 0.013230397198964655
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_llama-anon__instruct-13b | open-llm-leaderboard | 2023-09-17T02:24:19Z | 276 | 0 | [
"region:us"
]
| null | 2023-08-18T11:55:51Z | ---
pretty_name: Evaluation run of llama-anon/instruct-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [llama-anon/instruct-13b](https://huggingface.co/llama-anon/instruct-13b) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_llama-anon__instruct-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T02:24:06.962063](https://huggingface.co/datasets/open-llm-leaderboard/details_llama-anon__instruct-13b/blob/main/results_2023-09-17T02-24-06.962063.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.31438758389261745,\n\
\ \"em_stderr\": 0.004754574768123327,\n \"f1\": 0.3769809144295322,\n\
\ \"f1_stderr\": 0.004680725874888402,\n \"acc\": 0.37917019961428294,\n\
\ \"acc_stderr\": 0.00825067276736675\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.31438758389261745,\n \"em_stderr\": 0.004754574768123327,\n\
\ \"f1\": 0.3769809144295322,\n \"f1_stderr\": 0.004680725874888402\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.022744503411675512,\n \
\ \"acc_stderr\": 0.004106620637749704\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7355958958168903,\n \"acc_stderr\": 0.012394724896983799\n\
\ }\n}\n```"
repo_url: https://huggingface.co/llama-anon/instruct-13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|arc:challenge|25_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T02_24_06.962063
path:
- '**/details_harness|drop|3_2023-09-17T02-24-06.962063.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T02-24-06.962063.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T02_24_06.962063
path:
- '**/details_harness|gsm8k|5_2023-09-17T02-24-06.962063.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T02-24-06.962063.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hellaswag|10_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:48:36.816075.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T18:48:36.816075.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T18:48:36.816075.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T02_24_06.962063
path:
- '**/details_harness|winogrande|5_2023-09-17T02-24-06.962063.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T02-24-06.962063.parquet'
- config_name: results
data_files:
- split: 2023_07_19T18_48_36.816075
path:
- results_2023-07-19T18:48:36.816075.parquet
- split: 2023_09_17T02_24_06.962063
path:
- results_2023-09-17T02-24-06.962063.parquet
- split: latest
path:
- results_2023-09-17T02-24-06.962063.parquet
---
# Dataset Card for Evaluation run of llama-anon/instruct-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/llama-anon/instruct-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [llama-anon/instruct-13b](https://huggingface.co/llama-anon/instruct-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_llama-anon__instruct-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T02:24:06.962063](https://huggingface.co/datasets/open-llm-leaderboard/details_llama-anon__instruct-13b/blob/main/results_2023-09-17T02-24-06.962063.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.31438758389261745,
"em_stderr": 0.004754574768123327,
"f1": 0.3769809144295322,
"f1_stderr": 0.004680725874888402,
"acc": 0.37917019961428294,
"acc_stderr": 0.00825067276736675
},
"harness|drop|3": {
"em": 0.31438758389261745,
"em_stderr": 0.004754574768123327,
"f1": 0.3769809144295322,
"f1_stderr": 0.004680725874888402
},
"harness|gsm8k|5": {
"acc": 0.022744503411675512,
"acc_stderr": 0.004106620637749704
},
"harness|winogrande|5": {
"acc": 0.7355958958168903,
"acc_stderr": 0.012394724896983799
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_haonan-li__bactrian-x-llama-13b-merged | open-llm-leaderboard | 2023-09-18T01:46:36Z | 276 | 0 | [
"region:us"
]
| null | 2023-08-18T12:02:29Z | ---
pretty_name: Evaluation run of haonan-li/bactrian-x-llama-13b-merged
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [haonan-li/bactrian-x-llama-13b-merged](https://huggingface.co/haonan-li/bactrian-x-llama-13b-merged)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_haonan-li__bactrian-x-llama-13b-merged\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-18T01:46:24.914160](https://huggingface.co/datasets/open-llm-leaderboard/details_haonan-li__bactrian-x-llama-13b-merged/blob/main/results_2023-09-18T01-46-24.914160.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.2553481543624161,\n\
\ \"em_stderr\": 0.004465629087714431,\n \"f1\": 0.31091652684563814,\n\
\ \"f1_stderr\": 0.004443751758152442,\n \"acc\": 0.3974435920159074,\n\
\ \"acc_stderr\": 0.009316527734088942\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.2553481543624161,\n \"em_stderr\": 0.004465629087714431,\n\
\ \"f1\": 0.31091652684563814,\n \"f1_stderr\": 0.004443751758152442\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05534495830174375,\n \
\ \"acc_stderr\": 0.006298221796179595\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.739542225730071,\n \"acc_stderr\": 0.012334833671998289\n\
\ }\n}\n```"
repo_url: https://huggingface.co/haonan-li/bactrian-x-llama-13b-merged
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T19_16_59.483464
path:
- '**/details_harness|arc:challenge|25_2023-07-19T19:16:59.483464.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T19:16:59.483464.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_18T01_46_24.914160
path:
- '**/details_harness|drop|3_2023-09-18T01-46-24.914160.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-18T01-46-24.914160.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_18T01_46_24.914160
path:
- '**/details_harness|gsm8k|5_2023-09-18T01-46-24.914160.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-18T01-46-24.914160.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T19_16_59.483464
path:
- '**/details_harness|hellaswag|10_2023-07-19T19:16:59.483464.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T19:16:59.483464.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T19_16_59.483464
path:
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- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:16:59.483464.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:16:59.483464.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:16:59.483464.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:16:59.483464.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:16:59.483464.parquet'
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path:
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- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:16:59.483464.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:16:59.483464.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:16:59.483464.parquet'
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:16:59.483464.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T19_16_59.483464
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:16:59.483464.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:16:59.483464.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T19_16_59.483464
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:16:59.483464.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:16:59.483464.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T19_16_59.483464
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:16:59.483464.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:16:59.483464.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T19_16_59.483464
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:16:59.483464.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:16:59.483464.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T19_16_59.483464
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:16:59.483464.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:16:59.483464.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T19_16_59.483464
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:16:59.483464.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:16:59.483464.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T19_16_59.483464
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T19:16:59.483464.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T19:16:59.483464.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_18T01_46_24.914160
path:
- '**/details_harness|winogrande|5_2023-09-18T01-46-24.914160.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-18T01-46-24.914160.parquet'
- config_name: results
data_files:
- split: 2023_07_19T19_16_59.483464
path:
- results_2023-07-19T19:16:59.483464.parquet
- split: 2023_09_18T01_46_24.914160
path:
- results_2023-09-18T01-46-24.914160.parquet
- split: latest
path:
- results_2023-09-18T01-46-24.914160.parquet
---
# Dataset Card for Evaluation run of haonan-li/bactrian-x-llama-13b-merged
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/haonan-li/bactrian-x-llama-13b-merged
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [haonan-li/bactrian-x-llama-13b-merged](https://huggingface.co/haonan-li/bactrian-x-llama-13b-merged) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_haonan-li__bactrian-x-llama-13b-merged",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-18T01:46:24.914160](https://huggingface.co/datasets/open-llm-leaderboard/details_haonan-li__bactrian-x-llama-13b-merged/blob/main/results_2023-09-18T01-46-24.914160.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.2553481543624161,
"em_stderr": 0.004465629087714431,
"f1": 0.31091652684563814,
"f1_stderr": 0.004443751758152442,
"acc": 0.3974435920159074,
"acc_stderr": 0.009316527734088942
},
"harness|drop|3": {
"em": 0.2553481543624161,
"em_stderr": 0.004465629087714431,
"f1": 0.31091652684563814,
"f1_stderr": 0.004443751758152442
},
"harness|gsm8k|5": {
"acc": 0.05534495830174375,
"acc_stderr": 0.006298221796179595
},
"harness|winogrande|5": {
"acc": 0.739542225730071,
"acc_stderr": 0.012334833671998289
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
KATANABRAVE/stories | KATANABRAVE | 2023-08-25T06:37:13Z | 276 | 0 | [
"license:llama2",
"region:us"
]
| null | 2023-08-25T03:49:19Z | ---
license: llama2
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: title
dtype: string
- name: article
dtype: string
- name: text
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 110879624
num_examples: 8500
- name: validation
num_bytes: 3383807
num_examples: 277
download_size: 48437278
dataset_size: 114263431
---
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|
open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-Python-v1 | open-llm-leaderboard | 2023-09-17T16:02:50Z | 276 | 0 | [
"region:us"
]
| null | 2023-08-26T05:45:48Z | ---
pretty_name: Evaluation run of Phind/Phind-CodeLlama-34B-Python-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Phind/Phind-CodeLlama-34B-Python-v1](https://huggingface.co/Phind/Phind-CodeLlama-34B-Python-v1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-Python-v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T16:02:38.595550](https://huggingface.co/datasets/open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-Python-v1/blob/main/results_2023-09-17T16-02-38.595550.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.265625,\n \
\ \"em_stderr\": 0.004523067479107055,\n \"f1\": 0.3185192953020138,\n\
\ \"f1_stderr\": 0.004482746835839152,\n \"acc\": 0.4517772845779581,\n\
\ \"acc_stderr\": 0.012170333746109104\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.265625,\n \"em_stderr\": 0.004523067479107055,\n \
\ \"f1\": 0.3185192953020138,\n \"f1_stderr\": 0.004482746835839152\n \
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.21531463229719486,\n \
\ \"acc_stderr\": 0.011322096294579654\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6882399368587214,\n \"acc_stderr\": 0.013018571197638551\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Phind/Phind-CodeLlama-34B-Python-v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|arc:challenge|25_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T16_02_38.595550
path:
- '**/details_harness|drop|3_2023-09-17T16-02-38.595550.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T16-02-38.595550.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T16_02_38.595550
path:
- '**/details_harness|gsm8k|5_2023-09-17T16-02-38.595550.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T16-02-38.595550.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hellaswag|10_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T05:45:26.681000.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T05:45:26.681000.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T05:45:26.681000.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T16_02_38.595550
path:
- '**/details_harness|winogrande|5_2023-09-17T16-02-38.595550.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T16-02-38.595550.parquet'
- config_name: results
data_files:
- split: 2023_08_26T05_45_26.681000
path:
- results_2023-08-26T05:45:26.681000.parquet
- split: 2023_09_17T16_02_38.595550
path:
- results_2023-09-17T16-02-38.595550.parquet
- split: latest
path:
- results_2023-09-17T16-02-38.595550.parquet
---
# Dataset Card for Evaluation run of Phind/Phind-CodeLlama-34B-Python-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Phind/Phind-CodeLlama-34B-Python-v1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Phind/Phind-CodeLlama-34B-Python-v1](https://huggingface.co/Phind/Phind-CodeLlama-34B-Python-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-Python-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T16:02:38.595550](https://huggingface.co/datasets/open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-Python-v1/blob/main/results_2023-09-17T16-02-38.595550.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.265625,
"em_stderr": 0.004523067479107055,
"f1": 0.3185192953020138,
"f1_stderr": 0.004482746835839152,
"acc": 0.4517772845779581,
"acc_stderr": 0.012170333746109104
},
"harness|drop|3": {
"em": 0.265625,
"em_stderr": 0.004523067479107055,
"f1": 0.3185192953020138,
"f1_stderr": 0.004482746835839152
},
"harness|gsm8k|5": {
"acc": 0.21531463229719486,
"acc_stderr": 0.011322096294579654
},
"harness|winogrande|5": {
"acc": 0.6882399368587214,
"acc_stderr": 0.013018571197638551
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_zarakiquemparte__kuchiki-l2-7b | open-llm-leaderboard | 2023-10-27T01:56:21Z | 276 | 0 | [
"region:us"
]
| null | 2023-09-22T00:21:37Z | ---
pretty_name: Evaluation run of zarakiquemparte/kuchiki-l2-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [zarakiquemparte/kuchiki-l2-7b](https://huggingface.co/zarakiquemparte/kuchiki-l2-7b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_zarakiquemparte__kuchiki-l2-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-27T01:56:08.960825](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__kuchiki-l2-7b/blob/main/results_2023-10-27T01-56-08.960825.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.27611157718120805,\n\
\ \"em_stderr\": 0.004578442614328635,\n \"f1\": 0.35264576342282045,\n\
\ \"f1_stderr\": 0.004531331117609875,\n \"acc\": 0.38779557831535094,\n\
\ \"acc_stderr\": 0.009079399041337897\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.27611157718120805,\n \"em_stderr\": 0.004578442614328635,\n\
\ \"f1\": 0.35264576342282045,\n \"f1_stderr\": 0.004531331117609875\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04473085670962851,\n \
\ \"acc_stderr\": 0.005693886131407058\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7308602999210734,\n \"acc_stderr\": 0.012464911951268734\n\
\ }\n}\n```"
repo_url: https://huggingface.co/zarakiquemparte/kuchiki-l2-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|arc:challenge|25_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_27T01_56_08.960825
path:
- '**/details_harness|drop|3_2023-10-27T01-56-08.960825.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-27T01-56-08.960825.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_27T01_56_08.960825
path:
- '**/details_harness|gsm8k|5_2023-10-27T01-56-08.960825.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-27T01-56-08.960825.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hellaswag|10_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T00-21-14.015290.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-22T00-21-14.015290.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-22T00-21-14.015290.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_27T01_56_08.960825
path:
- '**/details_harness|winogrande|5_2023-10-27T01-56-08.960825.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-27T01-56-08.960825.parquet'
- config_name: results
data_files:
- split: 2023_09_22T00_21_14.015290
path:
- results_2023-09-22T00-21-14.015290.parquet
- split: 2023_10_27T01_56_08.960825
path:
- results_2023-10-27T01-56-08.960825.parquet
- split: latest
path:
- results_2023-10-27T01-56-08.960825.parquet
---
# Dataset Card for Evaluation run of zarakiquemparte/kuchiki-l2-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/zarakiquemparte/kuchiki-l2-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [zarakiquemparte/kuchiki-l2-7b](https://huggingface.co/zarakiquemparte/kuchiki-l2-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_zarakiquemparte__kuchiki-l2-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T01:56:08.960825](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__kuchiki-l2-7b/blob/main/results_2023-10-27T01-56-08.960825.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.27611157718120805,
"em_stderr": 0.004578442614328635,
"f1": 0.35264576342282045,
"f1_stderr": 0.004531331117609875,
"acc": 0.38779557831535094,
"acc_stderr": 0.009079399041337897
},
"harness|drop|3": {
"em": 0.27611157718120805,
"em_stderr": 0.004578442614328635,
"f1": 0.35264576342282045,
"f1_stderr": 0.004531331117609875
},
"harness|gsm8k|5": {
"acc": 0.04473085670962851,
"acc_stderr": 0.005693886131407058
},
"harness|winogrande|5": {
"acc": 0.7308602999210734,
"acc_stderr": 0.012464911951268734
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
coastalcph/mutability_classifier-1-n | coastalcph | 2023-11-04T11:14:13Z | 276 | 0 | [
"region:us"
]
| null | 2023-11-04T11:14:08Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: query
dtype: string
- name: answer
list:
- name: wikidata_id
dtype: string
- name: name
dtype: string
- name: id
dtype: string
- name: relation
dtype: string
- name: date
dtype: int64
- name: type
dtype: string
- name: is_mutable
dtype: int64
splits:
- name: train
num_bytes: 1199436.065450644
num_examples: 6824
- name: validation
num_bytes: 1017521.3408544267
num_examples: 5911
- name: test
num_bytes: 837675.0175438597
num_examples: 4256
download_size: 1322347
dataset_size: 3054632.4238489303
---
# Dataset Card for "mutability_classifier-1-n"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
bswac | null | 2022-11-03T16:15:55Z | 275 | 0 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"language:bs",
"license:cc-by-sa-3.0",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- bs
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: BsWac
dataset_info:
features:
- name: sentence
dtype: string
config_name: bswac
splits:
- name: train
num_bytes: 9156258478
num_examples: 354581267
download_size: 1988514951
dataset_size: 9156258478
---
# Dataset Card for BsWac
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://nlp.ffzg.hr/resources/corpora/bswac/
- **Repository:** https://www.clarin.si/repository/xmlui/handle/11356/1062
- **Paper:** http://nlp.ffzg.hr/data/publications/nljubesi/ljubesic14-bs.pdf
- **Leaderboard:**
- **Point of Contact:** [Nikola Ljubešič](mailto:[email protected])
### Dataset Summary
The Bosnian web corpus bsWaC was built by crawling the .ba top-level domain in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and language identification (Bosnian vs. Croatian vs. Serbian).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is monolingual in Bosnian language.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@misc{11356/1062,
title = {Bosnian web corpus {bsWaC} 1.1},
author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip},
url = {http://hdl.handle.net/11356/1062},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
year = {2016} }
```
### Contributions
Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset. | [
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|
opus_elhuyar | null | 2022-11-03T16:07:47Z | 275 | 0 | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:es",
"language:eu",
"license:unknown",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- es
- eu
license:
- unknown
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusElhuyar
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- es
- eu
config_name: es-eu
splits:
- name: train
num_bytes: 127833939
num_examples: 642348
download_size: 44468751
dataset_size: 127833939
---
# Dataset Card for [opus_elhuyar]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[Opus Elhuyar](http://opus.nlpl.eu/Elhuyar.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Dataset provided by the foundation Elhuyar (http://webcorpusak.elhuyar.eus/sarrera_paraleloa.html) and submitted to OPUS by Joseba Garcia Beaumont
### Supported Tasks and Leaderboards
The underlying task is machine translation from Spanish to Basque
### Languages
Spanish to Basque
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. | [
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|
tep_en_fa_para | null | 2022-11-03T16:08:03Z | 275 | 1 | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:fa",
"license:unknown",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- fa
license:
- unknown
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: TepEnFaPara
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- en
- fa
config_name: en-fa
splits:
- name: train
num_bytes: 58735557
num_examples: 612087
download_size: 16353318
dataset_size: 58735557
---
# Dataset Card for [tep_en_fa_para]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[TEP: Tehran English-Persian parallel corpus](http://opus.nlpl.eu/TEP.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
TEP: Tehran English-Persian parallel corpus. The first free Eng-Per corpus, provided by the Natural Language and Text Processing Laboratory, University of Tehran.
### Supported Tasks and Leaderboards
The underlying task is machine translation for language pair English-Persian
### Languages
English, Persian
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
M. T. Pilevar, H. Faili, and A. H. Pilevar, “TEP: Tehran English-Persian Parallel Corpus”, in proceedings of 12th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing-2011).
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. | [
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|
turkish_ner | null | 2023-01-25T14:54:39Z | 275 | 5 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:tr",
"license:cc-by-4.0",
"arxiv:1702.02363",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- tr
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: TurkishNer
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: domain
dtype:
class_label:
names:
'0': architecture
'1': basketball
'2': book
'3': business
'4': education
'5': fictional_universe
'6': film
'7': food
'8': geography
'9': government
'10': law
'11': location
'12': military
'13': music
'14': opera
'15': organization
'16': people
'17': religion
'18': royalty
'19': soccer
'20': sports
'21': theater
'22': time
'23': travel
'24': tv
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PERSON
'2': I-PERSON
'3': B-ORGANIZATION
'4': I-ORGANIZATION
'5': B-LOCATION
'6': I-LOCATION
'7': B-MISC
'8': I-MISC
splits:
- name: train
num_bytes: 177658278
num_examples: 532629
download_size: 204393976
dataset_size: 177658278
---
# Dataset Card for turkish_ner
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://arxiv.org/abs/1702.02363
- **Repository:** [Needs More Information]
- **Paper:** http://arxiv.org/abs/1702.02363
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [email protected]
### Dataset Summary
Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Turkish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
There's only the training set.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
H. Bahadir Sahin, Caglar Tirkaz, Eray Yildiz, Mustafa Tolga Eren and Omer Ozan Sonmez
### Licensing Information
Creative Commons Attribution 4.0 International
### Citation Information
@InProceedings@article{DBLP:journals/corr/SahinTYES17,
author = {H. Bahadir Sahin and
Caglar Tirkaz and
Eray Yildiz and
Mustafa Tolga Eren and
Omer Ozan Sonmez},
title = {Automatically Annotated Turkish Corpus for Named Entity Recognition
and Text Categorization using Large-Scale Gazetteers},
journal = {CoRR},
volume = {abs/1702.02363},
year = {2017},
url = {http://arxiv.org/abs/1702.02363},
archivePrefix = {arXiv},
eprint = {1702.02363},
timestamp = {Mon, 13 Aug 2018 16:46:36 +0200},
biburl = {https://dblp.org/rec/journals/corr/SahinTYES17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
### Contributions
Thanks to [@merveenoyan](https://github.com/merveenoyan) for adding this dataset. | [
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|
turku_ner_corpus | null | 2023-01-25T14:54:48Z | 275 | 0 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fi",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- fi
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Turku NER corpus
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-DATE
'1': B-EVENT
'2': B-LOC
'3': B-ORG
'4': B-PER
'5': B-PRO
'6': I-DATE
'7': I-EVENT
'8': I-LOC
'9': I-ORG
'10': I-PER
'11': I-PRO
'12': O
splits:
- name: train
num_bytes: 3257447
num_examples: 12217
- name: validation
num_bytes: 364223
num_examples: 1364
- name: test
num_bytes: 416644
num_examples: 1555
download_size: 1659911
dataset_size: 4038314
---
# Dataset Card for Turku NER corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://turkunlp.org/fin-ner.html
- **Repository:** https://github.com/TurkuNLP/turku-ner-corpus/
- **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.567/
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** {jouni.a.luoma,mhtoin,maria.h.pyykonen,mavela,sampo.pyysalo}@utu.f
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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]
|
GEM/SciDuet | GEM | 2022-10-24T15:30:06Z | 275 | 1 | [
"task_categories:other",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"text-to-slide",
"region:us"
]
| [
"other"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: SciDuet
tags:
- text-to-slide
---
# Dataset Card for GEM/SciDuet
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/GEM/SciDuet
- **Repository:** https://github.com/IBM/document2slides/tree/main/SciDuet-ACL
- **Paper:** https://aclanthology.org/2021.naacl-main.111/
- **Leaderboard:** N/A
- **Point of Contact:** N/A
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/SciDuet).
### Dataset Summary
This dataset supports the document-to-slide generation task where a model has to generate presentation slide content from the text of a document.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/SciDuet')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/SciDuet).
#### website
[Huggingface](https://huggingface.co/datasets/GEM/SciDuet)
#### paper
[ACL Anthology](https://aclanthology.org/2021.naacl-main.111/)
#### authors
Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy Wang
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Huggingface](https://huggingface.co/datasets/GEM/SciDuet)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/IBM/document2slides/tree/main/SciDuet-ACL)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2021.naacl-main.111/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{sun-etal-2021-d2s,
title = "{D}2{S}: Document-to-Slide Generation Via Query-Based Text Summarization",
author = "Sun, Edward and
Hou, Yufang and
Wang, Dakuo and
Zhang, Yunfeng and
Wang, Nancy X. R.",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.111",
doi = "10.18653/v1/2021.naacl-main.111",
pages = "1405--1418",
abstract = "Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benchmarking. In this work, we first contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years{'} NLP and ML conferences (e.g., ACL). Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach: 1) Use slide titles to retrieve relevant and engaging text, figures, and tables; 2) Summarize the retrieved context into bullet points with long-form question answering. Our evaluation suggests that long-form QA outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation.",
}
```
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Promote research on the task of document-to-slides generation
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Text-to-Slide
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
IBM Research
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy Wang
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
IBM Research
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Yufang Hou (IBM Research), Dakuo Wang (IBM Research)
### Dataset Structure
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
The original papers and slides (both are in PDF format) are carefully processed by a combination of PDF/Image processing tookits. The text contents from multiple slides that correspond to the same slide title are mreged.
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
Training, validation and testing data contain 136, 55, and 81 papers from ACL Anthology and their corresponding slides, respectively.
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The dataset integrated into GEM is the ACL portion of the whole dataset described in the [paper](https://aclanthology.org/2021.naacl-main.111), It contains the full Dev and Test sets, and a portion of the Train dataset.
Note that although we cannot release the whole training dataset due to copyright issues, researchers can still use our released data procurement code to generate the training dataset from the online ICML/NeurIPS anthologies.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
SciDuet is the first publicaly available dataset for the challenging task of document2slides generation, which requires a model has a good ability to "understand" long-form text, choose appropriate content and generate key points.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
content selection, long-form text undersanding and generation
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
content selection, long-form text undersanding and key points generation
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`ROUGE`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
Automatical Evaluation Metric: ROUGE
Human Evaluation: (Readability, Informativeness, Consistency)
1) Readability: The generated slide content is coherent, concise, and grammatically correct;
2) Informativeness: The generated slide provides sufficient and necessary information that corresponds to the given slide title, regardless of its similarity to the original slide;
3) Consistency: The generated slide content is similar to the original author’s reference slide.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
ROUGE + Human Evaluation
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
Paper "D2S: Document-to-Slide Generation Via Query-Based
Text Summarization" reports 20.47, 5.26 and 19.08 for ROUGE-1, ROUGE-2 and ROUGE-L (f-score).
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Provide a benchmark dataset for the document-to-slides task.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Other`
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Text on papers was extracted through Grobid. Figures andcaptions were extracted through pdffigures. Text on slides was extracted through IBM Watson Discovery package and OCR by pytesseract. Figures and tables that appear on slides and papers were linked through multiscale template matching by OpenCV. Further dataset
cleaning was performed with standard string-based
heuristics on sentence building, equation and floating caption removal, and duplicate line deletion.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
the slide context text shouldn't contain additional format information such as "*** University"
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
The original dataset was open-sourced under Apache-2.0.
Some of the original dataset creators are part of the GEM v2 dataset infrastructure team and take care of integrating this dataset into GEM.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
yes/very likely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`non-commercial use only`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`research use only`
### Known Technical Limitations
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|
allegro/klej-polemo2-in | allegro | 2022-08-30T06:57:28Z | 275 | 0 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:cc-by-sa-4.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: 'PolEmo2.0-IN'
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# klej-polemo2-in
## Description
The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, hotels, products, and university. It is human-annotated on a level of full reviews and individual sentences. It comprises over 8000 reviews, about 85% from the medicine and hotel domains.
We use the PolEmo2.0 dataset to form two tasks. Both use the same training dataset, i.e., reviews from medicine and hotel domains, but are evaluated on a different test set.
**In-Domain** is the first task, and we use accuracy to evaluate model performance within the in-domain context, i.e., on a test set of reviews from medicine and hotels domains.
## Tasks (input, output, and metrics)
The task is to predict the correct label of the review.
**Input** ('*text'* column): sentence
**Output** ('*target'* column): label for sentence sentiment ('zero': neutral, 'minus': negative, 'plus': positive, 'amb': ambiguous)
**Domain**: Online reviews
**Measurements**: Accuracy
**Example**:
Input: `Lekarz zalecił mi kurację alternatywną do dotychczasowej , więc jeszcze nie daję najwyższej oceny ( zobaczymy na ile okaże się skuteczna ) . Do Pana doktora nie mam zastrzeżeń : bardzo profesjonalny i kulturalny . Jedyny minus dotyczy gabinetu , który nie jest nowoczesny , co może zniechęcać pacjentki .`
Input (translated by DeepL): `The doctor recommended me an alternative treatment to the current one , so I do not yet give the highest rating ( we will see how effective it turns out to be ) . To the doctor I have no reservations : very professional and cultured . The only minus is about the office , which is not modern , which may discourage patients .`
Output: `amb` (ambiguous)
## Data splits
| Subset | Cardinality |
|:-----------|--------------:|
| train | 5783 |
| test | 722 |
| validation | 723 |
## Class distribution in train
| Class | Sentiment | train | validation | test |
|:------|:----------|------:|-----------:|------:|
| minus | positive | 0.379 | 0.375 | 0.416 |
| plus | negative | 0.271 | 0.289 | 0.273 |
| amb | ambiguous | 0.182 | 0.160 | 0.150 |
| zero | neutral | 0.168 | 0.176 | 0.162 |
## Citation
```
@inproceedings{kocon-etal-2019-multi,
title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
author = "Koco{\'n}, Jan and
Mi{\l}kowski, Piotr and
Za{\'s}ko-Zieli{\'n}ska, Monika",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K19-1092",
doi = "10.18653/v1/K19-1092",
pages = "980--991",
abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).",
}
```
## License
```
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
```
## Links
[HuggingFace](https://huggingface.co/datasets/allegro/klej-polemo2-in)
[Source](https://clarin-pl.eu/dspace/handle/11321/710)
[Paper](https://aclanthology.org/K19-1092/)
## Examples
### Loading
```python
from pprint import pprint
from datasets import load_dataset
dataset = load_dataset("allegro/klej-polemo2-in")
pprint(dataset['train'][0])
# {'sentence': 'Super lekarz i człowiek przez duże C . Bardzo duże doświadczenie '
# 'i trafne diagnozy . Wielka cierpliwość do ludzi starszych . Od '
# 'lat opiekuje się moją Mamą staruszką , i twierdzę , że mamy duże '
# 'szczęście , że mamy takiego lekarza . Naprawdę nie wiem cobyśmy '
# 'zrobili , gdyby nie Pan doktor . Dzięki temu , moja mama żyje . '
# 'Każda wizyta u specjalisty jest u niego konsultowana i uważam , '
# 'że jest lepszy od każdego z nich . Mamy do Niego prawie '
# 'nieograniczone zaufanie . Można wiele dobrego o Panu doktorze '
# 'jeszcze napisać . Niestety , ma bardzo dużo pacjentów , jest '
# 'przepracowany ( z tego powodu nawet obawiam się o jego zdrowie ) '
# 'i dostęp do niego jest trudny , ale zawsze możliwy .',
# 'target': '__label__meta_plus_m'}
```
### Evaluation
```python
import random
from pprint import pprint
from datasets import load_dataset, load_metric
dataset = load_dataset("allegro/klej-polemo2-in")
dataset = dataset.class_encode_column("target")
references = dataset["test"]["target"]
# generate random predictions
predictions = [random.randrange(max(references) + 1) for _ in range(len(references))]
acc = load_metric("accuracy")
f1 = load_metric("f1")
acc_score = acc.compute(predictions=predictions, references=references)
f1_score = f1.compute(predictions=predictions, references=references, average="macro")
pprint(acc_score)
pprint(f1_score)
# {'accuracy': 0.25069252077562326}
# {'f1': 0.23760962219870274}
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|
allegro/klej-polemo2-out | allegro | 2022-08-30T06:57:07Z | 275 | 0 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:cc-by-sa-4.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: 'PolEmo2.0-OUT'
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# klej-polemo2-out
## Description
The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, hotels, products, and university. It is human-annotated on a level of full reviews and individual sentences. It comprises over 8000 reviews, about 85% from the medicine and hotel domains.
We use the PolEmo2.0 dataset to form two tasks. Both use the same training dataset, i.e., reviews from medicine and hotel domains, but are evaluated on a different test set.
**Out-of-Domain** is the second task, and we test the model on out-of-domain reviews, i.e., from product and university domains. Since the original test sets for those domains are scarce (50 reviews each), we decided to use the original out-of-domain training set of 900 reviews for testing purposes and create a new split of development and test sets. As a result, the task consists of 1000 reviews, comparable in size to the in-domain test dataset of 1400 reviews.
## Tasks (input, output, and metrics)
The task is to predict the correct label of the review.
**Input** ('*text'* column): sentence
**Output** ('*target'* column): label for sentence sentiment ('zero': neutral, 'minus': negative, 'plus': positive, 'amb': ambiguous)
**Domain**: Online reviews
**Measurements**: Accuracy
**Example**:
Input: `Lekarz zalecił mi kurację alternatywną do dotychczasowej , więc jeszcze nie daję najwyższej oceny ( zobaczymy na ile okaże się skuteczna ) . Do Pana doktora nie mam zastrzeżeń : bardzo profesjonalny i kulturalny . Jedyny minus dotyczy gabinetu , który nie jest nowoczesny , co może zniechęcać pacjentki .`
Input (translated by DeepL): `The doctor recommended me an alternative treatment to the current one , so I do not yet give the highest rating ( we will see how effective it turns out to be ) . To the doctor I have no reservations : very professional and cultured . The only minus is about the office , which is not modern , which may discourage patients .`
Output: `amb` (ambiguous)
## Data splits
| Subset | Cardinality |
|:-----------|--------------:|
| train | 5783 |
| test | 722 |
| validation | 723 |
## Class distribution
| Class | Sentiment | train | validation | test |
|:------|:----------|------:|-----------:|------:|
| minus | positive | 0.379 | 0.334 | 0.368 |
| plus | negative | 0.271 | 0.332 | 0.302 |
| amb | ambiguous | 0.182 | 0.332 | 0.328 |
| zero | neutral | 0.168 | 0.002 | 0.002 |
## Citation
```
@inproceedings{kocon-etal-2019-multi,
title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
author = "Koco{\'n}, Jan and
Mi{\l}kowski, Piotr and
Za{\'s}ko-Zieli{\'n}ska, Monika",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K19-1092",
doi = "10.18653/v1/K19-1092",
pages = "980--991",
abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).",
}
```
## License
```
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
```
## Links
[HuggingFace](https://huggingface.co/datasets/allegro/klej-polemo2-out)
[Source](https://clarin-pl.eu/dspace/handle/11321/710)
[Paper](https://aclanthology.org/K19-1092/)
## Examples
### Loading
```python
from pprint import pprint
from datasets import load_dataset
dataset = load_dataset("allegro/klej-polemo2-out")
pprint(dataset['train'][0])
# {'sentence': 'Super lekarz i człowiek przez duże C . Bardzo duże doświadczenie '
# 'i trafne diagnozy . Wielka cierpliwość do ludzi starszych . Od '
# 'lat opiekuje się moją Mamą staruszką , i twierdzę , że mamy duże '
# 'szczęście , że mamy takiego lekarza . Naprawdę nie wiem cobyśmy '
# 'zrobili , gdyby nie Pan doktor . Dzięki temu , moja mama żyje . '
# 'Każda wizyta u specjalisty jest u niego konsultowana i uważam , '
# 'że jest lepszy od każdego z nich . Mamy do Niego prawie '
# 'nieograniczone zaufanie . Można wiele dobrego o Panu doktorze '
# 'jeszcze napisać . Niestety , ma bardzo dużo pacjentów , jest '
# 'przepracowany ( z tego powodu nawet obawiam się o jego zdrowie ) '
# 'i dostęp do niego jest trudny , ale zawsze możliwy .',
# 'target': '__label__meta_plus_m'}
```
### Evaluation
```python
import random
from pprint import pprint
from datasets import load_dataset, load_metric
dataset = load_dataset("allegro/klej-polemo2-out")
dataset = dataset.class_encode_column("target")
references = dataset["test"]["target"]
# generate random predictions
predictions = [random.randrange(max(references) + 1) for _ in range(len(references))]
acc = load_metric("accuracy")
f1 = load_metric("f1")
acc_score = acc.compute(predictions=predictions, references=references)
f1_score = f1.compute(predictions=predictions, references=references, average="macro")
pprint(acc_score)
pprint(f1_score)
# {'accuracy': 0.2894736842105263}
# {'f1': 0.2484406098784191}
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|
allegro/klej-psc | allegro | 2022-10-26T09:01:54Z | 275 | 0 | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:5K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:cc-by-sa-3.0",
"paraphrase-classification",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 5K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Polish Summaries Corpus
tags:
- paraphrase-classification
---
# klej-psc
## Description
The Polish Summaries Corpus (PSC) is a dataset of summaries for 569 news articles. The human annotators created five extractive summaries for each article by choosing approximately 5% of the original text. A different annotator created each summary. The subset of 154 articles was also supplemented with additional five abstractive summaries each, i.e., not created from the fragments of the original article. In huggingface version of this dataset, summaries of the same article are used as positive pairs, and the most similar summaries of different articles are sampled as negatives.
## Tasks (input, output, and metrics)
The task is to predict whether the extract text and summary are similar.
Based on PSC, we formulate a text-similarity task. We generate the positive pairs (i.e., referring
to the same article) using only those news articles with both extractive and abstractive summaries. We match each extractive summary with two least similar abstractive ones of the same article. To create negative pairs, we follow a similar procedure. We find two most similar abstractive summaries for each extractive summary, but from different articles.
**Input** (*'extract_text'*, *'summary_text'* columns): extract text and summary text sentences
**Output** (*'label'* column): label: 1 indicates summary is similar, 0 means that it is not similar
**Domain**: News articles
**Measurements**: F1-Score
**Example**:
Input: `Mit o potopie jest prastary, sięga czasów, gdy topniał lodowiec. Na skutek tego wydarzenia w dziejach planety, poziom mórz i oceanów podniósł się o kilkadziesiąt metrów. Potop polodowcowy z całą, naukową pewnością, miał miejsce, ale najprawdopodobniej został przez ludzkość przegapiony. I oto pojawiła się w tej sprawie kolejna glosa. Jej autorami są amerykańscy geofizycy.` ; `Dwójka amerykańskich geofizyków przedstawiła swój scenariusz pochodzenia mitu o potopie. Przed 7500 laty do będącego jeszcze jeziorem Morza Czarnego wlały się wezbrane wskutek topnienia lodowców wody Morza Śródziemnego. Geofizycy twierdzą, że dzięki temu rozkwitło rolnictwo, bo ludzie musieli migrować i szerzyć rolniczy tryb życia. Środowiska naukowe twierdzą jednak, że potop był tylko jednym z czynników ekspansji rolnictwa.`
Input (translated by DeepL): `The myth of the Flood is ancient, dating back to the time when the glacier melted. As a result of this event in the history of the planet, the level of the seas and oceans rose by several tens of meters. The post-glacial flood with all, scientific certainty, took place, but was most likely missed by mankind. And here is another gloss on the matter. Its authors are American geophysicists.` ; `Two American geophysicists presented their scenario of the origin of the Flood myth. 7500 years ago, the waters of the Mediterranean Sea flooded into the Black Sea, which was still a lake, due to the melting of glaciers. Geophysicists claim that this made agriculture flourish because people had to migrate and spread their agricultural lifestyle. However, the scientific community argues that the Flood was only one factor in the expansion of agriculture.`
Output: `1` (summary is similar)
## Data splits
| Subset | Cardinality |
| ----------- | ----------: |
| train | 4302 |
| val | 0 |
| test | 1078 |
## Class distribution
| Class | train | validation | test |
|:------------|--------:|-------------:|-------:|
| not similar | 0.705 | - | 0.696 |
| similar | 0.295 | - | 0.304 |
## Citation
```
@inproceedings{ogro:kop:14:lrec,
title={The {P}olish {S}ummaries {C}orpus},
author={Ogrodniczuk, Maciej and Kope{'c}, Mateusz},
booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
year = "2014",
}
```
## License
```
Creative Commons Attribution ShareAlike 3.0 licence (CC-BY-SA 3.0)
```
## Links
[HuggingFace](https://huggingface.co/datasets/allegro/klej-psc)
[Source](http://zil.ipipan.waw.pl/PolishSummariesCorpus)
[Paper](https://aclanthology.org/L14-1145/)
## Examples
### Loading
```python
from pprint import pprint
from datasets import load_dataset
dataset = load_dataset("allegro/klej-psc")
pprint(dataset['train'][100])
#{'extract_text': 'Nowe prawo energetyczne jest zagrożeniem dla małych '
# 'producentów energii ze źródeł odnawialnych. Sytuacja się '
# 'pogarsza wdobie urynkowienia energii. zniosło preferencje '
# 'wprowadzone dla energetyki wodnej. UE zamierza podwoić '
# 'udział takich źródeł energetyki jak woda, wiatr, słońce do '
# '2010 r.W Polsce 1-1,5 proc. zużycia energii wytwarza się ze '
# 'źródeł odnawialnych. W krajach Unii udział ten wynosi '
# 'średnio 5,6 proc.',
# 'label': 1,
# 'summary_text': 'W Polsce w niewielkim stopniu wykorzystuje się elektrownie '
# 'wodne oraz inne sposoby tworzenia energii ze źródeł '
# 'odnawialnych. Podczas gdy w innych krajach europejskich jest '
# 'to średnio 5,6 % w Polsce jest to 1-1,5 %. Powodem jest '
# 'niska opłacalność posiadania tego typu elektrowni-zakład '
# 'energetyczny płaci ok. 17 gr. za 1kWh, podczas gdy '
# 'wybudowanie takiej elektrowni kosztuje ok. 100 tyś. zł.'}
```
### Evaluation
```python
import random
from pprint import pprint
from datasets import load_dataset, load_metric
dataset = load_dataset("allegro/klej-psc")
dataset = dataset.class_encode_column("label")
references = dataset["test"]["label"]
# generate random predictions
predictions = [random.randrange(max(references) + 1) for _ in range(len(references))]
acc = load_metric("accuracy")
f1 = load_metric("f1")
acc_score = acc.compute(predictions=predictions, references=references)
f1_score = f1.compute(predictions=predictions, references=references, average="macro")
pprint(acc_score)
pprint(f1_score)
# {'accuracy': 0.18588469184890655}
# {'f1': 0.17511412402843068}
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0.10303378850221634,
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0.1301025152206421,
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0.4671815037727356,
0.2824022173881531,
-0.6513851284980774,
0.07039602845907211,
0.25552111864089966,
0.5216550230979919,
0.5095423460006714,
0.7258192896842957,
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0.038284432142972946,
0.3583299517631531,
0.26113149523735046,
0.29714855551719666,
-0.13404498994350433,
-0.13160277903079987,
0.2853490710258484,
-0.4537143111228943,
-0.15146872401237488
]
|
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