datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
reza-alipour/CelebA-HQ | reza-alipour | 2023-12-01T12:50:42Z | 430 | 0 | [
"region:us"
]
| null | 2023-11-19T19:30:37Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: mm_id
dtype: string
- name: image
dtype: image
- name: mask
dtype: image
- name: landmark
dtype: image
- name: landmark_cropped
dtype: image
- name: captions
sequence: string
- name: captions_eng
sequence: string
- name: captions_pes
sequence: string
- name: captions_fra
sequence: string
- name: captions_deu
sequence: string
- name: captions_ita
sequence: string
- name: captions_spa
sequence: string
- name: captions_all
sequence: string
splits:
- name: train
num_bytes: 3726486314.5
num_examples: 28495
- name: test
num_bytes: 222791982.0
num_examples: 1498
download_size: 3666684691
dataset_size: 3949278296.5
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
AlexZigma/msr-vtt | AlexZigma | 2023-07-13T10:35:08Z | 429 | 3 | [
"region:us"
]
| null | 2023-07-12T13:47:28Z | ---
dataset_info:
features:
- name: video_id
dtype: string
- name: caption
dtype: string
- name: sen_id
dtype: int64
- name: category
dtype: int64
- name: url
dtype: string
- name: start time
dtype: float64
- name: end time
dtype: float64
- name: split
dtype: string
- name: id
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1102963
num_examples: 6513
- name: val
num_bytes: 85199
num_examples: 497
download_size: 598248
dataset_size: 1188162
---
# Dataset Card for "msr-vtt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
colbertv2/lotte_passages | colbertv2 | 2023-08-23T01:55:55Z | 428 | 0 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2112.01488",
"region:us"
]
| [
"question-answering"
]
| 2022-07-14T22:44:41Z | ---
viewer: false
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: 'Lotte passages from ColBERTv2: Effective and Efficient Retrieval via
Lightweight Late Interaction'
size_categories:
- 1M<n<10M
source_datasets:
- original
tags: []
task_categories:
- question-answering
task_ids:
- extractive-qa
dataset_info:
features:
- name: doc_id
dtype: int32
- name: author
dtype: string
- name: text
dtype: string
splits:
- name: dev_collection
num_bytes: 263355925
num_examples: 268880
- name: test_collection
num_bytes: 105718627
num_examples: 119458
download_size: 225568795
dataset_size: 369074552
---
Passages for the LoTTe dataset used for [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://arxiv.org/abs/2112.01488) | [
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|
griffin/chain_of_density | griffin | 2023-09-08T00:43:00Z | 428 | 52 | [
"region:us"
]
| null | 2023-09-08T00:42:55Z | ---
dataset_info:
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splits:
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features:
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splits:
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download_size: 3719092
dataset_size: 6948744
configs:
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data_files:
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path: annotated/test-*
- config_name: unannotated
data_files:
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path: unannotated/train-*
---
# Dataset Card for "chain_of_density"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
princeton-nlp/SWE-bench_oracle_llama | princeton-nlp | 2023-10-17T13:59:03Z | 428 | 1 | [
"region:us"
]
| null | 2023-10-10T04:10:48Z | ---
dataset_info:
features:
- name: base_commit
dtype: string
- name: hints_text
dtype: string
- name: created_at
dtype: string
- name: test_patch
dtype: string
- name: repo
dtype: string
- name: problem_statement
dtype: string
- name: version
dtype: string
- name: instance_id
dtype: string
- name: FAIL_TO_PASS
dtype: string
- name: PASS_TO_PASS
dtype: string
- name: environment_setup_commit
dtype: string
- name: text
dtype: string
- name: input_ids
sequence: int32
- name: labels
sequence: int64
- name: patch
dtype: string
splits:
- name: test
num_bytes: 550241818
num_examples: 2294
download_size: 199730762
dataset_size: 550241818
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
### Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
### Supported Tasks and Leaderboards
SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at www.swebench.com
### Languages
The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.
## Dataset Structure
### Data Instances
An example of a SWE-bench datum is as follows:
```
instance_id: (str) - A formatted instance identifier, usually as repo_owner__repo_name-PR-number.
patch: (str) - The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue.
repo: (str) - The repository owner/name identifier from GitHub.
base_commit: (str) - The commit hash of the repository representing the HEAD of the repository before the solution PR is applied.
hints_text: (str) - Comments made on the issue prior to the creation of the solution PR’s first commit creation date.
created_at: (str) - The creation date of the pull request.
test_patch: (str) - A test-file patch that was contributed by the solution PR.
problem_statement: (str) - The issue title and body.
version: (str) - Installation version to use for running evaluation.
environment_setup_commit: (str) - commit hash to use for environment setup and installation.
FAIL_TO_PASS: (str) - A json list of strings that represent the set of tests resolved by the PR and tied to the issue resolution.
PASS_TO_PASS: (str) - A json list of strings that represent tests that should pass before and after the PR application.
text: (str) - The generated text according to the retrieval criterion and the style-2 prompt found in [github:SWE-bench](https://github.com/princeton-nlp/SWE-bench).
input_ids: (List[int]) - The llama tokens for each text.
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
SocialGrep/reddit-crypto-aug-2021 | SocialGrep | 2022-07-01T19:08:05Z | 427 | 4 | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for reddit-crypto-aug-2021
## 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://socialgrep.com/datasets](https://socialgrep.com/datasets?utm_source=huggingface&utm_medium=link&utm_campaign=dataset&utm_term=crypto)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=dataset&utm_term=crypto)
### Dataset Summary
This corpus contains the complete data for the activity on the following subreddits for the entire month of August 2021:
- /r/cryptocurrency
- /r/cryptocurrencyclassic
- /r/cryptocurrencyico
- /r/cryptomars
- /r/cryptomoon
- /r/cryptomoonshots
- /r/satoshistreetbets
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## 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
CC-BY v4.0
### Contributions
[Needs More Information] | [
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|
ArmelR/stack-exchange-instruction | ArmelR | 2023-05-26T08:37:42Z | 427 | 51 | [
"region:us"
]
| null | 2023-04-06T16:31:58Z | ---
pretty_name : stack exchange instruction
---
# Dataset Card for "stack-exchange-instruction"
[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_CHIH-HUNG__llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down | open-llm-leaderboard | 2023-10-28T03:26:37Z | 427 | 0 | [
"region:us"
]
| null | 2023-10-04T05:11:21Z | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down)\
\ 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_CHIH-HUNG__llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T03:26:24.700404](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down/blob/main/results_2023-10-28T03-26-24.700404.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\": 0.3543414429530201,\n\
\ \"em_stderr\": 0.004898375582961981,\n \"f1\": 0.39350461409396,\n\
\ \"f1_stderr\": 0.004814089237052805,\n \"acc\": 0.4362902583993399,\n\
\ \"acc_stderr\": 0.01014740888692861\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3543414429530201,\n \"em_stderr\": 0.004898375582961981,\n\
\ \"f1\": 0.39350461409396,\n \"f1_stderr\": 0.004814089237052805\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10462471569370735,\n \
\ \"acc_stderr\": 0.008430668082029278\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827943\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down
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_04T05_10_57.019261
path:
- '**/details_harness|arc:challenge|25_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_28T03_26_24.700404
path:
- '**/details_harness|drop|3_2023-10-28T03-26-24.700404.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T03-26-24.700404.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T03_26_24.700404
path:
- '**/details_harness|gsm8k|5_2023-10-28T03-26-24.700404.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T03-26-24.700404.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hellaswag|10_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T05-10-57.019261.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T05-10-57.019261.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T05-10-57.019261.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T03_26_24.700404
path:
- '**/details_harness|winogrande|5_2023-10-28T03-26-24.700404.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T03-26-24.700404.parquet'
- config_name: results
data_files:
- split: 2023_10_04T05_10_57.019261
path:
- results_2023-10-04T05-10-57.019261.parquet
- split: 2023_10_28T03_26_24.700404
path:
- results_2023-10-28T03-26-24.700404.parquet
- split: latest
path:
- results_2023-10-28T03-26-24.700404.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down
- **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 [CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down) 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_CHIH-HUNG__llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T03:26:24.700404](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down/blob/main/results_2023-10-28T03-26-24.700404.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": 0.3543414429530201,
"em_stderr": 0.004898375582961981,
"f1": 0.39350461409396,
"f1_stderr": 0.004814089237052805,
"acc": 0.4362902583993399,
"acc_stderr": 0.01014740888692861
},
"harness|drop|3": {
"em": 0.3543414429530201,
"em_stderr": 0.004898375582961981,
"f1": 0.39350461409396,
"f1_stderr": 0.004814089237052805
},
"harness|gsm8k|5": {
"acc": 0.10462471569370735,
"acc_stderr": 0.008430668082029278
},
"harness|winogrande|5": {
"acc": 0.7679558011049724,
"acc_stderr": 0.011864149691827943
}
}
```
### 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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|
igbo_ner | null | 2022-11-03T16:16:30Z | 426 | 0 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ig",
"license:unknown",
"arxiv:2004.00648",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- ig
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: Igbo NER dataset
dataset_info:
- config_name: ner_data
features:
- name: content_n
dtype: string
- name: named_entity
dtype: string
- name: sentences
sequence: string
splits:
- name: train
num_bytes: 60315228
num_examples: 30715
download_size: 3311204
dataset_size: 60315228
- config_name: free_text
features:
- name: sentences
dtype: string
splits:
- name: train
num_bytes: 1172152
num_examples: 10000
download_size: 1132151
dataset_size: 1172152
---
# Dataset Card for Igbo NER 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:** https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_ner
- **Repository:** https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_ner
- **Paper:** https://arxiv.org/abs/2004.00648
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```
{'content_n': 'content_0', 'named_entity': 'Ike Ekweremmadụ', 'sentences': ['Ike Ekweremmadụ', "Ike ịda jụụ otụ nkeji banyere oke ogbugbu na-eme n'ala Naijiria agwụla Ekweremmadụ"]}
```
### Data Fields
- content_n : ID
- named_entity : Name of the entity
- sentences : List of sentences for the entity
### 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
@misc{ezeani2020igboenglish,
title={Igbo-English Machine Translation: An Evaluation Benchmark},
author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple},
year={2020},
eprint={2004.00648},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
### Contributions
Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset. | [
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|
xor_tydi_qa | null | 2023-01-25T15:03:13Z | 426 | 1 | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"source_datasets:extended|tydiqa",
"language:ar",
"language:bn",
"language:fi",
"language:ja",
"language:ko",
"language:ru",
"language:te",
"license:mit",
"arxiv:2010.11856",
"region:us"
]
| [
"question-answering"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
- found
language:
- ar
- bn
- fi
- ja
- ko
- ru
- te
license:
- mit
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
- extended|tydiqa
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: xor-tydi-qa
pretty_name: XOR QA
dataset_info:
- config_name: xor-retrieve
features:
- name: question
dtype: string
- name: lang
dtype:
class_label:
names:
'0': ar
'1': bn
'2': fi
'3': ja
'4': ko
'5': ru
'6': te
- name: answers
dtype: string
splits:
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num_bytes: 1698662
num_examples: 15250
- name: validation
num_bytes: 259533
num_examples: 2110
- name: test
num_bytes: 219046
num_examples: 2499
download_size: 3702288
dataset_size: 2177241
- config_name: xor-full
features:
- name: question
dtype: string
- name: lang
dtype:
class_label:
names:
'0': ar
'1': bn
'2': fi
'3': ja
'4': ko
'5': ru
'6': te
- name: answers
dtype: string
splits:
- name: train
num_bytes: 7250913
num_examples: 61360
- name: validation
num_bytes: 444672
num_examples: 3473
- name: test
num_bytes: 706664
num_examples: 8176
download_size: 14018298
dataset_size: 8402249
---
# Dataset Card for XOR QA
## 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:** [XOR QA Homepage](https://nlp.cs.washington.edu/xorqa/)
- **Repository:** [XOR QA Repository](https://github.com/AkariAsai/XORQA)
- **Paper:** [XOR QA Paper](https://arxiv.org/abs/2010.11856)
- **Leaderboard:** [XOR QA Leaderboard](https://nlp.cs.washington.edu/xorqa/)
- **Point of Contact:** [Akari Asai]([email protected])
### Dataset Summary
XOR-TyDi QA brings together for the first time information-seeking questions, open-retrieval QA, and multilingual QA to create a multilingual open-retrieval QA dataset that enables cross-lingual answer retrieval. It consists of questions written by information-seeking native speakers in 7 typologically diverse languages and answer annotations that are retrieved from multilingual document collections.
### Supported Tasks and Leaderboards
There are three sub-tasks: XOR-Retrieve, XOR-EnglishSpan, and XOR-Full.
- `XOR-retrieve`: XOR-Retrieve is a cross-lingual retrieval task where a question is written in a target language (e.g., Japanese) and a system is required to retrieve English paragraphs that answer the question. The dataset can be used to train a model for cross-lingual retrieval. Success on this task is typically measured by R@5kt, R@2kt (the recall by computing the fraction of the questions for which the minimal answer is contained in the top 5,000 / 2,000 tokens selected). This task has an active leaderboard which can be found at [leaderboard url](https://nlp.cs.washington.edu/xorqa/)
- `XOR-English Span`: XOR-English Span is a cross-lingual retrieval task where a question is written in a target language (e.g., Japanese) and a system is required to output a short answer in English. The dataset can be used to train a model for cross-lingual retrieval. Success on this task is typically measured by F1, EM. This task has an active leaderboard which can be found at [leaderboard url](https://nlp.cs.washington.edu/xorqa/)
- `XOR-Full`: XOR-Full is a cross-lingual retrieval task where a question is written in the target language (e.g., Japanese) and a system is required to output a short answer in a target language. Success on this task is typically measured by F1, EM, BLEU This task has an active leaderboard which can be found at [leaderboard url](https://nlp.cs.washington.edu/xorqa/)
### Languages
The text in the dataset is available in 7 languages: Arabic `ar`, Bengali `bn`, Finnish `fi`, Japanese `ja`, Korean `ko`, Russian `ru`, Telugu `te`
## Dataset Structure
### Data Instances
A typical data point comprises a `question`, it's `answer` the `language` of the question text and the split to which it belongs.
```
{
"id": "-3979399588609321314",
"question": "Сколько детей было у Наполео́на I Бонапа́рта?",
"answers": ["сын"],
"lang": "ru",
"split": "train"
}
```
### Data Fields
- `id`: An identifier for each example in the dataset
- `question`: Open domain question
- `answers`: The corresponding answer to the question posed
- `lang`: BCP-47 language tag
- `split`: identifier to differentiate train, validation and test splits
### Data Splits
The data is split into a training, validation and test set for each of the two configurations.
| | train | validation | test |
|--------------|------:|-----------:|-----:|
| XOR Retrieve | 15250 | 2113 | 2501 |
| XOR Full | 61360 | 3179 | 8177 |
## Dataset Creation
### Curation Rationale
This task framework reflects well real-world scenarios where a QA system uses multilingual document collections and answers questions asked by users with diverse linguistic and cultural backgrounds. Despite the common assumption that we can find answers in the target language, web re- sources in non-English languages are largely lim- ited compared to English (information scarcity), or the contents are biased towards their own cul- tures (information asymmetry). To solve these issues, XOR-TYDI QA (Asai et al., 2020) provides a benchmark for developing a multilingual QA system that finds answers in multiple languages.
### Source Data
annotation pipeline consists of four steps: 1) collection of realistic questions that require cross-lingual ref- erences by annotating questions from TYDI QA without a same-language answer; 2) question translation from a target language to the pivot language of English where the missing informa- tion may exist; 3) answer span selection in the pivot language given a set of candidate documents; 4) answer verification and translation from the pivot language back to the original language.
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The Dataset is created by extending TyDiQA dataset and translating the questions into other languages. The answers are obtained by crowdsourcing the questions to Mechanical Turk workders
### Annotations
#### Annotation process
The English questions from TyDiQA are translated into other languages. The languages are chosen based on the availability of wikipedia data and the availability of tranlators.
#### Who are the annotators?
The translations are carried out using the professionla tranlation service (Gengo)[https://gengo.com] and the answers are annotated by MechanicalTurk workers
### Personal and Sensitive Information
The dataset is created from wikipedia content and the QA task requires preserving the named entities, there by all the Wikipedia Named Entities are preserved in the data. Not much information has been provided about masking sensitive information.
## 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 people associated with the creation of the dataset are Akari Asai, Jungo Kasai, Jonathan H. Clark, Kenton Lee, Eunsol Choi, Hannaneh Hajishirzi
### Licensing Information
XOR-TyDi QA is distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) license
### Citation Information
```
@article{xorqa,
title = {XOR QA: Cross-lingual Open-Retrieval Question Answering},
author = {Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi}
year = {2020}
}
```
### Contributions
Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset. | [
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|
bigbio/ddi_corpus | bigbio | 2022-12-22T15:44:31Z | 426 | 2 | [
"multilinguality:monolingual",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
]
| null | 2022-11-13T22:08:08Z |
---
language:
- en
bigbio_language:
- English
license: cc-by-nc-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_NC_4p0
pretty_name: DDI Corpus
homepage: https://github.com/isegura/DDICorpus
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION
---
# Dataset Card for DDI Corpus
## Dataset Description
- **Homepage:** https://github.com/isegura/DDICorpus
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,RE
The DDI corpus has been manually annotated with drugs and pharmacokinetics and pharmacodynamics interactions. It contains 1025 documents from two different sources: DrugBank database and MedLine.
## Citation Information
```
@article{HERREROZAZO2013914,
title = {
The DDI corpus: An annotated corpus with pharmacological substances and
drug-drug interactions
},
author = {
María Herrero-Zazo and Isabel Segura-Bedmar and Paloma Martínez and Thierry
Declerck
},
year = 2013,
journal = {Journal of Biomedical Informatics},
volume = 46,
number = 5,
pages = {914--920},
doi = {https://doi.org/10.1016/j.jbi.2013.07.011},
issn = {1532-0464},
url = {https://www.sciencedirect.com/science/article/pii/S1532046413001123},
keywords = {Biomedical corpora, Drug interaction, Information extraction}
}
```
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|
open-llm-leaderboard/details_fangloveskari__Platypus_QLoRA_LLaMA_70b | open-llm-leaderboard | 2023-09-17T21:04:43Z | 426 | 0 | [
"region:us"
]
| null | 2023-08-29T08:46:19Z | ---
pretty_name: Evaluation run of fangloveskari/Platypus_QLoRA_LLaMA_70b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [fangloveskari/Platypus_QLoRA_LLaMA_70b](https://huggingface.co/fangloveskari/Platypus_QLoRA_LLaMA_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_fangloveskari__Platypus_QLoRA_LLaMA_70b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T21:04:30.246280](https://huggingface.co/datasets/open-llm-leaderboard/details_fangloveskari__Platypus_QLoRA_LLaMA_70b/blob/main/results_2023-09-17T21-04-30.246280.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\": 0.3960780201342282,\n\
\ \"em_stderr\": 0.005008647185447735,\n \"f1\": 0.5245239093959767,\n\
\ \"f1_stderr\": 0.00450887492882971,\n \"acc\": 0.5682691139696489,\n\
\ \"acc_stderr\": 0.011651409152443089\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3960780201342282,\n \"em_stderr\": 0.005008647185447735,\n\
\ \"f1\": 0.5245239093959767,\n \"f1_stderr\": 0.00450887492882971\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3078089461713419,\n \
\ \"acc_stderr\": 0.012714401009923652\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8287292817679558,\n \"acc_stderr\": 0.010588417294962526\n\
\ }\n}\n```"
repo_url: https://huggingface.co/fangloveskari/Platypus_QLoRA_LLaMA_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_08_29T08_45_40.863548
path:
- '**/details_harness|arc:challenge|25_2023-08-29T08:45:40.863548.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T21_04_30.246280
path:
- '**/details_harness|drop|3_2023-09-17T21-04-30.246280.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T21-04-30.246280.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T21_04_30.246280
path:
- '**/details_harness|gsm8k|5_2023-09-17T21-04-30.246280.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T21-04-30.246280.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_29T08_45_40.863548
path:
- '**/details_harness|hellaswag|10_2023-08-29T08:45:40.863548.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_29T08_45_40.863548
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T08:45:40.863548.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T08:45:40.863548.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_29T08_45_40.863548
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T08:45:40.863548.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_29T08_45_40.863548
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
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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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- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
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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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- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
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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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- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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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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- split: latest
path:
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- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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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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- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T08:45:40.863548.parquet'
- 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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- 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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- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_physics_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_hendrycksTest_high_school_statistics_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
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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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path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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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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- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
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path:
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- 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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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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- split: latest
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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data_files:
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path:
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path:
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- 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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- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T08:45:40.863548.parquet'
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path:
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- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T08:45:40.863548.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_security_studies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T08:45:40.863548.parquet'
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path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T08:45:40.863548.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T08:45:40.863548.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T08:45:40.863548.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_29T08_45_40.863548
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T08:45:40.863548.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_29T08_45_40.863548
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-29T08:45:40.863548.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-29T08:45:40.863548.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T21_04_30.246280
path:
- '**/details_harness|winogrande|5_2023-09-17T21-04-30.246280.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T21-04-30.246280.parquet'
- config_name: results
data_files:
- split: 2023_08_29T08_45_40.863548
path:
- results_2023-08-29T08:45:40.863548.parquet
- split: 2023_09_17T21_04_30.246280
path:
- results_2023-09-17T21-04-30.246280.parquet
- split: latest
path:
- results_2023-09-17T21-04-30.246280.parquet
---
# Dataset Card for Evaluation run of fangloveskari/Platypus_QLoRA_LLaMA_70b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/fangloveskari/Platypus_QLoRA_LLaMA_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 [fangloveskari/Platypus_QLoRA_LLaMA_70b](https://huggingface.co/fangloveskari/Platypus_QLoRA_LLaMA_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_fangloveskari__Platypus_QLoRA_LLaMA_70b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T21:04:30.246280](https://huggingface.co/datasets/open-llm-leaderboard/details_fangloveskari__Platypus_QLoRA_LLaMA_70b/blob/main/results_2023-09-17T21-04-30.246280.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": 0.3960780201342282,
"em_stderr": 0.005008647185447735,
"f1": 0.5245239093959767,
"f1_stderr": 0.00450887492882971,
"acc": 0.5682691139696489,
"acc_stderr": 0.011651409152443089
},
"harness|drop|3": {
"em": 0.3960780201342282,
"em_stderr": 0.005008647185447735,
"f1": 0.5245239093959767,
"f1_stderr": 0.00450887492882971
},
"harness|gsm8k|5": {
"acc": 0.3078089461713419,
"acc_stderr": 0.012714401009923652
},
"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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|
electricity_load_diagrams | null | 2022-11-18T20:00:21Z | 425 | 5 | [
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"license:unknown",
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]
| [
"time-series-forecasting"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language: []
license:
- unknown
multilinguality:
- monolingual
pretty_name: Electricity Load Diagrams
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecasting
dataset_info:
- config_name: uci
features:
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dtype: timestamp[s]
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sequence: float32
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sequence: uint64
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download_size: 261335609
dataset_size: 244032000
---
# Dataset Card for Electricity Load Diagrams
## 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:** [Electricity Load Diagrams 2011-2014](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014)
- **Paper:** [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
](https://dl.acm.org/doi/10.1145/3209978.3210006)
- **Point of Contact:** [Artur Trindade](mailto:[email protected])
### Dataset Summary
This dataset contains hourly kW electricity consumption time series of 370 Portuguese clients from 2011 to 2014.
### Dataset Usage
The dataset has the following configuration parameters:
- `freq` is the time series frequency at which we resample (default: `"1H"`)
- `prediction_length` is the forecast horizon for this task which is used to make the validation and test splits (default: `24`)
- `rolling_evaluations` is the number of rolling window time series in the test split for evaluation purposes (default: `7`)
For example, you can specify your own configuration different from those used in the papers as follows:
```python
load_dataset("electricity_load_diagrams", "uci", rolling_evaluations=10)
```
> Notes:
> - Data set has no missing values.
> - Values are in kW of each 15 min rescaled to hourly. To convert values in kWh values must be divided by 4.
> - All time labels report to Portuguese hour, however all days present 96 measures (24*4).
> - Every year in March time change day (which has only 23 hours) the values between 1:00 am and 2:00 am are zero for all points.
> - Every year in October time change day (which has 25 hours) the values between 1:00 am and 2:00 am aggregate the consumption of two hours.
### Supported Tasks and Leaderboards
- `univariate-time-series-forecasting`: The time series forecasting tasks involves learning the future `target` values of time series in a dataset for the `prediction_length` time steps. The results of the forecasts can then be validated via the ground truth in the `validation` split and tested via the `test` split.
### Languages
## Dataset Structure
Data set has no missing values. The raw values are in kW of each 15 min interval and are resampled to hourly frequency.
Each time series represent one client. Some clients were created after 2011. In these cases consumption were considered zero. All time labels report to Portuguese hour, however all days contain 96 measurements (24*4). Every year in March time change day (which has only 23 hours) the values between 1:00 am and 2:00 am are zero for all points. Every year in October time change day (which has 25 hours) the values between 1:00 am and 2:00 am aggregate the consumption of two hours.
### Data Instances
A sample from the training set is provided below:
```
{
'start': datetime.datetime(2012, 1, 1, 0, 0),
'target': [14.0, 18.0, 21.0, 20.0, 22.0, 20.0, 20.0, 20.0, 13.0, 11.0], # <= this target array is a concatenated sample
'feat_static_cat': [0],
'item_id': '0'
}
```
We have two configurations `uci` and `lstnet`, which are specified as follows.
The time series are resampled to hourly frequency. We test on 7 rolling windows of prediction length of 24.
The `uci` validation therefore ends 24*7 time steps before the end of each time series. The training split ends 24 time steps before the end of the validation split.
For the `lsnet` configuration we split the training window so that it is 0.6-th of the full time series and the validation is 0.8-th of the full time series and the last 0.2-th length time windows is used as the test set of 7 rolling windows of the 24 time steps each. Finally, as in the LSTNet paper, we only consider time series that are active in the year 2012--2014, which leaves us with 320 time series.
### Data Fields
For this univariate regular time series we have:
- `start`: a `datetime` of the first entry of each time series in the dataset
- `target`: an `array[float32]` of the actual target values
- `feat_static_cat`: an `array[uint64]` which contains a categorical identifier of each time series in the dataset
- `item_id`: a string identifier of each time series in a dataset for reference
Given the `freq` and the `start` datetime, we can assign a datetime to each entry in the target array.
### Data Splits
| name |train|unsupervised|test |
|----------|----:|-----------:|----:|
|uci|370| 2590|370|
|lstnet|320| 2240|320|
## Dataset Creation
The Electricity Load Diagrams 2011–2014 Dataset was developed by Artur Trindade and shared in UCI Machine Learning Repository. This dataset covers the electricity load of 370 substations in Portugal from the start of 2011 to the end of 2014 with a sampling period of 15 min. We will resample this to hourly time series.
### Curation Rationale
Research and development of load forecasting methods. In particular short-term electricity forecasting.
### Source Data
This dataset covers the electricity load of 370 sub-stations in Portugal from the start of 2011 to the end of 2014 with a sampling period of 15 min.
#### 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
```bibtex
@inproceedings{10.1145/3209978.3210006,
author = {Lai, Guokun and Chang, Wei-Cheng and Yang, Yiming and Liu, Hanxiao},
title = {Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks},
year = {2018},
isbn = {9781450356572},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3209978.3210006},
doi = {10.1145/3209978.3210006},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {95--104},
numpages = {10},
location = {Ann Arbor, MI, USA},
series = {SIGIR '18}
}
```
### Contributions
Thanks to [@kashif](https://github.com/kashif) for adding this dataset. | [
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|
GEM/conversational_weather | GEM | 2022-10-24T15:30:13Z | 425 | 2 | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"data-to-text",
"region:us"
]
| [
"table-to-text"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: conversational_weather
tags:
- data-to-text
---
# Dataset Card for GEM/conversational_weather
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/facebookresearch/TreeNLG
- **Paper:** https://aclanthology.org/P19-1080
- **Leaderboard:** N/A
- **Point of Contact:** Kartikeya Upasani
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/conversational_weather).
### Dataset Summary
The purpose of this dataset is to assess how well a model can learn a template-like structure in a very low data setting. The task here is to produce a response to a weather-related query. The reply is further specified through the data attributes and discourse structure in the input. The output contains both the lexicalized text and discourse markers for attributes (e.g., `_ARG_TEMP_ 34`).
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/conversational_weather')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/conversational_weather).
#### paper
[ACL Anthology](https://aclanthology.org/P19-1080)
#### authors
Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba (Facebook Conversational AI)
## 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/facebookresearch/TreeNLG)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/P19-1080)
#### 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{balakrishnan-etal-2019-constrained,
title = "Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue",
author = "Balakrishnan, Anusha and
Rao, Jinfeng and
Upasani, Kartikeya and
White, Michael and
Subba, Rajen",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1080",
doi = "10.18653/v1/P19-1080",
pages = "831--844"
}
```
#### 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 -->
Kartikeya Upasani
#### 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 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 -->
cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
This dataset is intended to help develop conversational agents that exhibit human-like properties such as matching the framing of the response with the query or contrasting relevant data attributes.
#### 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 -->
Producing a text that is a response to a weather query as per the discourse structure and data 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 -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Facebook
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba (Facebook Conversational AI)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Facebook
#### 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 -->
Vipul Raheja (Grammarly)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `gem_id`: (string): GEM-formatted row id
- `id`: (string): Row id in the original data
- `user_query`: (string): Natural language weather query from humans
- `tree_str_mr`: (string): Synthetically-added user context (datetime and location) in the form of a tree-structured MR
- `response`: (string): A tree-structured annotation of the response.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{'gem_id': 'weather-train-11',
'id': '1108963',
'synthetic_user_context': '[__DG_INFORM__ [__ARG_TASK__ get_forecast ] '
'[__ARG_TEMP__ 37 ] [__ARG_TEMP_UNIT__ fahrenheit ] '
'[__ARG_CLOUD_COVERAGE__ partly cloudy ] '
'[__ARG_DATE_TIME__ [__ARG_COLLOQUIAL__ currently ] '
'] [__ARG_LOCATION__ [__ARG_CITY__ Oakland ] '
'[__ARG_COUNTRY__ United States ] [__ARG_REGION__ '
'California ] ] ] [__DG_INFORM__ [__ARG_TASK__ '
'get_forecast ] [__ARG_TEMP_SUMMARY__ mid 40s ] '
'[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This '
'afternoon ] ] [__ARG_LOCATION__ [__ARG_CITY__ '
'Oakland ] [__ARG_COUNTRY__ United States ] '
'[__ARG_REGION__ California ] ] ] [__DG_INFORM__ '
'[__ARG_TASK__ get_forecast ] '
'[__ARG_CLOUD_COVERAGE__ mostly sunny ] '
'[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This '
'afternoon ] ] [__ARG_LOCATION__ [__ARG_CITY__ '
'Oakland ] [__ARG_COUNTRY__ United States ] '
'[__ARG_REGION__ California ] ] ]',
'tree_str_mr': "[__DG_INFORM__ It's [__ARG_DATE_TIME__ [__ARG_COLLOQUIAL__ "
'currently ] ] [__ARG_CLOUD_COVERAGE__ partly cloudy ] and '
'[__ARG_TEMP__ __ARG_TEMP__ ] [__ARG_TEMP_UNIT__ '
'__ARG_TEMP_UNIT__ ] [__ARG_LOCATION__ in [__ARG_CITY__ '
'__ARG_CITY__ ] , [__ARG_REGION__ __ARG_REGION__ ] , '
'[__ARG_COUNTRY__ __ARG_COUNTRY__ ] ] . ] [__DG_INFORM__ '
'[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This afternoon ] '
"] , it'll be [__ARG_CLOUD_COVERAGE__ mostly sunny ] ] "
'[__DG_INFORM__ with temperatures in the [__ARG_TEMP_SUMMARY__ '
'mid <number> ] ]',
'user_query': 'Show weather forecast for Oakland, CA. '}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- Standard Splits: Train/Validation/Test
- Additional Split: Disc_Test (a more challenging subset of the test set that contains discourse relations)
#### 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 test set contains 3,121 examples, of which 1.1K (35%) have unique MRs that have never been seen in the training set.
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
```
{'gem_id': 'weather-train-13333', 'data_id': '1260610', 'user_query': 'Sundown', 'tree_str_mr': '[__DG_INFORM__ [__ARG_TASK__ get_weather_attribute ] [__ARG_SUNSET_TIME_DATE_TIME__ [__ARG_TIME__ 05:04 PM ] ] ]', 'response': '[__DG_INFORM__ The sun will go down at [__ARG_SUNSET_TIME_DATE_TIME__ [__ARG_TIME__ __ARG_TIME__ ] ] ]'}
```
## 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 dataset was curated to develop a weather bot that exhibits human-like properties such as matching the framing of the response with the query or contrasting relevant data attributes.
The dataset offers rich tree-based meaning representations that offer fine-grained control over the response, e.g. by specifying which two attributes are to be contrasted. The natural language input queries are also provided to model the coherence of the response based on the input. The output response is annotated with the input meaning components using special bracketing tokens, which enables developing new techniques such as constrained decoding to improve quality of output responses
#### 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 -->
Adequately expressing CONTRAST and JUSTIFY discourse relations with appropriate grouping of arguments; adequately generalizing to many combinations of arguments.
### 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 -->
`data points removed`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
The original repo contained a challenge set disc_test.tsv, which is a subset of the test set consisting of discourse relations (CONTRAST and JUSTIFY) , but also contained JOIN relations.
This discrepancy has been rectified in the GEM version. The rectified version has been added in the `challenge_sets`
#### 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 -->
Adequately expressing CONTRAST and JUSTIFY discourse relations with appropriate grouping of arguments; adequately generalizing to many combinations of arguments.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
Tree accuracy: It measures whether the tree structure in the prediction matches that of the input MR exactly (modulo repeated arguments that need only appear once).
#### 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 metrics are evaluated on the raw model predictions (which have de-lexicalized fields):
* Tree accuracy: Measures whether the tree structure in the prediction matches that of the input MR exactly.
* BLEU-4: A word overlap metric commonly used for evaluating NLG systems.
Authors also performed human evaluation studies by asking annotators to evaluate the quality of responses produced by different models. Annotators provided binary ratings on the following dimensions:
• Grammaticality: Measures fluency of the responses.
• Correctness: Measures semantic correctness of the responses.
#### 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 curated to develop a weather bot that exhibits human-like properties such as matching the framing of the response with the query or contrasting relevant data attributes. To achieve this, the dataset contains rich tree-structured meaning representations that are specified using several data arguments and discourse acts, the input natural language queries, and annotations for the responses.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Producing a text that is a response to a weather query as per the discourse structure and data attributes specified in the input meaning representation.
#### 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`, `Machine-generated`
#### 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 -->
The dataset is focused on the weather domain: Weather was the first successful case of NLG put into production back in the 80s (Reiter & Dale, 1997). This domain offers significant complexity for NLG. Weather forecast summaries in particular can be very long, and require reasoning over several disjoint pieces of information.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by crowdworker
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Please refer to Appendix D of the original paper for details.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
hybrid
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
Please refer to Appendix C of the original paper for details.
### 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 -->
Annotation was done as work for hire and contains no PII.
### 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 -->
Data is simulated and not specific to annotator.
### 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 -->
Grammatical evaluations performed with the data to date have used norms from informal Standard American English. These prescriptive notions of grammaticality potentially serve to perpetuate systemic power imbalances as they’re conveyed by language.
Since the data only contains informal Standard American English, its use to train a model may not be appropriate depending on the potential use case.
## 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 -->
Annotation was done as work for hire and contains no PII. Annotated data is simulated and not specific to annotator.
### Licenses
### Known Technical Limitations
#### 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 -->
An imperfect model used to convey actual weather data could mislead users about weather conditions?
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|
SocialGrep/one-year-of-r-india | SocialGrep | 2022-07-01T18:48:19Z | 425 | 1 | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for one-year-of-r-india
## 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://socialgrep.com/datasets](https://socialgrep.com/datasets?utm_source=huggingface&utm_medium=link&utm_campaign=oneyearofrindia)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=oneyearofrindia)
### Dataset Summary
This corpus contains the complete data for the activity of the subreddit /r/India from Sep 30, 2020 to Sep 30, 2021.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## 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
CC-BY v4.0
### Contributions
[Needs More Information] | [
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|
uitnlp/vietnamese_students_feedback | uitnlp | 2022-10-13T15:39:37Z | 425 | 9 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:topic-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:vi",
"license:unknown",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- vi
license:
- unknown
multilinguality:
- monolingual
pretty_name: "Vietnamese Students\u2019 Feedback Corpus"
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- topic-classification
---
# Dataset Card for Vietnamese Students’ Feedback 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://sites.google.com/uit.edu.vn/uit-nlp/datasets-projects#h.p_4Brw8L-cbfTe
- **Repository:**
- **Paper:** [UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis](https://www.researchgate.net/publication/329645066_UIT-VSFC_Vietnamese_Students'_Feedback_Corpus_for_Sentiment_Analysis)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Students’ feedback is a vital resource for the interdisciplinary research involving the combining of two different
research fields between sentiment analysis and education.
Vietnamese Students’ Feedback Corpus (UIT-VSFC) is the resource consists of over 16,000 sentences which are
human-annotated with two different tasks: sentiment-based and topic-based classifications.
To assess the quality of our corpus, we measure the annotator agreements and classification evaluation on the
UIT-VSFC corpus. As a result, we obtained the inter-annotator agreement of sentiments and topics with more than over
91% and 71% respectively. In addition, we built the baseline model with the Maximum Entropy classifier and achieved
approximately 88% of the sentiment F1-score and over 84% of the topic F1-score.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language of the dataset text sentence is Vietnamese (`vi`).
## Dataset Structure
### Data Instances
An instance example:
```
{
'sentence': 'slide giáo trình đầy đủ .',
'sentiment': 2,
'topic': 1
}
```
### Data Fields
- `sentence` (str): Text sentence.
- `sentiment`: Sentiment class, with values 0 (negative), 1 (neutral) and 2 (positive).
- `topic`: Topic class, with values 0 (lecturer), 1 (training_program), 2 (facility) and 3 (others).
### Data Splits
The dataset is split in train, validation and test.
| | Tain | Validation | Test |
|--------------------|------:|-----------:|-----:|
| Number of examples | 11426 | 1583 | 3166 |
## 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{8573337,
author={Nguyen, Kiet Van and Nguyen, Vu Duc and Nguyen, Phu X. V. and Truong, Tham T. H. and Nguyen, Ngan Luu-Thuy},
booktitle={2018 10th International Conference on Knowledge and Systems Engineering (KSE)},
title={UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis},
year={2018},
volume={},
number={},
pages={19-24},
doi={10.1109/KSE.2018.8573337}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
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|
TheFusion21/PokemonCards | TheFusion21 | 2022-11-21T18:28:25Z | 425 | 6 | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
]
| [
"text-to-image",
"image-to-text"
]
| 2022-11-20T14:14:51Z | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
pretty_name: Pokemoncards
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- text-to-image
- image-to-text
task_ids:
- image-captioning
---
# Dataset Card for PokemonCards
### Languages
All of the data is in English.
## Dataset Structure
### Data Instances
```json
{
"id": "pl1-1",
"image_url": "https://images.pokemontcg.io/pl1/1_hires.png",
"caption": "A Stage 2 Pokemon Card of type Lightning with the title ""Ampharos"" and 130 HP of rarity ""Rare Holo"" evolved from Flaaffy from the set Platinum and the flavor text: ""None"". It has the attack ""Gigavolt"" with the cost Lightning, Colorless, the energy cost 2 and the damage of 30+ with the description: ""Flip a coin. If heads, this attack does 30 damage plus 30 more damage. If tails, the Defending Pokemon is now Paralyzed."". It has the attack ""Reflect Energy"" with the cost Lightning, Colorless, Colorless, the energy cost 3 and the damage of 70 with the description: ""Move an Energy card attached to Ampharos to 1 of your Benched Pokemon."". It has the ability ""Damage Bind"" with the description: ""Each Pokemon that has any damage counters on it (both yours and your opponent's) can't use any Poke-Powers."". It has weakness against Fighting +30. It has resistance against Metal -20.",
"name": "Ampharos",
"hp": "130",
"set_name": "Platinum"
}
```
### Data Fields
- `id`: Unique ID of the pokemon card.
- `image_url`: Static URL for downloading the image associated with the post.
- `caption`: Caption generated for this card.
- `name`: Name of the pokemon on that card.
- `hp`: Health of the pokemon.
- `set_name`: The name of the set the card is in.
### Data Splits
All the data is contained in training set. The training set has nearly 13k instances.
## 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 | [
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|
squad_it | null | 2023-04-05T13:40:37Z | 424 | 2 | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|squad",
"language:it",
"license:unknown",
"region:us"
]
| [
"question-answering"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- it
language_bcp47:
- it-IT
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
paperswithcode_id: squad-it
pretty_name: SQuAD-it
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 50864824
num_examples: 54159
- name: test
num_bytes: 7858336
num_examples: 7609
download_size: 8776531
dataset_size: 58723160
---
# Dataset Card for "squad_it"
## 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/crux82/squad-it](https://github.com/crux82/squad-it)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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 downloaded dataset files:** 8.78 MB
- **Size of the generated dataset:** 58.79 MB
- **Total amount of disk used:** 67.57 MB
### Dataset Summary
SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is
split into training and test sets to support the replicability of the benchmarking of QA systems:
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 8.78 MB
- **Size of the generated dataset:** 58.79 MB
- **Total amount of disk used:** 67.57 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": "{\"answer_start\": [243, 243, 243, 243, 243], \"text\": [\"evitare di essere presi di mira dal boicottaggio\", \"evitare di essere pres...",
"context": "\"La crisi ha avuto un forte impatto sulle relazioni internazionali e ha creato una frattura all' interno della NATO. Alcune nazi...",
"id": "5725b5a689a1e219009abd28",
"question": "Perchè le nazioni europee e il Giappone si sono separati dagli Stati Uniti durante la crisi?"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | train | test |
| ------- | ----: | ---: |
| default | 54159 | 7609 |
## 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{10.1007/978-3-030-03840-3_29,
author="Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto",
editor="Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
title="Neural Learning for Question Answering in Italian",
booktitle="AI*IA 2018 -- Advances in Artificial Intelligence",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="389--402",
isbn="978-3-030-03840-3"
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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|
mb23/music_caps_4sec_wave_type | mb23 | 2023-10-25T13:54:12Z | 424 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-10-25T11:36:55Z | ---
license: mit
dataset_info:
features:
- name: ytid
dtype: string
- name: number
dtype: int64
- name: audio
dtype:
audio:
sampling_rate: 44100
- name: caption
dtype: string
splits:
- name: train
num_bytes: 8829076858.0
num_examples: 25000
download_size: 0
dataset_size: 8829076858.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
oliverwang15/news_with_gpt_instructions | oliverwang15 | 2023-07-10T19:39:33Z | 423 | 6 | [
"region:us"
]
| null | 2023-07-10T19:25:35Z | ---
dataset_info:
features:
- name: news
dtype: string
- name: prompt
dtype: string
- name: out
dtype: string
- name: prompt_tokens
dtype: int64
- name: completion_tokens
dtype: int64
- name: total_tokens
dtype: int64
- name: label
dtype: string
splits:
- name: train
num_bytes: 23897976.286293313
num_examples: 16184
- name: test
num_bytes: 5975970.713706688
num_examples: 4047
download_size: 13558209
dataset_size: 29873947.0
---
# Dataset Card for "news_with_gpt_instructions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Villekom/oa_dolly_15k_fi | Villekom | 2023-11-28T10:52:22Z | 423 | 0 | [
"region:us"
]
| null | 2023-08-23T14:15:04Z | ---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
- name: METADATA
struct:
- name: CATEGORY
dtype: string
- name: CONTEXT
dtype: string
splits:
- name: train
num_bytes: 13654728
num_examples: 15015
download_size: 8698896
dataset_size: 13654728
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "oa_dolly_15k_fi"
Taken from https://github.com/TurkuNLP/dolly-fi
[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_mistralai__Mistral-7B-v0.1 | open-llm-leaderboard | 2023-12-02T13:02:30Z | 423 | 0 | [
"region:us"
]
| null | 2023-09-27T15:31:20Z | ---
pretty_name: Evaluation run of mistralai/Mistral-7B-v0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)\
\ 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 6 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 aggregated 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_mistralai__Mistral-7B-v0.1\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:02:14.153054](https://huggingface.co/datasets/open-llm-leaderboard/details_mistralai__Mistral-7B-v0.1/blob/main/results_2023-12-02T13-02-14.153054.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\": 0.3707354056103108,\n\
\ \"acc_stderr\": 0.013304267705458433\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.3707354056103108,\n \"acc_stderr\": 0.013304267705458433\n\
\ }\n}\n```"
repo_url: https://huggingface.co/mistralai/Mistral-7B-v0.1
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_09_27T15_30_59.039834
path:
- '**/details_harness|arc:challenge|25_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T23_48_21.884715
path:
- '**/details_harness|drop|3_2023-10-25T23-48-21.884715.parquet'
- split: 2023_10_26T01_29_53.089924
path:
- '**/details_harness|drop|3_2023-10-26T01-29-53.089924.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-26T01-29-53.089924.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T23_48_21.884715
path:
- '**/details_harness|gsm8k|5_2023-10-25T23-48-21.884715.parquet'
- split: 2023_10_26T01_29_53.089924
path:
- '**/details_harness|gsm8k|5_2023-10-26T01-29-53.089924.parquet'
- split: 2023_12_01T11_13_53.246042
path:
- '**/details_harness|gsm8k|5_2023-12-01T11-13-53.246042.parquet'
- split: 2023_12_02T13_01_55.687268
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-01-55.687268.parquet'
- split: 2023_12_02T13_02_14.153054
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-02-14.153054.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-02-14.153054.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hellaswag|10_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T23_48_21.884715
path:
- '**/details_harness|winogrande|5_2023-10-25T23-48-21.884715.parquet'
- split: 2023_10_26T01_29_53.089924
path:
- '**/details_harness|winogrande|5_2023-10-26T01-29-53.089924.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-26T01-29-53.089924.parquet'
- config_name: results
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- results_2023-09-27T15-30-59.039834.parquet
- split: 2023_10_25T23_48_21.884715
path:
- results_2023-10-25T23-48-21.884715.parquet
- split: 2023_10_26T01_29_53.089924
path:
- results_2023-10-26T01-29-53.089924.parquet
- split: 2023_12_01T11_13_53.246042
path:
- results_2023-12-01T11-13-53.246042.parquet
- split: 2023_12_02T13_01_55.687268
path:
- results_2023-12-02T13-01-55.687268.parquet
- split: 2023_12_02T13_02_14.153054
path:
- results_2023-12-02T13-02-14.153054.parquet
- split: latest
path:
- results_2023-12-02T13-02-14.153054.parquet
---
# Dataset Card for Evaluation run of mistralai/Mistral-7B-v0.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/mistralai/Mistral-7B-v0.1
- **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 [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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 6 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 aggregated 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_mistralai__Mistral-7B-v0.1",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:02:14.153054](https://huggingface.co/datasets/open-llm-leaderboard/details_mistralai__Mistral-7B-v0.1/blob/main/results_2023-12-02T13-02-14.153054.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": 0.3707354056103108,
"acc_stderr": 0.013304267705458433
},
"harness|gsm8k|5": {
"acc": 0.3707354056103108,
"acc_stderr": 0.013304267705458433
}
}
```
### 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_bsp-albz__llama2-13b-platypus-ckpt-1000 | open-llm-leaderboard | 2023-10-28T07:28:03Z | 422 | 0 | [
"region:us"
]
| null | 2023-09-13T14:31:17Z | ---
pretty_name: Evaluation run of bsp-albz/llama2-13b-platypus-ckpt-1000
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bsp-albz/llama2-13b-platypus-ckpt-1000](https://huggingface.co/bsp-albz/llama2-13b-platypus-ckpt-1000)\
\ 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_bsp-albz__llama2-13b-platypus-ckpt-1000\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T07:27:50.665316](https://huggingface.co/datasets/open-llm-leaderboard/details_bsp-albz__llama2-13b-platypus-ckpt-1000/blob/main/results_2023-10-28T07-27-50.665316.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\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 9.123322147651008e-05,\n \"f1_stderr\"\
: 2.468836036222127e-05,\n \"acc\": 0.24506708760852408,\n \"acc_stderr\"\
: 0.007024874916683796\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n\
\ \"em_stderr\": 0.0,\n \"f1\": 9.123322147651008e-05,\n \"\
f1_stderr\": 2.468836036222127e-05\n },\n \"harness|gsm8k|5\": {\n \
\ \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.49013417521704816,\n \"acc_stderr\": 0.014049749833367592\n\
\ }\n}\n```"
repo_url: https://huggingface.co/bsp-albz/llama2-13b-platypus-ckpt-1000
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_13T14_31_01.492634
path:
- '**/details_harness|arc:challenge|25_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_28T07_27_50.665316
path:
- '**/details_harness|drop|3_2023-10-28T07-27-50.665316.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T07-27-50.665316.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T07_27_50.665316
path:
- '**/details_harness|gsm8k|5_2023-10-28T07-27-50.665316.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T07-27-50.665316.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hellaswag|10_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T14-31-01.492634.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-13T14-31-01.492634.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-13T14-31-01.492634.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T07_27_50.665316
path:
- '**/details_harness|winogrande|5_2023-10-28T07-27-50.665316.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T07-27-50.665316.parquet'
- config_name: results
data_files:
- split: 2023_09_13T14_31_01.492634
path:
- results_2023-09-13T14-31-01.492634.parquet
- split: 2023_10_28T07_27_50.665316
path:
- results_2023-10-28T07-27-50.665316.parquet
- split: latest
path:
- results_2023-10-28T07-27-50.665316.parquet
---
# Dataset Card for Evaluation run of bsp-albz/llama2-13b-platypus-ckpt-1000
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bsp-albz/llama2-13b-platypus-ckpt-1000
- **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 [bsp-albz/llama2-13b-platypus-ckpt-1000](https://huggingface.co/bsp-albz/llama2-13b-platypus-ckpt-1000) 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_bsp-albz__llama2-13b-platypus-ckpt-1000",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T07:27:50.665316](https://huggingface.co/datasets/open-llm-leaderboard/details_bsp-albz__llama2-13b-platypus-ckpt-1000/blob/main/results_2023-10-28T07-27-50.665316.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": 0.0,
"em_stderr": 0.0,
"f1": 9.123322147651008e-05,
"f1_stderr": 2.468836036222127e-05,
"acc": 0.24506708760852408,
"acc_stderr": 0.007024874916683796
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 9.123322147651008e-05,
"f1_stderr": 2.468836036222127e-05
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.49013417521704816,
"acc_stderr": 0.014049749833367592
}
}
```
### 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_CHIH-HUNG__llama-2-13b-Open-Platypus_2.5w | open-llm-leaderboard | 2023-10-28T05:00:38Z | 422 | 0 | [
"region:us"
]
| null | 2023-10-01T15:07:34Z | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-Open-Platypus_2.5w
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-Open-Platypus_2.5w](https://huggingface.co/CHIH-HUNG/llama-2-13b-Open-Platypus_2.5w)\
\ 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_CHIH-HUNG__llama-2-13b-Open-Platypus_2.5w\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T05:00:14.069037](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-Open-Platypus_2.5w/blob/main/results_2023-10-28T05-00-14.069037.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\": 0.003984899328859061,\n\
\ \"em_stderr\": 0.000645180584810236,\n \"f1\": 0.06641568791946297,\n\
\ \"f1_stderr\": 0.001486566771660937,\n \"acc\": 0.42681338197780844,\n\
\ \"acc_stderr\": 0.009786684273855366\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.003984899328859061,\n \"em_stderr\": 0.000645180584810236,\n\
\ \"f1\": 0.06641568791946297,\n \"f1_stderr\": 0.001486566771660937\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08567096285064443,\n \
\ \"acc_stderr\": 0.007709218855882788\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827943\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-Open-Platypus_2.5w
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_01T15_07_10.980202
path:
- '**/details_harness|arc:challenge|25_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_28T05_00_14.069037
path:
- '**/details_harness|drop|3_2023-10-28T05-00-14.069037.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T05-00-14.069037.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T05_00_14.069037
path:
- '**/details_harness|gsm8k|5_2023-10-28T05-00-14.069037.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T05-00-14.069037.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hellaswag|10_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T15-07-10.980202.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T15-07-10.980202.parquet'
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- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T15-07-10.980202.parquet'
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- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T15-07-10.980202.parquet'
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- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T15-07-10.980202.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T15-07-10.980202.parquet'
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- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T15-07-10.980202.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T15-07-10.980202.parquet'
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- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T15-07-10.980202.parquet'
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- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T15-07-10.980202.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T15-07-10.980202.parquet'
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- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T15-07-10.980202.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-01T15-07-10.980202.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-01T15-07-10.980202.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T05_00_14.069037
path:
- '**/details_harness|winogrande|5_2023-10-28T05-00-14.069037.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T05-00-14.069037.parquet'
- config_name: results
data_files:
- split: 2023_10_01T15_07_10.980202
path:
- results_2023-10-01T15-07-10.980202.parquet
- split: 2023_10_28T05_00_14.069037
path:
- results_2023-10-28T05-00-14.069037.parquet
- split: latest
path:
- results_2023-10-28T05-00-14.069037.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-Open-Platypus_2.5w
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-Open-Platypus_2.5w
- **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 [CHIH-HUNG/llama-2-13b-Open-Platypus_2.5w](https://huggingface.co/CHIH-HUNG/llama-2-13b-Open-Platypus_2.5w) 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_CHIH-HUNG__llama-2-13b-Open-Platypus_2.5w",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T05:00:14.069037](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-Open-Platypus_2.5w/blob/main/results_2023-10-28T05-00-14.069037.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": 0.003984899328859061,
"em_stderr": 0.000645180584810236,
"f1": 0.06641568791946297,
"f1_stderr": 0.001486566771660937,
"acc": 0.42681338197780844,
"acc_stderr": 0.009786684273855366
},
"harness|drop|3": {
"em": 0.003984899328859061,
"em_stderr": 0.000645180584810236,
"f1": 0.06641568791946297,
"f1_stderr": 0.001486566771660937
},
"harness|gsm8k|5": {
"acc": 0.08567096285064443,
"acc_stderr": 0.007709218855882788
},
"harness|winogrande|5": {
"acc": 0.7679558011049724,
"acc_stderr": 0.011864149691827943
}
}
```
### 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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|
LabHC/bias_in_bios | LabHC | 2023-09-10T15:41:38Z | 421 | 0 | [
"task_categories:text-classification",
"language:en",
"license:mit",
"region:us"
]
| [
"text-classification"
]
| 2023-09-05T11:22:24Z | ---
license: mit
task_categories:
- text-classification
language:
- en
dataset_info:
features:
- name: hard_text
dtype: string
- name: profession
dtype: int64
- name: gender
dtype: int64
splits:
- name: train
num_bytes: 107487885
num_examples: 257478
- name: test
num_bytes: 41312256
num_examples: 99069
- name: dev
num_bytes: 16504417
num_examples: 39642
download_size: 99808338
dataset_size: 165304558
---
# Bias in Bios
Bias in Bios was created by (De-Artega et al., 2019) and published under the MIT license (https://github.com/microsoft/biosbias). The dataset is used to investigate bias in NLP models. It consists of textual biographies used to predict professional occupations, the sensitive attribute is the gender (binary).
The version shared here is the version proposed by (Ravgofel et al., 2020) which slightly smaller due to the unavailability of 5,557 biographies.
The dataset is divided between train (257,000 samples), test (99,000 samples) and dev (40,000 samples) sets.
To load each all splits ('train', 'dev', 'test'), use the following code :
```python
train_dataset = load_dataset("LabHC/bias_in_bios", split='train')
test_dataset = load_dataset("LabHC/bias_in_bios", split='test')
dev_dataset = load_dataset("LabHC/bias_in_bios", split='dev')
```
Below are presented the classifiaction and sensitive attribtues labels and their proportion. Distributions are similar through the three sets.
#### Classification labels
| Profession | Numerical label | Proportion (%)| | Profession | Numerical label | Proportion (%)|
|---|---|---|---|---|---|---|
accountant | 0 | 1.42 | | nurse | 13 | 4.78
architect | 1 | 2.55 | | painter | 14 | 1.95
attorney | 2 | 8.22 | | paralegal | 15 | 0.45
chiropractor | 3 | 0.67 | | pastor | 16 | 0.64
comedian | 4 | 0.71 | | personal_trainer | 17 | 0.36
composer | 5 | 1.41 | | photographer | 18 | 6.13
dentist | 6 | 3.68 | | physician | 19 | 10.35
dietitian | 7 | 1.0 | | poet | 20 | 1.77
dj | 8 | 0.38 | | professor | 21 | 29.8
filmmaker | 9 | 1.77 | | psychologist | 22 | 4.64
interior_designer | 10 | 0.37 | | rapper | 23 | 0.35
journalist | 11 | 5.03 | | software_engineer | 24 | 1.74
model | 12 | 1.89 | | surgeon | 25 | 3.43
nurse | 13 | 4.78 | | teacher | 26 | 4.09
painter | 14 | 1.95 | | yoga_teacher | 27 | 0.42
#### Sensitive attributes
| Gender | Numerical label | Proportion (%)|
|---|---|---|
Male | 0 | 53.9 |
Female | 1 | 46.1
---
(De-Artega et al., 2019) Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019. Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19). Association for Computing Machinery, New York, NY, USA, 120–128. https://doi.org/10.1145/3287560.3287572
(Ravgofel et al., 2020) Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020. Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7237–7256, Online. Association for Computational Linguistics. | [
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|
bongsoo/news_talk_en_ko | bongsoo | 2022-10-05T00:09:50Z | 420 | 4 | [
"language:ko",
"license:apache-2.0",
"region:us"
]
| null | 2022-09-20T05:10:56Z | ---
language:
- ko
license: apache-2.0
---
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|
jxu124/refcoco | jxu124 | 2023-05-20T18:58:37Z | 420 | 1 | [
"region:us"
]
| null | 2023-04-22T10:52:18Z | ---
dataset_info:
features:
- name: sent_ids
sequence: int64
- name: file_name
dtype: string
- name: ann_id
dtype: int64
- name: ref_id
dtype: int64
- name: image_id
dtype: int64
- name: split
dtype: string
- name: sentences
list:
- name: raw
dtype: string
- name: sent
dtype: string
- name: sent_id
dtype: int64
- name: tokens
sequence: string
- name: category_id
dtype: int64
- name: raw_anns
dtype: string
- name: raw_image_info
dtype: string
- name: raw_sentences
dtype: string
- name: image_path
dtype: string
- name: bbox
sequence: float64
- name: captions
sequence: string
- name: global_image_id
dtype: string
- name: anns_id
dtype: string
splits:
- name: train
num_bytes: 81385755
num_examples: 42404
- name: testB
num_bytes: 3284397
num_examples: 1810
- name: test
num_bytes: 3943834
num_examples: 1975
- name: validation
num_bytes: 7355626
num_examples: 3811
download_size: 38895129
dataset_size: 95969612
---
# Dataset Card for "refcoco"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
bigcode/guanaco-commits | bigcode | 2023-06-28T08:54:47Z | 419 | 4 | [
"region:us"
]
| null | 2023-06-28T08:54:28Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 17347601.0
num_examples: 12958
- name: test
num_bytes: 827046.0
num_examples: 629
download_size: 10948498
dataset_size: 18174647.0
---
# Dataset Card for "guanaco-commits"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
christti/squad-augmented-v2 | christti | 2023-11-29T08:27:18Z | 419 | 0 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| [
"question-answering"
]
| 2023-11-20T06:15:01Z | ---
pretty_name: SQuAD Augmented v2
license: cc-by-4.0
task_categories:
- question-answering
source_datasets:
- extended|wikipedia
task_ids:
- extractive-qa
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
paperswithcode_id: squad
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
viewer: true
train-eval-index:
- config: plain_text
task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: validation
col_mapping:
question: question
context: context
answers:
text: text
answer_start: answer_start
metrics:
- type: squad
name: SQuAD
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
config_name: plain_text
splits:
- name: train
num_bytes: 156093315
num_examples: 169211
- name: validation
num_bytes: 10472653
num_examples: 10570
download_size: 35142551
dataset_size: 89789763
--- | [
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|
datacommons_factcheck | null | 2023-06-01T14:59:47Z | 417 | 3 | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: null
pretty_name: DataCommons Fact Checked claims
dataset_info:
- config_name: fctchk_politifact_wapo
features:
- name: reviewer_name
dtype: string
- name: claim_text
dtype: string
- name: review_date
dtype: string
- name: review_url
dtype: string
- name: review_rating
dtype: string
- name: claim_author_name
dtype: string
- name: claim_date
dtype: string
splits:
- name: train
num_bytes: 1772321
num_examples: 5632
download_size: 671896
dataset_size: 1772321
- config_name: weekly_standard
features:
- name: reviewer_name
dtype: string
- name: claim_text
dtype: string
- name: review_date
dtype: string
- name: review_url
dtype: string
- name: review_rating
dtype: string
- name: claim_author_name
dtype: string
- name: claim_date
dtype: string
splits:
- name: train
num_bytes: 35061
num_examples: 132
download_size: 671896
dataset_size: 35061
config_names:
- fctchk_politifact_wapo
- weekly_standard
---
# Dataset Card for DataCommons Fact Checked claims
## 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:** [Data Commons fact checking FAQ](https://datacommons.org/factcheck/faq)
### Dataset Summary
A dataset of fact checked claims by news media maintained by [datacommons.org](https://datacommons.org/) containing the claim, author, and judgments, as well as the URL of the full explanation by the original fact-checker.
The fact checking is done by [FactCheck.org](https://www.factcheck.org/), [PolitiFact](https://www.politifact.com/), and [The Washington Post](https://www.washingtonpost.com/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The data is in English (`en`).
## Dataset Structure
### Data Instances
An example of fact checking instance looks as follows:
```
{'claim_author_name': 'Facebook posts',
'claim_date': '2019-01-01',
'claim_text': 'Quotes Michelle Obama as saying, "White folks are what’s wrong with America."',
'review_date': '2019-01-03',
'review_rating': 'Pants on Fire',
'review_url': 'https://www.politifact.com/facebook-fact-checks/statements/2019/jan/03/facebook-posts/did-michelle-obama-once-say-white-folks-are-whats-/',
'reviewer_name': 'PolitiFact'}
```
### Data Fields
A data instance has the following fields:
- `review_date`: the day the fact checking report was posted. Missing values are replaced with empty strings
- `review_url`: URL for the full fact checking report
- `reviewer_name`: the name of the fact checking service.
- `claim_text`: the full text of the claim being reviewed.
- `claim_author_name`: the author of the claim being reviewed. Missing values are replaced with empty strings
- `claim_date` the date of the claim. Missing values are replaced with empty strings
- `review_rating`: the judgments of the fact checker (under `alternateName`, names vary by fact checker)
### Data Splits
No splits are provided. There are a total of 5632 claims fact-checked.
## 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?
The fact checking is done by [FactCheck.org](https://www.factcheck.org/), [PolitiFact](https://www.politifact.com/), [The Washington Post](https://www.washingtonpost.com/), and [The Weekly Standard](https://www.weeklystandard.com/).
- [FactCheck.org](https://www.factcheck.org/) self describes as "a nonpartisan, nonprofit 'consumer advocate' for voters that aims to reduce the level of deception and confusion in U.S. politics." It was founded by journalists Kathleen Hall Jamieson and Brooks Jackson and is currently directed by Eugene Kiely.
- [PolitiFact](https://www.politifact.com/) describe their ethics as "seeking to present the true facts, unaffected by agenda or biases, [with] journalists setting their own opinions aside." It was started in August 2007 by Times Washington Bureau Chief Bill Adair. The organization was acquired in February 2018 by the Poynter Institute, a non-profit journalism education and news media research center that also owns the Tampa Bay Times.
- [The Washington Post](https://www.washingtonpost.com/) is a newspaper considered to be near the center of the American political spectrum. In 2013 Amazon.com founder Jeff Bezos bought the newspaper and affiliated publications.
The original data source also contains 132 items reviewed by [The Weekly Standard](https://www.weeklystandard.com/), which was a neo-conservative American newspaper. IT is the most politically loaded source of the group, which was originally a vocal creitic of the activity of fact-checking, and has historically taken stances [close to the American right](https://en.wikipedia.org/wiki/The_Weekly_Standard#Support_of_the_invasion_of_Iraq). It also had to admit responsibility for baseless accusations against a well known author in a public [libel case](https://en.wikipedia.org/wiki/The_Weekly_Standard#Libel_case). The fact checked items from this source can be found in the `weekly_standard` configuration but should be used only with full understanding of this context.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
See section above describing the [fact checking organizations](#who-are-the-annotators?).
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
This fact checking dataset is maintained by [datacommons.org](https://datacommons.org/), a Google initiative.
### Licensing Information
All fact checked items are released under a `CC-BY-NC-4.0` License.
### Citation Information
Data Commons 2020, Fact Checks, electronic dataset, Data Commons, viewed 16 Dec 2020, <https://datacommons.org>.
### Contributions
Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset. | [
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sil-ai/bloom-vist | sil-ai | 2022-12-06T19:37:53Z | 417 | 3 | [
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| null | 2022-05-05T16:25:54Z | ---
annotations_creators:
- expert-generated
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- 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
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multilinguality:
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size_categories:
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source_datasets:
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paperswithcode_id: null
pretty_name: BloomVIST
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 visual story telling (or VIST) 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." For those with higher numbers of available stories we include the following numbers of stories:
| ISO639-3 Code | Stories | Image-Caption Pairs |
|:-----------|----------:|----------------------:|
| ahk | 55 | 493 |
| awa | 163 | 1200 |
| ben | 220 | 1938 |
| bho | 172 | 1163 |
| bis | 21 | 183 |
| brb | 22 | 330 |
| bzi | 66 | 497 |
| cak | 50 | 694 |
| ceb | 394 | 2806 |
| cgc | 182 | 1473 |
| deu | 22 | 250 |
| dty | 172 | 1310 |
| eng | 2187 | 24338 |
| fas | 128 | 620 |
| fil | 34 | 366 |
| fra | 315 | 4350 |
| hat | 224 | 1881 |
| hau | 229 | 1594 |
| ind | 232 | 1866 |
| jra | 56 | 575 |
| kak | 195 | 1416 |
| kek | 21 | 419 |
| khb | 31 | 167 |
| khm | 26 | 246 |
| kir | 278 | 2866 |
| kjb | 63 | 584 |
| kor | 129 | 2732 |
| krr | 29 | 362 |
| lsi | 22 | 173 |
| mai | 177 | 1186 |
| mam | 118 | 1058 |
| mhx | 51 | 544 |
| myk | 22 | 214 |
| nep | 194 | 1464 |
| new | 177 | 1225 |
| pbt | 203 | 979 |
| por | 148 | 2939 |
| quc | 99 | 817 |
| rus | 271 | 2977 |
| snk | 21 | 210 |
| spa | 444 | 5201 |
| swh | 34 | 387 |
| tdg | 31 | 231 |
| tha | 275 | 2929 |
| thl | 185 | 1464 |
| tpi | 137 | 1528 |
| tpu | 28 | 513 |
| zho | 42 | 339 |
## 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-vist", 'hin')
# An individual samples consists of stories in the specified language code.
# To see a story:
print(dataset['train'][0]['story'])
```
This would produce an output:
```
{'image_id': ['4e9bdde5-996d-4a98-ac1c-d80fb6349314',
'614e4d51-bbdb-4538-98d3-f603c12dccd0',
'970d60bf-2acb-44ac-8ffb-5aa3f7989630',
'd4ad1199-863e-4929-a377-93276fe5caa8',
'0d9ad694-995a-433d-af4e-6f40ddfa208a',
'811176eb-c9f3-4226-8af5-e6c4e524c494',
'83180da7-4ba8-4104-a0d9-49aa2ef48f7a'],
'image_url': ['https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_03_Image_00011.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_04_Image_0001.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_05_Image_0001.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_06_Image_0001.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_07_Image_0001.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_07_Image_00011.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_09_Image_0001.png'],
'story_index': [0, 1, 2, 3, 4, 5, 6],
'story_id': ['cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6'],
'text': ['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। \xa0मैं 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।\nसाबू खुशी से चिल्लाया, “जोजो दाएँ मुड़ो,\n 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।\xa0\n जोजो ने गुब्बारे लटकाने में आन्टी की मदद करी क्योंकि वह इतना... लम्बा था।\xa0\n कितना आसान था!',
'जोजो ने सब बच्चों को सवारी कराई।\n उनके साथ बॉल भी खेली। बड़े मज़े की पार्टी थी।सब ने गाया, “हैप्पी बर्थ डे टु यू ।”\n 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। \xa0अब घर जाने का समय हो गया।\n\nसब ने कहा, “बाय बाय जोजो, बाय बाय साबू।” साबू और जोजो घर लौटे।',
'']}
```
### Data Fields
The metadata fields below are available. 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).
- **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".
- **album_id**: an ID value corresponding to the set of images corresponding to the given story
- **story**: the sequenced story data including lists of image IDs, image URLs, and corresponding text
### Data Splits
Currently all languages include a train split only. In the future, we will be creating manual splits of the data.
## Changelog
- **6 December 2022** - dataset is made public | [
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|
colbertv2/lotte | colbertv2 | 2022-08-04T17:55:59Z | 417 | 1 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2112.01488",
"region:us"
]
| [
"question-answering"
]
| 2022-07-14T22:11:39Z | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: 'Lotte queries from ColBERTv2: Effective and Efficient Retrieval via
Lightweight Late Interaction'
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- question-answering
task_ids:
- extractive-qa
---
Queries for Lotte dataset from [ColBERTv2: Effective and Efficient Retrieval via
Lightweight Late Interaction](https://arxiv.org/abs/2112.01488) | [
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|
wangrui6/Zhihu-KOL | wangrui6 | 2023-04-23T13:26:03Z | 417 | 101 | [
"task_categories:question-answering",
"language:zh",
"region:us"
]
| [
"question-answering"
]
| 2023-02-25T00:21:29Z | ---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
- name: METADATA
dtype: string
splits:
- name: train
num_bytes: 2295601241
num_examples: 1006218
download_size: 1501204472
dataset_size: 2295601241
task_categories:
- question-answering
language:
- zh
---
# Dataset Card for "Zhihu-KOL"
Zhihu data for training Open Assitant
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
cardy/kohatespeech | cardy | 2023-05-01T02:24:59Z | 417 | 0 | [
"license:cc-by-nc-sa-4.0",
"region:us"
]
| null | 2023-04-30T11:55:56Z | ---
license: cc-by-nc-sa-4.0
---
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|
cornell_movie_dialog | null | 2023-04-05T10:02:37Z | 416 | 12 | [
"language:en",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
paperswithcode_id: cornell-movie-dialogs-corpus
pretty_name: Cornell Movie-Dialogs Corpus
dataset_info:
features:
- name: movieID
dtype: string
- name: movieTitle
dtype: string
- name: movieYear
dtype: string
- name: movieIMDBRating
dtype: string
- name: movieNoIMDBVotes
dtype: string
- name: movieGenres
sequence: string
- name: characterID1
dtype: string
- name: characterID2
dtype: string
- name: characterName1
dtype: string
- name: characterName2
dtype: string
- name: utterance
sequence:
- name: text
dtype: string
- name: LineID
dtype: string
splits:
- name: train
num_bytes: 19548840
num_examples: 83097
download_size: 9916637
dataset_size: 19548840
---
# Dataset Card for "cornell_movie_dialog"
## 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://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html](http://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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 downloaded dataset files:** 9.92 MB
- **Size of the generated dataset:** 19.55 MB
- **Total amount of disk used:** 29.46 MB
### Dataset Summary
This corpus contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts:
- 220,579 conversational exchanges between 10,292 pairs of movie characters
- involves 9,035 characters from 617 movies
- in total 304,713 utterances
- movie metadata included:
- genres
- release year
- IMDB rating
- number of IMDB votes
- IMDB rating
- character metadata included:
- gender (for 3,774 characters)
- position on movie credits (3,321 characters)
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 9.92 MB
- **Size of the generated dataset:** 19.55 MB
- **Total amount of disk used:** 29.46 MB
An example of 'train' looks as follows.
```
{
"characterID1": "u0 ",
"characterID2": " u2 ",
"characterName1": " m0 ",
"characterName2": " m0 ",
"movieGenres": ["comedy", "romance"],
"movieID": " m0 ",
"movieIMDBRating": " 6.90 ",
"movieNoIMDBVotes": " 62847 ",
"movieTitle": " f ",
"movieYear": " 1999 ",
"utterance": {
"LineID": ["L1"],
"text": ["L1 "]
}
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `movieID`: a `string` feature.
- `movieTitle`: a `string` feature.
- `movieYear`: a `string` feature.
- `movieIMDBRating`: a `string` feature.
- `movieNoIMDBVotes`: a `string` feature.
- `movieGenres`: a `list` of `string` features.
- `characterID1`: a `string` feature.
- `characterID2`: a `string` feature.
- `characterName1`: a `string` feature.
- `characterName2`: a `string` feature.
- `utterance`: a dictionary feature containing:
- `text`: a `string` feature.
- `LineID`: a `string` feature.
### Data Splits
| name |train|
|-------|----:|
|default|83097|
## 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{Danescu-Niculescu-Mizil+Lee:11a,
author={Cristian Danescu-Niculescu-Mizil and Lillian Lee},
title={Chameleons in imagined conversations:
A new approach to understanding coordination of linguistic style in dialogs.},
booktitle={Proceedings of the
Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011},
year={2011}
}
```
### Contributions
Thanks to [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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|
wyzelabs/RuleRecommendation | wyzelabs | 2023-11-02T14:53:43Z | 416 | 9 | [
"license:cc-by-nc-nd-4.0",
"IoT",
"Smart Home",
"Rule Recommendation",
"Recommendation Systems",
"region:us"
]
| null | 2023-07-12T18:32:35Z | ---
license: cc-by-nc-nd-4.0
extra_gated_heading: >-
Wyze Rule Recommendation Challenge Participation and Dataset Access Terms and
Conditions
extra_gated_prompt: >-
Please read the <a href="https://drive.google.com/uc?id=1v-4gjp1EQZcdxYn6uZfft6CVKtWh3S87" target="_blank">Wyze Rule Recommendation Challenge Participation and Dataset Access Terms and Conditions</a>
carefully. In order to gain access to the data and take part in the Wyze Rule
Recommendation challenge, you must first read and consent to these terms and
conditions.
extra_gated_fields:
Name: text
Affiliation: text
Email: text
I have read and agree to the Wyze Rule Recommendation Challenge Participation and Dataset Access Terms and Conditions: checkbox
tags:
- IoT
- Smart Home
- Rule Recommendation
- Recommendation Systems
pretty_name: Wyze Rule Recommendation Dataset
---
# Wyze Rule Recommendation Dataset
<img src="https://drive.google.com/uc?id=17X5SpY8m-IQD35EZ7hy0uBlUqDhZiJ4r" alt="WRR" width="100%"/>
<!---
## Dataset Description
- **Paper:TBA**
- **Leaderboard:TBA**
- **Point of Contact:**
--->
## Dataset Summary
The Wyze Rule dataset is a new large-scale dataset designed specifically for smart home rule recommendation research. It contains over 1 million rules generated by 300,000 users from Wyze Labs, offering an extensive collection of real-world automation rules tailored to users' unique smart home setups.
The goal of the Wyze Rule dataset is to advance research and development of personalized rule recommendation systems for smart home automation. As smart devices proliferate in homes, automating their interactions becomes increasingly complex. Rules recommend how a user's devices could be connected to work together automatically, like a motion sensor triggering a camera to record. But with users having different devices, manually configuring these rules is difficult. This dataset enables creating intelligent algorithms that automatically recommend customized rules tailored to each user's specific smart home setup. By training machine learning models on the diverse real-world data of over 1 million rules from 300,000 Wyze users, researchers can build personalized recommendation systems. These would simplify and enhance automation for end users by suggesting rules that connect their devices in useful ways, while respecting their privacy. The Wyze Rule dataset provides the large-scale and varied data needed to make such personalized, private rule recommendation a reality.
The key features of this dataset are:
- Over 1 million automation rules governing how users' smart devices interact
- Rules are highly personalized based on each user's specific devices and needs
- 16 distinct device types like cameras, sensors, lights etc.
- There are 44 different trigger states and 46 different action by various devices.
- 1,641 unique trigger-action device and state (trigger_device + trigger_state + action + action_device) pairs capturing diverse automation logics
- Non-IID distribution among users makes it suitable for federated learning
- Allows development of personalized rule recommendation systems while preserving user privacy
- Enables benchmarking different algorithms on large-scale real-world data
Overall, the Wyze Rule dataset bridges the gap between rule recommendation research and practical applications, facilitating the creation of intelligent home automation systems. Its scale, diversity, and focus on individual users' needs make it a valuable resource for advancing personalized recommendation techniques.
## Dataset Structure
The Wyze Rule dataset contains two main CSV files - one for the rules and one for the devices owned by each user.
Each rule has attributes like user ID, trigger device, trigger state, action device, and action.
For example, a rule could be: user 123, contact sensor, "open", light bulb, "turn on".
This captures the trigger condition and the action to take. The device file maps user IDs to the specific devices owned by each user.
This is key because automating different device setups requires different valid rules.
With 16 device types and 1641 trigger-action state and device pairs, the rules reflect a user's customized needs.
Each user can have multiple instances of a device type, like several motion sensors.
The non-IID distribution of rules among 300,000 users with varying device combinations makes this dataset uniquely suitable for developing personalized federated learning algorithms for rule recommendation.
By separating rules into triggers and actions, the data structure provides flexibility lacking in user-item matrices that treat rules as single items.
Overall, the real-world granularity enables personalized automation.
### Data Fields
The main two files of this dataset, rules and devices, have the following fields:
1. Rule Dataset: This dataset contains data related to the rules that govern the behavior of Wyze smart home devices. Each row represents a single rule and contains various attributes describing the rule. The attributes of this file are as follows:
+ `user_id` (int): A unique integer identifier for the user associated with the rule. This identifier has been anonymized and does not contain any information related to the Wyze users.
+ `trigger_device` (str): The model of the device that triggers the rule when a specific condition is met. It may be a Wyze smart home device such as a sensor or a camera.
+ `trigger_device_id` (int): A unique integer identifier for the trigger device.
+ `trigger_state` (str): The state or condition that needs to be met on the trigger device for the rule to be activated. It may represent values such as "on," "off," "motion detected," or "sensor open."
+ `trigger_state_id` (int): A unique integer identifier for the trigger state.
+ `action` (str): The action to be executed on the action device when the rule is triggered. It may include values like "power on," "power off," "start recording," or "change brightness."
+ `action_id` (int): A unique integer identifier for the action.
+ `action_device` (str): The model of the device that performs an action when the rule is triggered. It is a Wyze smart home device such as a light or a camera.
+ `action_device_id` (int): A unique integer identifier for the action device.
+ `rule` (str): The combination of 4 ids as follows: `trigger_device_id`\_\_`trigger_state_id`\_\_`action_id`\_\_`action_device_id`
3. Device Dataset: This file contains data related to the devices owned by users. Each row represents a single device and contains information about the device model and its association with a specific user. There are a number of devices in this dataset that are not used in any rules by users, and hence, are not present in the rule dataset. The attributes of this dataset are as follows:
+ `user_id` (int): A unique integer identifier for the user associated with the device.
+ `device_id` (int): A unique integer identifier for the device.
+ `device_model` (str): The model or type of the device owned by the user. It represents various Wyze smart home devices such as a camera, a sensor, or a switch
There are a total of 16 different device types included in this dataset as follows:
1. `Camera`
2. `ClimateSensor`
3. `Cloud`
4. `ContactSensor`
5. `Irrigation`
6. `LeakSensor`
7. `Light`
8. `LightStrip`
9. `Lock`
10. `MeshLight`
11. `MotionSensor`
12. `OutdoorPlug`
13. `Plug`
14. `RobotVacuum`
15. `Switch`
16. `Thermostat`
### Data Splits
We have two public splits, which are `train` and `test`. The `train` split contains all the available rules set by the users in the dataset, as well as their device list. In the `test` dataset, for each user in this dataset, we have omitted one rule at random. The goal of building recommendation system is to recommend that omitted rule with high probability. The ground truth for this dataset will be released after the Wyze Rule Recommendation challenge has finished.
### Personal and Sensitive Information
Protecting user privacy was a top priority when creating the Wyze Rule dataset.
Any personally identifiable information or sensitive data that could reveal users' identities has been meticulously obscured.
The user IDs have been anonymized into random numeric values, removing any links to actual Wyze users.
The rules simply capture abstract triggers and actions for automation using generic device types.
By only retaining high-level functionality while erasing all personal attributes, the Wyze Rule dataset enables developing personalized recommendation algorithms without compromising user privacy.
Researchers can leverage this rich real-world data to advance the field of automation systems significantly while ensuring ethical data practices.
The dataset creators' commitment to protecting users' privacy will help propel innovation responsibly.
## Considerations for Using the Data
This data is mainly released for the [Wyze Rule Recommendation Challenge](https://huggingface.co/spaces/competitions/wyze-rule-recommendation).
### Licensing Information
This dataset is licensed by cc-by-nc-nd-4.0, which prohibits commercial use, distribution, modification, and reproduction of the data without permission from the copyright holder.
### Citation Information
TBA
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|
generated_reviews_enth | null | 2023-01-25T14:30:46Z | 415 | 4 | [
"task_categories:translation",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:th",
"license:cc-by-sa-4.0",
"arxiv:2007.03541",
"arxiv:1909.05858",
"region:us"
]
| [
"translation",
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- machine-generated
language:
- en
- th
license:
- cc-by-sa-4.0
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
- text-classification
task_ids:
- multi-class-classification
- semantic-similarity-classification
pretty_name: generated_reviews_enth
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- en
- th
- name: review_star
dtype: int32
- name: correct
dtype:
class_label:
names:
'0': neg
'1': pos
config_name: generated_reviews_enth
splits:
- name: train
num_bytes: 147673215
num_examples: 141369
- name: validation
num_bytes: 16409966
num_examples: 15708
- name: test
num_bytes: 18133523
num_examples: 17453
download_size: 59490601
dataset_size: 182216704
---
# Dataset Card for generated_reviews_enth
## 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:** ttp://airesearch.in.th/
- **Repository:** https://github.com/vistec-ai/generated_reviews_enth
- **Paper:** https://arxiv.org/pdf/2007.03541.pdf
- **Leaderboard:**
- **Point of Contact:** [AIResearch](http://airesearch.in.th/)
### Dataset Summary
`generated_reviews_enth` is created as part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) for machine translation task. This dataset (referred to as `generated_reviews_yn` in [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf)) are English product reviews generated by [CTRL](https://arxiv.org/abs/1909.05858), translated by Google Translate API and annotated as accepted or rejected (`correct`) based on fluency and adequacy of the translation by human annotators. This allows it to be used for English-to-Thai translation quality esitmation (binary label), machine translation, and sentiment analysis.
### Supported Tasks and Leaderboards
English-to-Thai translation quality estimation (binary label) is the intended use. Other uses include machine translation and sentiment analysis.
### Languages
English, Thai
## Dataset Structure
### Data Instances
```
{'correct': 0, 'review_star': 4, 'translation': {'en': "I had a hard time finding a case for my new LG Lucid 2 but finally found this one on amazon. The colors are really pretty and it works just as well as, if not better than the otterbox. Hopefully there will be more available by next Xmas season. Overall, very cute case. I love cheetah's. :)", 'th': 'ฉันมีปัญหาในการหาเคสสำหรับ LG Lucid 2 ใหม่ของฉัน แต่ในที่สุดก็พบเคสนี้ใน Amazon สีสวยมากและใช้งานได้ดีเช่นเดียวกับถ้าไม่ดีกว่านาก หวังว่าจะมีให้มากขึ้นในช่วงเทศกาลคริสต์มาสหน้า โดยรวมแล้วน่ารักมาก ๆ ฉันรักเสือชีตาห์ :)'}}
{'correct': 0, 'review_star': 1, 'translation': {'en': "This is the second battery charger I bought as a Christmas present, that came from Amazon, after one purchased before for my son. His was still working. The first charger, received in July, broke apart and wouldn't charge anymore. Just found out two days ago they discontinued it without warning. It took quite some time to find the exact replacement charger. Too bad, really liked it. One of these days, will purchase an actual Nikon product, or go back to buying batteries.", 'th': 'นี่เป็นเครื่องชาร์จแบตเตอรี่ก้อนที่สองที่ฉันซื้อเป็นของขวัญคริสต์มาสซึ่งมาจากอเมซอนหลังจากที่ซื้อมาเพื่อลูกชายของฉัน เขายังทำงานอยู่ เครื่องชาร์จแรกที่ได้รับในเดือนกรกฎาคมแตกเป็นชิ้น ๆ และจะไม่ชาร์จอีกต่อไป เพิ่งค้นพบเมื่อสองวันก่อนพวกเขาหยุดมันโดยไม่มีการเตือนล่วงหน้า ใช้เวลาพอสมควรในการหาที่ชาร์จที่ถูกต้อง แย่มากชอบมาก สักวันหนึ่งจะซื้อผลิตภัณฑ์ Nikon จริงหรือกลับไปซื้อแบตเตอรี่'}}
{'correct': 1, 'review_star': 1, 'translation': {'en': 'I loved the idea of having a portable computer to share pictures with family and friends on my big screen. It worked really well for about 3 days, then when i opened it one evening there was water inside where all the wires came out. I cleaned that up and put some tape over that, so far, no leaks. My husband just told me yesterday, however, that this thing is trash.', 'th': 'ฉันชอบไอเดียที่มีคอมพิวเตอร์พกพาเพื่อแชร์รูปภาพกับครอบครัวและเพื่อน ๆ บนหน้าจอขนาดใหญ่ของฉัน มันใช้งานได้ดีจริง ๆ ประมาณ 3 วันจากนั้นเมื่อฉันเปิดมันในเย็นวันหนึ่งมีน้ำอยู่ภายในที่ซึ่งสายไฟทั้งหมดออกมา ฉันทำความสะอาดมันแล้ววางเทปไว้ที่นั่นจนถึงตอนนี้ไม่มีรอยรั่ว สามีของฉันเพิ่งบอกฉันเมื่อวานนี้ว่าสิ่งนี้เป็นขยะ'}}
```
### Data Fields
- `translation`:
- `en`: English product reviews generated by [CTRL](https://arxiv.org/abs/1909.05858)
- `th`: Thai product reviews translated from `en` by Google Translate API
- `review_star`: Stars of the generated reviews, put as condition for [CTRL](https://arxiv.org/abs/1909.05858)
- `correct`: 1 if the English-to-Thai translation is accepted (`correct`) based on fluency and adequacy of the translation by human annotators else 0
### Data Splits
| | train | valid | test |
|-----------------|--------|-------|-------|
| # samples | 141369 | 15708 | 17453 |
| # correct:0 | 99296 | 10936 | 12208 |
| # correct:1 | 42073 | 4772 | 5245 |
| # review_star:1 | 50418 | 5628 | 6225 |
| # review_star:2 | 22876 | 2596 | 2852 |
| # review_star:3 | 22825 | 2521 | 2831 |
| # review_star:1 | 22671 | 2517 | 2778 |
| # review_star:5 | 22579 | 2446 | 2767 |
## Dataset Creation
### Curation Rationale
`generated_reviews_enth` is created as part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) for machine translation task. This dataset (referred to as `generated_reviews_yn` in [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf)) are English product reviews generated by [CTRL](https://arxiv.org/abs/1909.05858), translated by Google Translate API and annotated as accepted or rejected (`correct`) based on fluency and adequacy of the translation by human annotators. This allows it to be used for English-to-Thai translation quality esitmation (binary label), machine translation, and sentiment analysis.
### Source Data
#### Initial Data Collection and Normalization
The data generation process is as follows:
- `en` is generated using conditional generation of [CTRL](https://arxiv.org/abs/1909.05858), stating a star review for each generated product review.
- `th` is translated from `en` using Google Translate API
- `correct` is annotated as accepted or rejected (1 or 0) based on fluency and adequacy of the translation by human annotators
For this specific dataset for translation quality estimation task, we apply the following preprocessing:
- Drop duplciates on `en`,`th`,`review_star`,`correct`; duplicates might exist because the translation checking is done by annotators.
- Remove reviews that are not between 1-5 stars.
- Remove reviews whose `correct` are not 0 or 1.
- Deduplicate on `en` which contains the source sentences.
#### Who are the source language producers?
[CTRL](https://arxiv.org/abs/1909.05858)
### Annotations
#### Annotation process
Annotators are given English and Thai product review pairs. They are asked to label the pair as acceptable translation or not based on fluency and adequacy of the translation.
#### Who are the annotators?
Human annotators of [Hope Data Annotations](https://www.hopedata.org/) hired by [AIResearch.in.th](http://airesearch.in.th/)
### Personal and Sensitive Information
The authors do not expect any personal or sensitive information to be in the generated product reviews, but they could slip through from pretraining of [CTRL](https://arxiv.org/abs/1909.05858).
## Considerations for Using the Data
### Social Impact of Dataset
- English-Thai translation quality estimation for machine translation
- Product review classification for Thai
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Due to annotation process constraints, the number of one-star reviews are notably higher than other-star reviews. This makes the dataset slighly imbalanced.
## Additional Information
### Dataset Curators
The dataset was created by [AIResearch.in.th](http://airesearch.in.th/)
### Licensing Information
CC BY-SA 4.0
### Citation Information
```
@article{lowphansirikul2020scb,
title={scb-mt-en-th-2020: A Large English-Thai Parallel Corpus},
author={Lowphansirikul, Lalita and Polpanumas, Charin and Rutherford, Attapol T and Nutanong, Sarana},
journal={arXiv preprint arXiv:2007.03541},
year={2020}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | [
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|
open-llm-leaderboard/details_cyberagent__open-calm-7b | open-llm-leaderboard | 2023-09-18T05:13:53Z | 415 | 0 | [
"region:us"
]
| null | 2023-08-18T11:51:48Z | ---
pretty_name: Evaluation run of cyberagent/open-calm-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [cyberagent/open-calm-7b](https://huggingface.co/cyberagent/open-calm-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_cyberagent__open-calm-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-18T05:13:41.363810](https://huggingface.co/datasets/open-llm-leaderboard/details_cyberagent__open-calm-7b/blob/main/results_2023-09-18T05-13-41.363810.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\": 0.009123322147651007,\n\
\ \"em_stderr\": 0.000973701770554162,\n \"f1\": 0.039316275167785276,\n\
\ \"f1_stderr\": 0.0014587233446804973,\n \"acc\": 0.24383651483119942,\n\
\ \"acc_stderr\": 0.00767932509907164\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.009123322147651007,\n \"em_stderr\": 0.000973701770554162,\n\
\ \"f1\": 0.039316275167785276,\n \"f1_stderr\": 0.0014587233446804973\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.002274450341167551,\n \
\ \"acc_stderr\": 0.0013121578148674368\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.48539857932123126,\n \"acc_stderr\": 0.014046492383275842\n\
\ }\n}\n```"
repo_url: https://huggingface.co/cyberagent/open-calm-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_19T16_51_08.421995
path:
- '**/details_harness|arc:challenge|25_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_18T05_13_41.363810
path:
- '**/details_harness|drop|3_2023-09-18T05-13-41.363810.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-18T05-13-41.363810.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_18T05_13_41.363810
path:
- '**/details_harness|gsm8k|5_2023-09-18T05-13-41.363810.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-18T05-13-41.363810.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hellaswag|10_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:51:08.421995.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T16:51:08.421995.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T16:51:08.421995.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_18T05_13_41.363810
path:
- '**/details_harness|winogrande|5_2023-09-18T05-13-41.363810.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-18T05-13-41.363810.parquet'
- config_name: results
data_files:
- split: 2023_07_19T16_51_08.421995
path:
- results_2023-07-19T16:51:08.421995.parquet
- split: 2023_09_18T05_13_41.363810
path:
- results_2023-09-18T05-13-41.363810.parquet
- split: latest
path:
- results_2023-09-18T05-13-41.363810.parquet
---
# Dataset Card for Evaluation run of cyberagent/open-calm-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/cyberagent/open-calm-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 [cyberagent/open-calm-7b](https://huggingface.co/cyberagent/open-calm-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_cyberagent__open-calm-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-18T05:13:41.363810](https://huggingface.co/datasets/open-llm-leaderboard/details_cyberagent__open-calm-7b/blob/main/results_2023-09-18T05-13-41.363810.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": 0.009123322147651007,
"em_stderr": 0.000973701770554162,
"f1": 0.039316275167785276,
"f1_stderr": 0.0014587233446804973,
"acc": 0.24383651483119942,
"acc_stderr": 0.00767932509907164
},
"harness|drop|3": {
"em": 0.009123322147651007,
"em_stderr": 0.000973701770554162,
"f1": 0.039316275167785276,
"f1_stderr": 0.0014587233446804973
},
"harness|gsm8k|5": {
"acc": 0.002274450341167551,
"acc_stderr": 0.0013121578148674368
},
"harness|winogrande|5": {
"acc": 0.48539857932123126,
"acc_stderr": 0.014046492383275842
}
}
```
### 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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|
result-kand2-sdxl-wuerst-karlo/a6175b5f | result-kand2-sdxl-wuerst-karlo | 2023-11-07T19:09:35Z | 415 | 0 | [
"region:us"
]
| null | 2023-11-07T19:09:33Z | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1303
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a6175b5f"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
mocha | null | 2022-11-18T21:29:45Z | 414 | 2 | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"generative-reading-comprehension-metric",
"region:us"
]
| [
"question-answering"
]
| 2022-03-02T23:29:22Z | ---
pretty_name: MOCHA
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: mocha
tags:
- generative-reading-comprehension-metric
dataset_info:
features:
- name: constituent_dataset
dtype: string
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: reference
dtype: string
- name: candidate
dtype: string
- name: score
dtype: float32
- name: metadata
struct:
- name: scores
sequence: int32
- name: source
dtype: string
- name: candidate2
dtype: string
- name: score2
dtype: float32
splits:
- name: train
num_bytes: 33292592
num_examples: 31069
- name: validation
num_bytes: 4236883
num_examples: 4009
- name: test
num_bytes: 6767409
num_examples: 6321
- name: minimal_pairs
num_bytes: 193560
num_examples: 200
download_size: 14452311
dataset_size: 44490444
---
# Dataset Card for Mocha
## 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:** [Mocha](https://allennlp.org/mocha)
- **Repository:** [https://github.com/anthonywchen/MOCHA](https://github.com/anthonywchen/MOCHA)
- **Paper:** [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://www.aclweb.org/anthology/2020.emnlp-main.528/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, we train a Learned Evaluation metric for Reading Comprehension, LERC, to mimic human judgement scores. LERC outperforms baseline metrics by 10 to 36 absolute Pearson points on held-out annotations. When we evaluate robustness on minimal pairs, LERC achieves 80% accuracy, outperforming baselines by 14 to 26 absolute percentage points while leaving significant room for improvement. MOCHA presents a challenging problem for developing accurate and robust generative reading comprehension metrics.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. MOCHA pairs reading comprehension instances, which consists of a passage, question, and reference, with candidates and human judgement scores.
### Data Fields
- `constituent_dataset`: the original QA dataset which the data instance came from.
- `id`
- `context`: the passage content.
- `question`: the question related to the passage content.
- `reference`: the correct answer for the question.
- `candidate`: the answer generated from the `reference` by `source`
- `score`: the human judgement score for the `candidate`. Not included in test split, defaults to `-1`
- `metadata`: Not included in minimal pairs split.
- `scores`: list of scores from difference judges, averaged out to get final `score`. defaults to `[]`
- `source`: the generative model to generate the `candidate`
In minimal pairs, we'll have an additional candidate for robust evaluation.
- `candidate2`
- `score2`
### Data Splits
Dataset Split | Number of Instances in Split
--------------|--------------------------------------------
Train | 31,069
Validation | 4,009
Test | 6,321
Minimal Pairs | 200
## 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 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
### Citation Information
```bitex
@inproceedings{Chen2020MOCHAAD,
author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics},
booktitle={EMNLP},
year={2020}
}
```
### Contributions
Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset. | [
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|
msr_sqa | null | 2022-11-18T21:30:23Z | 414 | 1 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:ms-pl",
"region:us"
]
| [
"question-answering"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- ms-pl
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_name: Microsoft Research Sequential Question Answering
dataset_info:
features:
- name: id
dtype: string
- name: annotator
dtype: int32
- name: position
dtype: int32
- name: question
dtype: string
- name: question_and_history
sequence: string
- name: table_file
dtype: string
- name: table_header
sequence: string
- name: table_data
sequence:
sequence: string
- name: answer_coordinates
sequence:
- name: row_index
dtype: int32
- name: column_index
dtype: int32
- name: answer_text
sequence: string
splits:
- name: train
num_bytes: 19732499
num_examples: 12276
- name: validation
num_bytes: 3738331
num_examples: 2265
- name: test
num_bytes: 5105873
num_examples: 3012
download_size: 4796932
dataset_size: 28576703
---
# Dataset Card for Microsoft Research Sequential Question Answering
## 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 Research Sequential Question Answering (SQA) Dataset](https://msropendata.com/datasets/b25190ed-0f59-47b1-9211-5962858142c2)
- **Repository:**
- **Paper:** [https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf)
- **Leaderboard:**
- **Point of Contact:**
- Scott Wen-tau Yih [email protected]
- Mohit Iyyer [email protected]
- Ming-Wei Chang [email protected]
### Dataset Summary
Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions.
We created SQA by asking crowdsourced workers to decompose 2,022 questions from WikiTableQuestions (WTQ)*, which contains highly-compositional questions about tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences that contain 17,553 questions in total. Each question is also associated with answers in the form of cell locations in the tables.
- Panupong Pasupat, Percy Liang. "Compositional Semantic Parsing on Semi-Structured Tables" ACL-2015.
[http://www-nlp.stanford.edu/software/sempre/wikitable/](http://www-nlp.stanford.edu/software/sempre/wikitable/)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`).
## Dataset Structure
### Data Instances
```
{'id': 'nt-639',
'annotator': 0,
'position': 0,
'question': 'where are the players from?',
'table_file': 'table_csv/203_149.csv',
'table_header': ['Pick', 'Player', 'Team', 'Position', 'School'],
'table_data': [['1',
'Ben McDonald',
'Baltimore Orioles',
'RHP',
'Louisiana State University'],
['2',
'Tyler Houston',
'Atlanta Braves',
'C',
'"Valley HS (Las Vegas',
' NV)"'],
['3', 'Roger Salkeld', 'Seattle Mariners', 'RHP', 'Saugus (CA) HS'],
['4',
'Jeff Jackson',
'Philadelphia Phillies',
'OF',
'"Simeon HS (Chicago',
' IL)"'],
['5', 'Donald Harris', 'Texas Rangers', 'OF', 'Texas Tech University'],
['6', 'Paul Coleman', 'Saint Louis Cardinals', 'OF', 'Frankston (TX) HS'],
['7', 'Frank Thomas', 'Chicago White Sox', '1B', 'Auburn University'],
['8', 'Earl Cunningham', 'Chicago Cubs', 'OF', 'Lancaster (SC) HS'],
['9',
'Kyle Abbott',
'California Angels',
'LHP',
'Long Beach State University'],
['10',
'Charles Johnson',
'Montreal Expos',
'C',
'"Westwood HS (Fort Pierce',
' FL)"'],
['11',
'Calvin Murray',
'Cleveland Indians',
'3B',
'"W.T. White High School (Dallas',
' TX)"'],
['12', 'Jeff Juden', 'Houston Astros', 'RHP', 'Salem (MA) HS'],
['13', 'Brent Mayne', 'Kansas City Royals', 'C', 'Cal State Fullerton'],
['14',
'Steve Hosey',
'San Francisco Giants',
'OF',
'Fresno State University'],
['15',
'Kiki Jones',
'Los Angeles Dodgers',
'RHP',
'"Hillsborough HS (Tampa',
' FL)"'],
['16', 'Greg Blosser', 'Boston Red Sox', 'OF', 'Sarasota (FL) HS'],
['17', 'Cal Eldred', 'Milwaukee Brewers', 'RHP', 'University of Iowa'],
['18',
'Willie Greene',
'Pittsburgh Pirates',
'SS',
'"Jones County HS (Gray',
' GA)"'],
['19', 'Eddie Zosky', 'Toronto Blue Jays', 'SS', 'Fresno State University'],
['20', 'Scott Bryant', 'Cincinnati Reds', 'OF', 'University of Texas'],
['21', 'Greg Gohr', 'Detroit Tigers', 'RHP', 'Santa Clara University'],
['22',
'Tom Goodwin',
'Los Angeles Dodgers',
'OF',
'Fresno State University'],
['23', 'Mo Vaughn', 'Boston Red Sox', '1B', 'Seton Hall University'],
['24', 'Alan Zinter', 'New York Mets', 'C', 'University of Arizona'],
['25', 'Chuck Knoblauch', 'Minnesota Twins', '2B', 'Texas A&M University'],
['26', 'Scott Burrell', 'Seattle Mariners', 'RHP', 'Hamden (CT) HS']],
'answer_coordinates': {'row_index': [0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25],
'column_index': [4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4]},
'answer_text': ['Louisiana State University',
'Valley HS (Las Vegas, NV)',
'Saugus (CA) HS',
'Simeon HS (Chicago, IL)',
'Texas Tech University',
'Frankston (TX) HS',
'Auburn University',
'Lancaster (SC) HS',
'Long Beach State University',
'Westwood HS (Fort Pierce, FL)',
'W.T. White High School (Dallas, TX)',
'Salem (MA) HS',
'Cal State Fullerton',
'Fresno State University',
'Hillsborough HS (Tampa, FL)',
'Sarasota (FL) HS',
'University of Iowa',
'Jones County HS (Gray, GA)',
'Fresno State University',
'University of Texas',
'Santa Clara University',
'Fresno State University',
'Seton Hall University',
'University of Arizona',
'Texas A&M University',
'Hamden (CT) HS']}
```
### Data Fields
- `id` (`str`): question sequence id (the id is consistent with those in WTQ)
- `annotator` (`int`): `0`, `1`, `2` (the 3 annotators who annotated the question intent)
- `position` (`int`): the position of the question in the sequence
- `question` (`str`): the question given by the annotator
- `table_file` (`str`): the associated table
- `table_header` (`List[str]`): a list of headers in the table
- `table_data` (`List[List[str]]`): 2d array of data in the table
- `answer_coordinates` (`List[Dict]`): the table cell coordinates of the answers (0-based, where 0 is the first row after the table header)
- `row_index`
- `column_index`
- `answer_text` (`List[str]`): the content of the answer cells
Note that some text fields may contain Tab or LF characters and thus start with quotes.
It is recommended to use a CSV parser like the Python CSV package to process the data.
### Data Splits
| | train | test |
|-------------|------:|-----:|
| N. examples | 14541 | 3012 |
## 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
[Microsoft Research Data License Agreement](https://msropendata-web-api.azurewebsites.net/licenses/2f933be3-284d-500b-7ea3-2aa2fd0f1bb2/view).
### Citation Information
```
@inproceedings{iyyer-etal-2017-search,
title = "Search-based Neural Structured Learning for Sequential Question Answering",
author = "Iyyer, Mohit and
Yih, Wen-tau and
Chang, Ming-Wei",
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://aclanthology.org/P17-1167",
doi = "10.18653/v1/P17-1167",
pages = "1821--1831",
}
```
### Contributions
Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset. | [
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|
bigbio/blurb | bigbio | 2022-12-22T15:27:48Z | 414 | 1 | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
]
| null | 2022-10-03T06:19:58Z | ---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: MIXED
pretty_name: BLURB
homepage: https://microsoft.github.io/BLURB/tasks.html
bigbio_pubmed: true
bigbio_public: true
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for BLURB
## Dataset Description
- **Homepage:** https://microsoft.github.io/BLURB/tasks.html
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
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.
This implementation contains a subset of 5 tasks as of 2022.10.06, with their original train, dev, and test splits.
## Citation Information
```
@article{gu2021domain,
title = {
Domain-specific language model pretraining for biomedical natural
language processing
},
author = {
Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and
Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao,
Jianfeng and Poon, Hoifung
},
year = 2021,
journal = {ACM Transactions on Computing for Healthcare (HEALTH)},
publisher = {ACM New York, NY},
volume = 3,
number = 1,
pages = {1--23}
}
```
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|
open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-test-WVG | open-llm-leaderboard | 2023-10-28T07:52:36Z | 414 | 0 | [
"region:us"
]
| null | 2023-10-03T17:19:32Z | ---
pretty_name: Evaluation run of LTC-AI-Labs/L2-7b-Base-test-WVG
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [LTC-AI-Labs/L2-7b-Base-test-WVG](https://huggingface.co/LTC-AI-Labs/L2-7b-Base-test-WVG)\
\ 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 3 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_LTC-AI-Labs__L2-7b-Base-test-WVG\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T07:52:27.189086](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-test-WVG/blob/main/results_2023-10-28T07-52-27.189086.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\": 0.003145973154362416,\n\
\ \"em_stderr\": 0.0005734993648436373,\n \"f1\": 0.07481229026845672,\n\
\ \"f1_stderr\": 0.0016422896702234556,\n \"acc\": 0.40267285313968093,\n\
\ \"acc_stderr\": 0.00970555723399882\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.003145973154362416,\n \"em_stderr\": 0.0005734993648436373,\n\
\ \"f1\": 0.07481229026845672,\n \"f1_stderr\": 0.0016422896702234556\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06974981046247157,\n \
\ \"acc_stderr\": 0.007016389571013843\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7355958958168903,\n \"acc_stderr\": 0.012394724896983799\n\
\ }\n}\n```"
repo_url: https://huggingface.co/LTC-AI-Labs/L2-7b-Base-test-WVG
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_03T17_19_09.186622
path:
- '**/details_harness|arc:challenge|25_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_28T06_36_16.797528
path:
- '**/details_harness|drop|3_2023-10-28T06-36-16.797528.parquet'
- split: 2023_10_28T07_52_27.189086
path:
- '**/details_harness|drop|3_2023-10-28T07-52-27.189086.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T07-52-27.189086.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T06_36_16.797528
path:
- '**/details_harness|gsm8k|5_2023-10-28T06-36-16.797528.parquet'
- split: 2023_10_28T07_52_27.189086
path:
- '**/details_harness|gsm8k|5_2023-10-28T07-52-27.189086.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T07-52-27.189086.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hellaswag|10_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T17-19-09.186622.parquet'
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- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T17-19-09.186622.parquet'
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- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T17-19-09.186622.parquet'
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- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T17-19-09.186622.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T17-19-09.186622.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T17-19-09.186622.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T06_36_16.797528
path:
- '**/details_harness|winogrande|5_2023-10-28T06-36-16.797528.parquet'
- split: 2023_10_28T07_52_27.189086
path:
- '**/details_harness|winogrande|5_2023-10-28T07-52-27.189086.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T07-52-27.189086.parquet'
- config_name: results
data_files:
- split: 2023_10_03T17_19_09.186622
path:
- results_2023-10-03T17-19-09.186622.parquet
- split: 2023_10_28T06_36_16.797528
path:
- results_2023-10-28T06-36-16.797528.parquet
- split: 2023_10_28T07_52_27.189086
path:
- results_2023-10-28T07-52-27.189086.parquet
- split: latest
path:
- results_2023-10-28T07-52-27.189086.parquet
---
# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Base-test-WVG
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/LTC-AI-Labs/L2-7b-Base-test-WVG
- **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 [LTC-AI-Labs/L2-7b-Base-test-WVG](https://huggingface.co/LTC-AI-Labs/L2-7b-Base-test-WVG) 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 3 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_LTC-AI-Labs__L2-7b-Base-test-WVG",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T07:52:27.189086](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-test-WVG/blob/main/results_2023-10-28T07-52-27.189086.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": 0.003145973154362416,
"em_stderr": 0.0005734993648436373,
"f1": 0.07481229026845672,
"f1_stderr": 0.0016422896702234556,
"acc": 0.40267285313968093,
"acc_stderr": 0.00970555723399882
},
"harness|drop|3": {
"em": 0.003145973154362416,
"em_stderr": 0.0005734993648436373,
"f1": 0.07481229026845672,
"f1_stderr": 0.0016422896702234556
},
"harness|gsm8k|5": {
"acc": 0.06974981046247157,
"acc_stderr": 0.007016389571013843
},
"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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|
McGill-NLP/TopiOCQA | McGill-NLP | 2023-09-29T19:37:48Z | 413 | 4 | [
"task_categories:text-retrieval",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"language:en",
"license:cc-by-nc-sa-4.0",
"conversational-question-answering",
"arxiv:2110.00768",
"region:us"
]
| [
"text-retrieval",
"text-generation"
]
| 2022-04-08T18:29:53Z | ---
annotations_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
task_categories:
- text-retrieval
- text-generation
task_ids:
- language-modeling
- open-domain-qa
pretty_name: Open-domain Conversational Question Answering with Topic Switching
tags:
- conversational-question-answering
---
# Dataset Card for TopiOCQA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [TopiOCQA homepage](https://mcgill-nlp.github.io/topiocqa/)
- **Repository:** [TopiOCQA Github](https://github.com/McGill-NLP/topiocqa)
- **Paper:** [Open-domain Conversational Question Answering with Topic Switching](https://arxiv.org/abs/2110.00768)
- **Point of Contact:** [Vaibhav Adlakha](mailto:[email protected])
### Dataset Summary
TopiOCQA is an information-seeking conversational dataset with challenging topic switching phenomena.
### Languages
The language in the dataset is English as spoken by the crowdworkers. The BCP-47 code for English is en.
## Additional Information
### Licensing Information
TopiOCQA is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
```
@inproceedings{adlakha2022topiocqa,
title={Topi{OCQA}: Open-domain Conversational Question Answering with Topic Switching},
author={Adlakha, Vaibhav and Dhuliawala, Shehzaad and Suleman, Kaheer and de Vries, Harm and Reddy, Siva},
journal={Transactions of the Association for Computational Linguistics},
volume = {10},
pages = {468-483},
year = {2022},
month = {04},
year={2022},
issn = {2307-387X},
doi = {10.1162/tacl_a_00471},
url = {https://doi.org/10.1162/tacl\_a\_00471},
eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00471/2008126/tacl\_a\_00471.pdf},
}
``` | [
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|
vctk | null | 2023-11-23T14:05:24Z | 412 | 9 | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:text-to-audio",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| [
"automatic-speech-recognition",
"text-to-speech",
"text-to-audio"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: VCTK
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- text-to-speech
- text-to-audio
task_ids: []
paperswithcode_id: vctk
train-eval-index:
- config: main
task: automatic-speech-recognition
task_id: speech_recognition
splits:
train_split: train
col_mapping:
file: path
text: text
metrics:
- type: wer
name: WER
- type: cer
name: CER
dataset_info:
features:
- name: speaker_id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: file
dtype: string
- name: text
dtype: string
- name: text_id
dtype: string
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: region
dtype: string
- name: comment
dtype: string
config_name: main
splits:
- name: train
num_bytes: 40103111
num_examples: 88156
download_size: 11747302977
dataset_size: 40103111
---
# Dataset Card for VCTK
## 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:** [Edinburg DataShare](https://doi.org/10.7488/ds/2645)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This CSTR VCTK Corpus includes around 44-hours of speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.
### Supported Tasks
- `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
A data point comprises the path to the audio file, called `file` and its transcription, called `text`.
```
{
'speaker_id': 'p225',
'text_id': '001',
'text': 'Please call Stella.',
'age': '23',
'gender': 'F',
'accent': 'English',
'region': 'Southern England',
'file': '/datasets/downloads/extracted/8ed7dad05dfffdb552a3699777442af8e8ed11e656feb277f35bf9aea448f49e/wav48_silence_trimmed/p225/p225_001_mic1.flac',
'audio':
{
'path': '/datasets/downloads/extracted/8ed7dad05dfffdb552a3699777442af8e8ed11e656feb277f35bf9aea448f49e/wav48_silence_trimmed/p225/p225_001_mic1.flac',
'array': array([0.00485229, 0.00689697, 0.00619507, ..., 0.00811768, 0.00836182, 0.00854492], dtype=float32),
'sampling_rate': 48000
},
'comment': ''
}
```
Each audio file is a single-channel FLAC with a sample rate of 48000 Hz.
### Data Fields
Each row consists of the following fields:
- `speaker_id`: Speaker ID
- `audio`: Audio recording
- `file`: Path to audio file
- `text`: Text transcription of corresponding audio
- `text_id`: Text ID
- `age`: Speaker's age
- `gender`: Speaker's gender
- `accent`: Speaker's accent
- `region`: Speaker's region, if annotation exists
- `comment`: Miscellaneous comments, if any
### Data Splits
The dataset has no predefined 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
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## 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
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
```bibtex
@inproceedings{Veaux2017CSTRVC,
title = {CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit},
author = {Christophe Veaux and Junichi Yamagishi and Kirsten MacDonald},
year = 2017
}
```
### Contributions
Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset. | [
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|
open-llm-leaderboard/details_tiiuae__falcon-7b | open-llm-leaderboard | 2023-12-03T17:58:23Z | 412 | 0 | [
"region:us"
]
| null | 2023-08-18T00:12:34Z | ---
pretty_name: Evaluation run of tiiuae/falcon-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 122 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 aggregated 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_tiiuae__falcon-7b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T17:58:16.188347](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-7b/blob/main/results_2023-12-03T17-58-16.188347.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\": 0.04624715693707354,\n\
\ \"acc_stderr\": 0.005784991662691836\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.04624715693707354,\n \"acc_stderr\": 0.005784991662691836\n\
\ }\n}\n```"
repo_url: https://huggingface.co/tiiuae/falcon-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_19T10_51_47.706539
path:
- '**/details_harness|arc:challenge|25_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T10_06_45.584443
path:
- '**/details_harness|drop|3_2023-09-17T10-06-45.584443.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T10-06-45.584443.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T10_06_45.584443
path:
- '**/details_harness|gsm8k|5_2023-09-17T10-06-45.584443.parquet'
- split: 2023_12_03T17_58_16.188347
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-58-16.188347.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-58-16.188347.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hellaswag|10_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:51:47.706539.parquet'
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- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:51:47.706539.parquet'
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- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:51:47.706539.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:51:47.706539.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:51:47.706539.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:51:47.706539.parquet'
- split: latest
path:
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data_files:
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path:
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- split: latest
path:
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data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T10_51_47.706539
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T10_51_47.706539
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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- split: latest
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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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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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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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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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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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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- config_name: original_mmlu_abstract_algebra_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_anatomy_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:anatomy|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:anatomy|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_astronomy_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:astronomy|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:astronomy|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_business_ethics_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:business_ethics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:business_ethics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_clinical_knowledge_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_college_biology_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:college_biology|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_biology|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_college_chemistry_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_college_computer_science_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_college_mathematics_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_college_medicine_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:college_medicine|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_medicine|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_college_physics_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:college_physics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_physics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_computer_security_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:computer_security|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:computer_security|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_conceptual_physics_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_econometrics_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:econometrics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:econometrics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_electrical_engineering_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_elementary_mathematics_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_formal_logic_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_global_facts_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_biology_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_chemistry_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_computer_science_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_european_history_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_geography_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_government_and_politics_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_macroeconomics_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_mathematics_5
data_files:
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path:
- '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_microeconomics_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_physics_5
data_files:
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path:
- '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_psychology_5
data_files:
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path:
- '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_statistics_5
data_files:
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path:
- '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_us_history_5
data_files:
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path:
- '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_high_school_world_history_5
data_files:
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path:
- '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_human_aging_5
data_files:
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path:
- '**/details_original|mmlu:human_aging|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:human_aging|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_human_sexuality_5
data_files:
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path:
- '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_international_law_5
data_files:
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path:
- '**/details_original|mmlu:international_law|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:international_law|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_jurisprudence_5
data_files:
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path:
- '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_logical_fallacies_5
data_files:
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path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_machine_learning_5
data_files:
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path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_management_5
data_files:
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path:
- '**/details_original|mmlu:management|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:management|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_marketing_5
data_files:
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path:
- '**/details_original|mmlu:marketing|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:marketing|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_medical_genetics_5
data_files:
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path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_miscellaneous_5
data_files:
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path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_moral_disputes_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_moral_scenarios_5
data_files:
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path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_nutrition_5
data_files:
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path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_philosophy_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_prehistory_5
data_files:
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path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_professional_accounting_5
data_files:
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path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_professional_law_5
data_files:
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path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_professional_medicine_5
data_files:
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path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_professional_psychology_5
data_files:
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path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_public_relations_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_security_studies_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_sociology_5
data_files:
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path:
- '**/details_original|mmlu:sociology|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_us_foreign_policy_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_virology_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:virology|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:virology|5_2023-08-28T20:05:31.227903.parquet'
- config_name: original_mmlu_world_religions_5
data_files:
- split: 2023_08_28T20_05_31.227903
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T20:05:31.227903.parquet'
- split: latest
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T20:05:31.227903.parquet'
- config_name: results
data_files:
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path:
- results_2023-07-19T10:51:47.706539.parquet
- split: 2023_08_28T20_05_31.227903
path:
- results_2023-08-28T20:05:31.227903.parquet
- split: 2023_09_17T10_06_45.584443
path:
- results_2023-09-17T10-06-45.584443.parquet
- split: 2023_12_03T17_58_16.188347
path:
- results_2023-12-03T17-58-16.188347.parquet
- split: latest
path:
- results_2023-12-03T17-58-16.188347.parquet
---
# Dataset Card for Evaluation run of tiiuae/falcon-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tiiuae/falcon-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 [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 122 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 aggregated 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_tiiuae__falcon-7b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T17:58:16.188347](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-7b/blob/main/results_2023-12-03T17-58-16.188347.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": 0.04624715693707354,
"acc_stderr": 0.005784991662691836
},
"harness|gsm8k|5": {
"acc": 0.04624715693707354,
"acc_stderr": 0.005784991662691836
}
}
```
### 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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|
allenai/peer_read | allenai | 2022-11-18T21:37:46Z | 411 | 4 | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"acceptability-classification",
"arxiv:1804.09635",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: peerread
pretty_name: PeerRead
tags:
- acceptability-classification
dataset_info:
- config_name: parsed_pdfs
features:
- name: name
dtype: string
- name: metadata
struct:
- name: source
dtype: string
- name: title
dtype: string
- name: authors
sequence: string
- name: emails
sequence: string
- name: sections
sequence:
- name: heading
dtype: string
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dtype: string
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sequence:
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dtype: string
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sequence: string
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dtype: string
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dtype: string
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dtype: int32
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sequence:
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dtype: int32
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dtype: string
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dtype: int32
- name: endOffset
dtype: int32
- name: year
dtype: int32
- name: abstractText
dtype: string
- name: creator
dtype: string
splits:
- name: train
num_bytes: 571263679
num_examples: 11090
- name: test
num_bytes: 34284777
num_examples: 637
- name: validation
num_bytes: 32488519
num_examples: 637
download_size: 1246688292
dataset_size: 638036975
- config_name: reviews
features:
- name: id
dtype: string
- name: conference
dtype: string
- name: comments
dtype: string
- name: subjects
dtype: string
- name: version
dtype: string
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dtype: string
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sequence: string
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dtype: bool
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sequence:
sequence: string
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sequence:
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dtype: bool
- name: is_annotated
dtype: bool
- name: recommendation
dtype: string
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- name: presentation_format
dtype: string
- name: clarity
dtype: string
- name: meaningful_comparison
dtype: string
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dtype: string
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dtype: string
- name: appropriateness
dtype: string
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dtype: string
splits:
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num_bytes: 15234922
num_examples: 11090
- name: test
num_bytes: 878906
num_examples: 637
- name: validation
num_bytes: 864799
num_examples: 637
download_size: 1246688292
dataset_size: 16978627
---
# Dataset Card for peer_read
## 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://arxiv.org/abs/1804.09635
- **Repository:** https://github.com/allenai/PeerRead
- **Paper:** https://arxiv.org/pdf/1804.09635.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
en-English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
#### parsed_pdfs
- `name`: `string` Filename in the dataset
- `metadata`: `dict` Paper metadata
- `source`: `string` Paper source
- `authors`: `list<string>` List of paper authors
- `title`: `string` Paper title
- `sections`: `list<dict>` List of section heading and corresponding description
- `heading`: `string` Section heading
- `text`: `string` Section description
- `references`: `string` List of references
- `title`: `string` Title of reference paper
- `author`: `list<string>` List of reference paper authors
- `venue`: `string` Reference venue
- `citeRegEx`: `string` Reference citeRegEx
- `shortCiteRegEx`: `string` Reference shortCiteRegEx
- `year`: `int` Reference publish year
- `referenceMentions`: `list<string>` List of reference mentions
- `referenceID`: `int` Reference mention ID
- `context`: `string` Reference mention context
- `startOffset`: `int` Reference startOffset
- `endOffset`: `int` Reference endOffset
- `year`: `int` Paper publish year
- `abstractText`: `string` Paper abstract
- `creator`: `string` Paper creator
#### reviews
- `id`: `int` Review ID
- `conference`: `string` Conference name
- `comments`: `string` Review comments
- `subjects`: `string` Review subjects
- `version`: `string` Review version
- `date_of_submission`: `string` Submission date
- `title`: `string` Paper title
- `authors`: `list<string>` List of paper authors
- `accepted`: `bool` Paper accepted flag
- `abstract`: `string` Paper abstract
- `histories`: `list<string>` Paper details with link
- `reviews`: `dict` Paper reviews
- `date`: `string` Date of review
- `title`: `string` Paper title
- `other_keys`: `string` Reviewer other details
- `originality`: `string` Originality score
- `comments`: `string` Reviewer comments
- `is_meta_review`: `bool` Review type flag
- `recommendation`: `string` Reviewer recommendation
- `replicability`: `string` Replicability score
- `presentation_format`: `string` Presentation type
- `clarity`: `string` Clarity score
- `meaningful_comparison`: `string` Meaningful comparison score
- `substance`: `string` Substance score
- `reviewer_confidence`: `string` Reviewer confidence score
- `soundness_correctness`: `string` Soundness correctness score
- `appropriateness`: `string` Appropriateness score
- `impact`: `string` Impact score
### 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
Dongyeop Kang, Waleed Ammar, Bhavana Dalvi Mishra, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz
### Licensing Information
[More Information Needed]
### Citation Information
@inproceedings{kang18naacl,
title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications},
author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz},
booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)},
address = {New Orleans, USA},
month = {June},
url = {https://arxiv.org/abs/1804.09635},
year = {2018}
}
### Contributions
Thanks to [@vinaykudari](https://github.com/vinaykudari) for adding this dataset. | [
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|
dream-textures/textures-color-normal-1k | dream-textures | 2023-01-13T21:20:22Z | 411 | 7 | [
"task_categories:image-to-image",
"size_categories:1K<n<10K",
"license:cc0-1.0",
"region:us"
]
| [
"image-to-image"
]
| 2023-01-13T21:14:42Z | ---
dataset_info:
features:
- name: color
dtype: image
- name: normal
dtype: image
splits:
- name: train
num_bytes: 110631687.194
num_examples: 1426
download_size: 111043422
dataset_size: 110631687.194
license: cc0-1.0
task_categories:
- image-to-image
size_categories:
- 1K<n<10K
---
# textures-color-normal-1k
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The `textures-color-normal-1k` dataset is an image dataset of 1000+ color and normal map textures in 512x512 resolution.
The dataset was created for use in image to image tasks.
It contains a combination of CC0 procedural and photoscanned PBR materials from [ambientCG](https://ambientcg.com/).
## Dataset Structure
### Data Instances
Each data point contains a 512x512 color texture and the corresponding 512x512 normal map.
### Data Fields
* `color`: the color texture as a PIL image
* `normal`: the normal map as a PIL image
### Data Splits
| | train |
| -- | ----- |
| ambientCG | 1426 |
## Dataset Creation
### Curation Rationale
`textures-color-normal-1k` was created to provide an accesible source of data for automating 3D-asset creation workflows.
The [Dream Textures](https://github.com/carson-katri/dream-textures) add-on is one such tool providing AI automation in Blender.
By training models designed for image to image tasks, this particular use-case can be more accurately automated.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained from [ambientCG](https://ambientcg.com/)'s CC0 textures. Only the color and normal maps were included in this dataset.
## Additional Information
### Dataset Curators
The dataset was created by Carson Katri, with the images being provided by [ambientCG](https://ambientcg.com/).
### Licensing Information
All of the images used in this dataset are CC0.
### Citation Information
[N/A]
### Contributions
Thanks to [@carson-katri](https://github.com/carson-katri) for adding this dataset. | [
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|
LeoLM/ArcChallenge_de | LeoLM | 2023-08-29T13:32:23Z | 411 | 0 | [
"region:us"
]
| null | 2023-08-10T22:22:09Z | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
struct:
- name: text
sequence: string
- name: label
sequence: string
- name: answerKey
dtype: string
- name: question_de
dtype: string
- name: choices_de
struct:
- name: label
sequence: string
- name: text
sequence: string
- name: translation_de
dtype: string
splits:
- name: test
num_bytes: 1170655
num_examples: 1172
- name: validation
num_bytes: 301790
num_examples: 299
download_size: 807450
dataset_size: 1472445
---
# Dataset Card for "arc_challenge_de"
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|
lst20 | null | 2023-01-25T14:34:28Z | 410 | 2 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:th",
"license:other",
"word-segmentation",
"clause-segmentation",
"sentence-segmentation",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
pretty_name: LST20
tags:
- word-segmentation
- clause-segmentation
- sentence-segmentation
dataset_info:
features:
- name: id
dtype: string
- name: fname
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': NN
'1': VV
'2': PU
'3': CC
'4': PS
'5': AX
'6': AV
'7': FX
'8': NU
'9': AJ
'10': CL
'11': PR
'12': NG
'13': PA
'14': XX
'15': IJ
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B_BRN
'2': B_DES
'3': B_DTM
'4': B_LOC
'5': B_MEA
'6': B_NUM
'7': B_ORG
'8': B_PER
'9': B_TRM
'10': B_TTL
'11': I_BRN
'12': I_DES
'13': I_DTM
'14': I_LOC
'15': I_MEA
'16': I_NUM
'17': I_ORG
'18': I_PER
'19': I_TRM
'20': I_TTL
'21': E_BRN
'22': E_DES
'23': E_DTM
'24': E_LOC
'25': E_MEA
'26': E_NUM
'27': E_ORG
'28': E_PER
'29': E_TRM
'30': E_TTL
- name: clause_tags
sequence:
class_label:
names:
'0': O
'1': B_CLS
'2': I_CLS
'3': E_CLS
config_name: lst20
splits:
- name: train
num_bytes: 107725145
num_examples: 63310
- name: validation
num_bytes: 9646167
num_examples: 5620
- name: test
num_bytes: 8217425
num_examples: 5250
download_size: 0
dataset_size: 125588737
---
# Dataset Card for LST20
## 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://aiforthai.in.th/
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [email]([email protected])
### Dataset Summary
LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with
16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is
considered large enough for developing joint neural models for NLP.
Manually download at https://aiforthai.in.th/corpus.php
See `LST20 Annotation Guideline.pdf` and `LST20 Brief Specification.pdf` within the downloaded `AIFORTHAI-LST20Corpus.tar.gz` for more details.
### Supported Tasks and Leaderboards
- POS tagging
- NER tagging
- clause segmentation
- sentence segmentation
- word tokenization
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'clause_tags': [1, 2, 2, 2, 2, 2, 2, 2, 3], 'fname': 'T11964.txt', 'id': '0', 'ner_tags': [8, 0, 0, 0, 0, 0, 0, 0, 25], 'pos_tags': [0, 0, 0, 1, 0, 8, 8, 8, 0], 'tokens': ['ธรรมนูญ', 'แชมป์', 'สิงห์คลาสสิก', 'กวาด', 'รางวัล', 'แสน', 'สี่', 'หมื่น', 'บาท']}
{'clause_tags': [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3], 'fname': 'T11964.txt', 'id': '1', 'ner_tags': [8, 18, 28, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 15, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6], 'pos_tags': [0, 2, 0, 2, 1, 1, 2, 8, 2, 10, 2, 8, 2, 1, 0, 1, 0, 4, 7, 1, 0, 2, 8, 2, 10, 1, 10, 4, 2, 8, 2, 4, 0, 4, 0, 2, 8, 2, 10, 2, 8], 'tokens': ['ธรรมนูญ', '_', 'ศรีโรจน์', '_', 'เก็บ', 'เพิ่ม', '_', '4', '_', 'อันเดอร์พาร์', '_', '68', '_', 'เข้า', 'ป้าย', 'รับ', 'แชมป์', 'ใน', 'การ', 'เล่น', 'อาชีพ', '_', '19', '_', 'ปี', 'เป็น', 'ครั้ง', 'ที่', '_', '8', '_', 'ใน', 'ชีวิต', 'ด้วย', 'สกอร์', '_', '18', '_', 'อันเดอร์พาร์', '_', '270']}
```
### Data Fields
- `id`: nth sentence in each set, starting at 0
- `fname`: text file from which the sentence comes from
- `tokens`: word tokens
- `pos_tags`: POS tags
- `ner_tags`: NER tags
- `clause_tags`: clause tags
### Data Splits
| | train | eval | test | all |
|----------------------|-----------|-------------|-------------|-----------|
| words | 2,714,848 | 240,891 | 207,295 | 3,163,034 |
| named entities | 246,529 | 23,176 | 18,315 | 288,020 |
| clauses | 214,645 | 17,486 | 16,050 | 246,181 |
| sentences | 63,310 | 5,620 | 5,250 | 74,180 |
| distinct words | 42,091 | (oov) 2,595 | (oov) 2,006 | 46,692 |
| breaking spaces※ | 63,310 | 5,620 | 5,250 | 74,180 |
| non-breaking spaces※※| 402,380 | 39,920 | 32,204 | 475,504 |
※ Breaking space = space that is used as a sentence boundary marker
※※ Non-breaking space = space that is not used as a sentence boundary marker
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Respective authors of the news articles
### Annotations
#### Annotation process
Detailed annotation guideline can be found in `LST20 Annotation Guideline.pdf`.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
All texts are from public news. No personal and sensitive information is expected to be included.
## Considerations for Using the Data
### Social Impact of Dataset
- Large-scale Thai NER & POS tagging, clause & sentence segmentatation, word tokenization
### Discussion of Biases
- All 3,745 texts are from news domain:
- politics: 841
- crime and accident: 592
- economics: 512
- entertainment: 472
- sports: 402
- international: 279
- science, technology and education: 216
- health: 92
- general: 75
- royal: 54
- disaster: 52
- development: 45
- environment: 40
- culture: 40
- weather forecast: 33
- Word tokenization is done accoding to InterBEST 2009 Guideline.
### Other Known Limitations
- Some NER tags do not correspond with given labels (`B`, `I`, and so on)
## Additional Information
### Dataset Curators
[NECTEC](https://www.nectec.or.th/en/)
### Licensing Information
1. Non-commercial use, research, and open source
Any non-commercial use of the dataset for research and open-sourced projects is encouraged and free of charge. Please cite our technical report for reference.
If you want to perpetuate your models trained on our dataset and share them to the research community in Thailand, please send your models, code, and APIs to the AI for Thai Project. Please contact Dr. Thepchai Supnithi via [email protected] for more information.
Note that modification and redistribution of the dataset by any means are strictly prohibited unless authorized by the corpus authors.
2. Commercial use
In any commercial use of the dataset, there are two options.
- Option 1 (in kind): Contributing a dataset of 50,000 words completely annotated with our annotation scheme within 1 year. Your data will also be shared and recognized as a dataset co-creator in the research community in Thailand.
- Option 2 (in cash): Purchasing a lifetime license for the entire dataset is required. The purchased rights of use cover only this dataset.
In both options, please contact Dr. Thepchai Supnithi via [email protected] for more information.
### Citation Information
```
@article{boonkwan2020annotation,
title={The Annotation Guideline of LST20 Corpus},
author={Boonkwan, Prachya and Luantangsrisuk, Vorapon and Phaholphinyo, Sitthaa and Kriengket, Kanyanat and Leenoi, Dhanon and Phrombut, Charun and Boriboon, Monthika and Kosawat, Krit and Supnithi, Thepchai},
journal={arXiv preprint arXiv:2008.05055},
year={2020}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | [
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|
ubuntu_dialogs_corpus | null | 2023-04-05T13:42:49Z | 410 | 15 | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1506.08909",
"region:us"
]
| [
"conversational"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: UDC (Ubuntu Dialogue Corpus)
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- conversational
task_ids:
- dialogue-generation
paperswithcode_id: ubuntu-dialogue-corpus
dataset_info:
- config_name: train
features:
- name: Context
dtype: string
- name: Utterance
dtype: string
- name: Label
dtype: int32
splits:
- name: train
num_bytes: 525126729
num_examples: 1000000
download_size: 0
dataset_size: 525126729
- config_name: dev_test
features:
- name: Context
dtype: string
- name: Ground Truth Utterance
dtype: string
- name: Distractor_0
dtype: string
- name: Distractor_1
dtype: string
- name: Distractor_2
dtype: string
- name: Distractor_3
dtype: string
- name: Distractor_4
dtype: string
- name: Distractor_5
dtype: string
- name: Distractor_6
dtype: string
- name: Distractor_7
dtype: string
- name: Distractor_8
dtype: string
splits:
- name: test
num_bytes: 27060502
num_examples: 18920
- name: validation
num_bytes: 27663181
num_examples: 19560
download_size: 0
dataset_size: 54723683
---
# Dataset Card for "ubuntu_dialogs_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
- **Repository:** https://github.com/rkadlec/ubuntu-ranking-dataset-creator
- **Paper:** [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://arxiv.org/abs/1506.08909)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 65.49 MB
- **Total amount of disk used:** 65.49 MB
### Dataset Summary
Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### train
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 65.49 MB
- **Total amount of disk used:** 65.49 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"Context": "\"i think we could import the old comment via rsync , but from there we need to go via email . i think it be easier than cach the...",
"Label": 1,
"Utterance": "basic each xfree86 upload will not forc user to upgrad 100mb of font for noth __eou__ no someth i do in my spare time . __eou__"
}
```
### Data Fields
The data fields are the same among all splits.
#### train
- `Context`: a `string` feature.
- `Utterance`: a `string` feature.
- `Label`: a `int32` feature.
### Data Splits
|name |train |
|-----|-----:|
|train|127422|
## 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
```
@article{DBLP:journals/corr/LowePSP15,
author = {Ryan Lowe and
Nissan Pow and
Iulian Serban and
Joelle Pineau},
title = {The Ubuntu Dialogue Corpus: {A} Large Dataset for Research in Unstructured
Multi-Turn Dialogue Systems},
journal = {CoRR},
volume = {abs/1506.08909},
year = {2015},
url = {http://arxiv.org/abs/1506.08909},
archivePrefix = {arXiv},
eprint = {1506.08909},
timestamp = {Mon, 13 Aug 2018 16:48:23 +0200},
biburl = {https://dblp.org/rec/journals/corr/LowePSP15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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|
GroNLP/ik-nlp-22_slp | GroNLP | 2023-02-01T18:25:21Z | 410 | 0 | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-retrieval",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"question-generation",
"region:us"
]
| [
"question-answering",
"summarization",
"text-retrieval"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
- summarization
- text-retrieval
pretty_name: slp3ed-iknlp2022
tags:
- question-generation
---
# Dataset Card for IK-NLP-22 Speech and Language Processing
## Table of Contents
- [Dataset Card for IK-NLP-22 Speech and Language Processing](#dataset-card-for-ik-nlp-22-speech-and-language-processing)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Projects](#projects)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Paragraphs Configuration](#paragraphs-configuration)
- [Questions Configuration](#questions-configuration)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Source:** [Stanford](https://web.stanford.edu/~jurafsky/slp3/)
- **Point of Contact:** [Gabriele Sarti](mmailto:[email protected])
### Dataset Summary
This dataset contains chapters extracted from the Speech and Language Processing book (3ed draft of January 2022) by Jurafsky and Martin via a semi-automatic procedure (see below for additional details). Moreover, a small set of conceptual questions associated with each chapter is provided alongside possible answers.
Only the content of chapters 2 to 11 of the book draft are provided, since these are the ones relevant to the contents of 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) with the assistance of [Gabriele Sarti](https://research.rug.nl/en/persons/gabriele-sarti).
*The Speech and Language Processing book was made freely available by the authors [Dan Jurafsky](http://web.stanford.edu/people/jurafsky/) and [James H. Martin](http://www.cs.colorado.edu/~martin/) on the [Stanford University website](https://web.stanford.edu/~jurafsky/slp3/). The present dataset was created for educational purposes, and is based on the draft of the 3rd edition of the book accessed on December 29th, 2021. All rights of the present contents are attributed to the original authors.*
### Projects
See the course page for a description of possible research directions.
### Languages
The language data of Speech and Language Processing is in English (BCP-47 `en`)
## Dataset Structure
### Data Instances
The dataset contains two configurations: `paragraphs` (default), containing the full set of parsed paragraphs associated to the respective chapter and sections, and `questions`, containing a small subset of example questions matched with the relevant paragraph, and with the answer span extracted.
#### Paragraphs Configuration
The `paragraphs` configuration contains all the paragraphs of the selected book chapters, each associated with the respective chapter, section and subsection. An example from the `train` split of the `paragraphs` config is provided below. The example belongs to section 2.3 but not to a subsection, so the `n_subsection` and `subsection` fields are empty strings.
```json
{
"n_chapter": "2",
"chapter": "Regular Expressions",
"n_section": "2.3",
"section": "Corpora",
"n_subsection": "",
"subsection": "",
"text": "It's also quite common for speakers or writers to use multiple languages in a single communicative act, a phenomenon called code switching. Code switching (2.2) Por primera vez veo a @username actually being hateful! it was beautiful:)"
}
```
The text is provided as-is, without further preprocessing or tokenization.
#### Questions Configuration
The `questions` configuration contains a small subset of questions, the top retrieved paragraph relevant to the question and the answer spans. An example from the `test` split of the `questions` config is provided below.
```json
{
"chapter": "Regular Expressions",
"section": "Regular Expressions",
"subsection": "Basic Regular Expressions",
"question": "What is the meaning of the Kleene star in Regex?",
"paragraph": "This language consists of strings with a b, followed by at least two a's, followed by an exclamation point. The set of operators that allows us to say things like \"some number of as\" are based on the asterisk or *, commonly called the Kleene * (gen-Kleene * erally pronounced \"cleany star\"). The Kleene star means \"zero or more occurrences of the immediately previous character or regular expression\". So /a*/ means \"any string of zero or more as\". This will match a or aaaaaa, but it will also match Off Minor since the string Off Minor has zero a's. So the regular expression for matching one or more a is /aa*/, meaning one a followed by zero or more as. More complex patterns can also be repeated. So /[ab]*/ means \"zero or more a's or b's\" (not \"zero or more right square braces\"). This will match strings like aaaa or ababab or bbbb.",
"answer": "The Kleene star means \"zero or more occurrences of the immediately previous character or regular expression\""
}
```
### Data Splits
| config| train| test|
|------------:|-----:|----:|
|`paragraphs` | 1697 | - |
|`questions` | - | 59 |
### Dataset Creation
The contents of the Speech and Language Processing book PDF were extracted using the [PDF to S2ORC JSON Converter](https://github.com/allenai/s2orc-doc2json) by AllenAI. The texts extracted by the converter were then manually cleaned to remove end-of-chapter exercises and other irrelevant content (e.g. tables, TikZ figures, etc.). Some issues in the parsed content were preserved in the final version to maintain a naturalistic setting for the associated projects, promoting the use of data filtering heuristics for students.
The question-answer pairs were created manually by Gabriele Sarti.
## Additional Information
### Dataset Curators
For problems on this 🤗 Datasets version, please contact us at [[email protected]](mailto:[email protected]).
### Licensing Information
Please refer to the authors' websites for licensing information.
### Citation Information
Please cite the authors if you use these corpora in your work:
```bibtex
@book{slp3ed-iknlp2022,
author = {Jurafsky, Daniel and Martin, James},
year = {2021},
month = {12},
pages = {1--235, 1--19},
title = {Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition},
volume = {3}
}
``` | [
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|
joelniklaus/lextreme | joelniklaus | 2023-04-29T07:02:17Z | 410 | 16 | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:topic-classification",
"task_ids:named-entity-recognition",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:extended",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:cc-by-4.0",
"arxiv:2301.13126",
"region:us"
]
| [
"text-classification",
"token-classification"
]
| 2022-08-01T08:41:55Z | ---
annotations_creators:
- other
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: "LEXTREME: A Multilingual Legal Benchmark for Natural Language Understanding"
size_categories:
- 10K<n<100K
source_datasets:
- extended
task_categories:
- text-classification
- token-classification
task_ids:
- multi-class-classification
- multi-label-classification
- topic-classification
- named-entity-recognition
---
# Dataset Card for LEXTREME: A Multilingual Legal Benchmark for Natural Language Understanding
## 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:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:[email protected])
### Dataset Summary
The dataset consists of 11 diverse multilingual legal NLU datasets. 6 datasets have one single configuration and 5 datasets have two or three configurations. This leads to a total of 18 tasks (8 single-label text classification tasks, 5 multi-label text classification tasks and 5 token-classification tasks).
Use the dataset like this:
```python
from datasets import load_dataset
dataset = load_dataset("joelito/lextreme", "swiss_judgment_prediction")
```
### Supported Tasks and Leaderboards
The dataset supports the tasks of text classification and token classification.
In detail, we support the folliwing tasks and configurations:
| task | task type | configurations | link |
|:---------------------------|--------------------------:|---------------------------------:|-------------------------------------------------------------------------------------------------------:|
| Brazilian Court Decisions | Judgment Prediction | (judgment, unanimity) | [joelito/brazilian_court_decisions](https://huggingface.co/datasets/joelito/brazilian_court_decisions) |
| Swiss Judgment Prediction | Judgment Prediction | default | [joelito/swiss_judgment_prediction](https://huggingface.co/datasets/swiss_judgment_prediction) |
| German Argument Mining | Argument Mining | default | [joelito/german_argument_mining](https://huggingface.co/datasets/joelito/german_argument_mining) |
| Greek Legal Code | Topic Classification | (volume, chapter, subject) | [greek_legal_code](https://huggingface.co/datasets/greek_legal_code) |
| Online Terms of Service | Unfairness Classification | (unfairness level, clause topic) | [online_terms_of_service](https://huggingface.co/datasets/joelito/online_terms_of_service) |
| Covid 19 Emergency Event | Event Classification | default | [covid19_emergency_event](https://huggingface.co/datasets/joelito/covid19_emergency_event) |
| MultiEURLEX | Topic Classification | (level 1, level 2, level 3) | [multi_eurlex](https://huggingface.co/datasets/multi_eurlex) |
| LeNER BR | Named Entity Recognition | default | [lener_br](https://huggingface.co/datasets/lener_br) |
| LegalNERo | Named Entity Recognition | default | [legalnero](https://huggingface.co/datasets/joelito/legalnero) |
| Greek Legal NER | Named Entity Recognition | default | [greek_legal_ner](https://huggingface.co/datasets/joelito/greek_legal_ner) |
| MAPA | Named Entity Recognition | (coarse, fine) | [mapa](https://huggingface.co/datasets/joelito/mapa) |
### Languages
The following languages are supported: bg , cs , da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
## Dataset Structure
### Data Instances
The file format is jsonl and three data splits are present for each configuration (train, validation and test).
### 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
How can I contribute a dataset to lextreme?
Please follow the following steps:
1. Make sure your dataset is available on the huggingface hub and has a train, validation and test split.
2. Create a pull request to the lextreme repository by adding the following to the lextreme.py file:
- Create a dict _{YOUR_DATASET_NAME} (similar to _BRAZILIAN_COURT_DECISIONS_JUDGMENT) containing all the necessary information about your dataset (task_type, input_col, label_col, etc.)
- Add your dataset to the BUILDER_CONFIGS list: `LextremeConfig(name="{your_dataset_name}", **_{YOUR_DATASET_NAME})`
- Test that it works correctly by loading your subset with `load_dataset("lextreme", "{your_dataset_name}")` and inspecting a few examples.
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{niklaus2023lextreme,
title={LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain},
author={Joel Niklaus and Veton Matoshi and Pooja Rani and Andrea Galassi and Matthias Stürmer and Ilias Chalkidis},
year={2023},
eprint={2301.13126},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
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|
HuggingFaceH4/hh-rlhf-h4 | HuggingFaceH4 | 2023-11-29T16:33:41Z | 410 | 1 | [
"region:us"
]
| null | 2023-11-29T16:25:52Z | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 472329144
num_examples: 160800
- name: test
num_bytes: 25348918
num_examples: 8552
download_size: 0
dataset_size: 497678062
---
# Dataset Card for "hh-rlhf-h4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
irc_disentangle | null | 2022-11-18T20:10:09Z | 409 | 4 | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"conversation-disentanglement",
"arxiv:1810.11118",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
paperswithcode_id: irc-disentanglement
pretty_name: IRC Disentanglement
tags:
- conversation-disentanglement
dataset_info:
- config_name: ubuntu
features:
- name: id
dtype: int32
- name: raw
dtype: string
- name: ascii
dtype: string
- name: tokenized
dtype: string
- name: date
dtype: string
- name: connections
sequence: int32
splits:
- name: train
num_bytes: 56012854
num_examples: 220616
- name: validation
num_bytes: 3081479
num_examples: 12510
- name: test
num_bytes: 3919900
num_examples: 15010
download_size: 118470210
dataset_size: 63014233
- config_name: channel_two
features:
- name: id
dtype: int32
- name: raw
dtype: string
- name: ascii
dtype: string
- name: tokenized
dtype: string
- name: connections
sequence: int32
splits:
- name: dev
num_bytes: 197505
num_examples: 1001
- name: pilot
num_bytes: 92663
num_examples: 501
- name: test
num_bytes: 186823
num_examples: 1001
- name: pilot_dev
num_bytes: 290175
num_examples: 1501
- name: all_
num_bytes: 496524
num_examples: 2602
download_size: 118470210
dataset_size: 1263690
---
# Dataset Card for IRC Disentanglement
## 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)
- [Acknowledgments](#acknowledgments)
## Dataset Description
- **Homepage:** https://jkk.name/irc-disentanglement/
- **Repository:** https://github.com/jkkummerfeld/irc-disentanglement/tree/master/data
- **Paper:** https://aclanthology.org/P19-1374/
- **Leaderboard:** NA
- **Point of Contact:** [email protected]
### Dataset Summary
Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. This new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. The dataset is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context.
Note, the Github repository for the dataset also contains several useful tools for:
- Conversion (e.g. extracting conversations from graphs)
- Evaluation
- Preprocessing
- Word embeddings trained on the full Ubuntu logs in 2018
### Supported Tasks and Leaderboards
Conversational Disentanglement
### Languages
English (en)
## Dataset Structure
### Data Instances
For Ubuntu:
data["train"][1050]
```
{
'ascii': "[03:57] <Xophe> (also, I'm guessing that this isn't a good place to report minor but annoying bugs... what is?)",
'connections': [1048, 1054, 1055, 1072, 1073],
'date': '2004-12-25',
'id': 1050,
'raw': "[03:57] <Xophe> (also, I'm guessing that this isn't a good place to report minor but annoying bugs... what is?)",
'tokenized': "<s> ( also , i 'm guessing that this is n't a good place to report minor but annoying bugs ... what is ?) </s>"
}
```
For Channel_two:
data["train"][50]
```
{
'ascii': "[01:04] <Felicia> Chanel: i don't know off hand sorry",
'connections': [49, 53],
'id': 50,
'raw': "[01:04] <Felicia> Chanel: i don't know off hand sorry",
'tokenized': "<s> <user> : i do n't know off hand sorry </s>"
}
```
### Data Fields
'id' : The id of the message, this is the value that would be in the 'connections' of associated messages.
'raw' : The original message from the IRC log, as downloaded.
'ascii' : The raw message converted to ascii (unconvertable characters are replaced with a special word).
'tokenized' : The same message with automatic tokenisation and replacement of rare words with placeholder symbols.
'connections' : The indices of linked messages.
(only ubuntu) 'date' : The date the messages are from. The labelling for each date only start after the first 1000 messages of that date.
### Data Splits
The dataset has 4 parts:
| Part | Number of Annotated Messages |
| ------------- | ------------------------------------------- |
| Train | 67,463 |
| Dev | 2,500 |
| Test | 5,000 |
| Channel 2 | 2,600 |
## Dataset Creation
### Curation Rationale
IRC is a synchronous chat setting with a long history of use.
Several channels log all messages and make them publicly available.
The Ubuntu channel is particularly heavily used and has been the subject of several academic studies.
Data was selected from the channel in order to capture the diversity of situations in the channel (e.g. when there are many users or very few users).
For full details, see the [annotation information page](https://github.com/jkkummerfeld/irc-disentanglement/blob/master/data/READ.history.md).
### Source Data
#### Initial Data Collection and Normalization
Data was collected from the Ubuntu IRC channel logs, which are publicly available at [https://irclogs.ubuntu.com/](https://irclogs.ubuntu.com/).
The raw files are included, as well as two other versions:
- ASCII, converted using the script [make_txt.py](https://github.com/jkkummerfeld/irc-disentanglement/blob/master/tools/preprocessing/make-txt.py)
- Tok, tokenised text with rare words replaced by UNK using the script [dstc8-tokenise.py](https://github.com/jkkummerfeld/irc-disentanglement/blob/master/tools/preprocessing/dstc8-tokenise.py)
The raw channel two data is from prior work [(Elsner and Charniak, 2008)](https://www.aclweb.org/anthology/P08-1095.pdf)].
#### Who are the source language producers?
The text is from a large group of internet users asking questions and providing answers related to Ubuntu.
### Annotations
#### Annotation process
The data is expert annotated with:
- Training, one annotation per line in general, a small portion is double-annotated and adjudicated
- Dev, Channel 2, double annotated and adjudicated
- Test, triple annotated and adjudicated
| Part | Annotators | Adjudication? |
| ------------- | --------------- | ------------------------------------- |
| Train | 1 or 2 per file | For files with 2 annotators (only 10) |
| Dev | 2 | Yes |
| Test | 3 | Yes |
| Channel 2 | 2 | Yes |
#### Who are the annotators?
Students and a postdoc at the University of Michigan.
Everyone involved went through a training process with feedback to learn the annotation guidelines.
### Personal and Sensitive Information
No content is removed or obfuscated.
There is probably personal information in the dataset from users.
## Considerations for Using the Data
### Social Impact of Dataset
The raw data is already available online and the annotations do not significantly provide additional information that could have a direct social impact.
### Discussion of Biases
The data is mainly from a single technical domain (Ubuntu tech support) that probably has a demographic skew of some sort.
Given that users are only identified by their self-selected usernames, it is difficult to know more about the authors.
### Other Known Limitations
Being focused on a single language and a single channel means that the data is likely capturing a particular set of conventions in communication.
Those conventions may not apply to other channels, or beyond IRC.
## Additional Information
### Dataset Curators
Jonathan K. Kummerfeld
### Licensing Information
Creative Commons Attribution 4.0
### Citation Information
```
@inproceedings{kummerfeld-etal-2019-large,
title = "A Large-Scale Corpus for Conversation Disentanglement",
author = "Kummerfeld, Jonathan K. and
Gouravajhala, Sai R. and
Peper, Joseph J. and
Athreya, Vignesh and
Gunasekara, Chulaka and
Ganhotra, Jatin and
Patel, Siva Sankalp and
Polymenakos, Lazaros C and
Lasecki, Walter",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1374",
doi = "10.18653/v1/P19-1374",
pages = "3846--3856",
arxiv = "https://arxiv.org/abs/1810.11118",
software = "https://jkk.name/irc-disentanglement",
data = "https://jkk.name/irc-disentanglement",
abstract = "Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our data is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 89{\%} of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.",
}
```
### Contributions
Thanks to [@dhruvjoshi1998](https://github.com/dhruvjoshi1998) for adding this dataset.
Thanks to [@jkkummerfeld](https://github.com/jkkummerfeld) for improvements to the documentation.
### Acknowledgments
This material is based in part upon work supported by IBM under contract 4915012629. Any opinions, findings, conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of IBM. | [
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|
GEM/turku_hockey_data2text | GEM | 2022-10-24T15:30:33Z | 409 | 0 | [
"task_categories:table-to-text",
"annotations_creators:expert-created",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:fi",
"license:cc-by-nc-sa-4.0",
"data-to-text",
"region:us"
]
| [
"table-to-text"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-created
language_creators:
- unknown
language:
- fi
license:
- cc-by-nc-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: turku_hockey_data2text
tags:
- data-to-text
---
# Dataset Card for GEM/turku_hockey_data2text
## Dataset Description
- **Homepage:** https://turkunlp.org/hockey_data2text.html
- **Repository:** https://github.com/TurkuNLP/Turku-hockey-data2text
- **Paper:** https://aclanthology.org/W19-6125/
- **Leaderboard:** N/A
- **Point of Contact:** Jenna Kanerva, Filip Ginter
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/turku_hockey_data2text).
### Dataset Summary
This is a Finnish data-to-text dataset in which the input is structured information about a hockey game and the output a description of the game.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/turku_hockey_data2text')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/turku_hockey_data2text).
#### website
[Website](https://turkunlp.org/hockey_data2text.html)
#### paper
[ACL anthology](https://aclanthology.org/W19-6125/)
#### authors
Jenna Kanerva, Samuel Rönnqvist, Riina Kekki, Tapio Salakoski, Filip Ginter (TurkuNLP / University of Turku)
## 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://turkunlp.org/hockey_data2text.html)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/TurkuNLP/Turku-hockey-data2text)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL anthology](https://aclanthology.org/W19-6125/)
#### 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{kanerva2019newsgen,
Title = {Template-free Data-to-Text Generation of Finnish Sports News},
Author = {Jenna Kanerva and Samuel R{\"o}nnqvist and Riina Kekki and Tapio Salakoski and Filip Ginter},
booktitle = {Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa’19)},
year={2019}
}
```
#### 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 -->
Jenna Kanerva, Filip Ginter
#### 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]
#### 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 -->
written standard language
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Finnish`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
The original news articles are written by professional journalists. The text passages extracted in the annotation may be slightly edited compared to the original language during the corpus annotation.
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-nc-sa-4.0: Creative Commons Attribution Non Commercial Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
This dataset was developed as a benchmark for evaluating template-free, machine learning methods on Finnish news generation in the area of ice hockey reporting.
#### 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 an event from an ice hockey game based on the given structural data.
### 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 -->
University of Turku
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Jenna Kanerva, Samuel Rönnqvist, Riina Kekki, Tapio Salakoski, Filip Ginter (TurkuNLP / University of Turku)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
The project was supported by the Google Digital News Innovation Fund.
#### 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 -->
Jenna Kanerva, Filip Ginter (TurkuNLP / University of Turku)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
The dataset is constructed of games, where each game is a list of events. If the event was annotated (corresponding sentence was found from the news article), it includes `text` field with value other than empty string ("").
For each game (dict), there are keys `gem_id` (string), `id` (string), `news_article` (string), and `events` (list).
For each event (dict), there are different, relevant keys available with non empty values depending on the event type (e.g. goal or penalty). The mandatory keys for each event are `event_id` (string), `event_type` (string), `text` (string, empty string if not annotated), and `multi_reference` (bool). The keys not relevant for the specific event type are left empty.
The relevant keys in the event dictionary are:
For each event type, the following keys are relevant:
`event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
`event_type`: Type of the event, possible values are `game result`, `goal`, `penalty`, or `saves` (string)
`text`: Natural language description of the event, or empty string if not available (string)
`multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
The rest of the fields are specific to the event type. The relevant fields for each event type are:
game result:
`event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
`event_type`: Type of the event (string)
`home_team`: Name of the home team (string)
`guest_team`: Name of the guest team (string)
`score`: Final score of the game, in the form of home–guest (string)
`periods`: Scores for individual periods, each in the form of home–guest score in that period (list of strings)
`features`: Additional features, such as overtime win or shoot out (list of strings)
`text`: Natural language description of the event, or empty string if not available (string)
`multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
goal:
`event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
`event_type`: Type of the event (string)
`player`: Name of the player scoring (string)
`assist`: Names of the players assisting, at most two players (list of strings)
`team`: Team scoring with possible values of `home` or `guest` (string)
`team_name`: Name of the team scoring (string)
`score`: Score after the goal, in the form of home–guest (string)
`time`: Time of the goal, minutes and seconds from the beginning (string)
`features`: Additional features, such as power play or short-handed goal (list of strings)
`text`: Natural language description of the event, or empty string if not available (string)
`multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
penalty:
`event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
`event_type`: Type of the event (string)
`player`: Name of the player getting the penalty (string)
`team`: Team getting the penalty with possible values of `home` or `guest` (string)
`team_name`: Name of the team getting the penalty (string)
`penalty_minutes`: Penalty minutes (string)
`time`: Time of the penalty, minutes and seconds from the beginning (string)
`text`: Natural language description of the event, or empty string if not available (string)
`multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
saves:
`event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
`event_type`: Type of the event (string)
`player`: Name of the goalkeeper (string)
`team`: Team of the goalkeeper with possible values of `home` or `guest` (string)
`team_name`: Name of the team (string)
`saves`: Number of saves in the game (string)
`text`: Natural language description of the event, or empty string if not available (string)
`multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
Text passages describing multiple events (multi_reference):
Some text passages refer to multiple events in such way that separating them to individual statements is not adequate (e.g. "The home team received two penalties towards the end of the first period."). In these cases, multiple events are aligned to the same text passage so that the first event (in chronological order) include the annotated text passage, while the rest of the events referring to the same text passage include the identifier of the first event in the annotated text field (e.g. `text`: "E4").
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
'gem_id': 'gem-turku_hockey_data2text-train-0',
'id': '20061031-TPS-HPK',
'news_article': 'HPK:n hyvä syysvire jatkuu jääkiekon SM-liigassa. Tiistaina HPK kukisti mainiolla liikkeellä ja tehokkaalla ylivoimapelillä TPS:n vieraissa 1–0 (1–0, 0–0, 0–0).\nHPK hyödynsi ylivoimaa mennen jo ensimmäisessä erässä Mikko Mäenpään maalilla 1–0 -johtoon.\nToisessa ja kolmannessa erässä HPK tarjosi edelleen TPS:lle runsaasti tilanteita, mutta maalia eivät turkulaiset millään ilveellä saaneet. Pahin este oli loistavan pelin Hämeenlinnan maalilla pelannut Mika Oksa.\nTPS:n maalissa Jani Hurme ei osumille mitään mahtanut. Joukkueen suuri yksinäinen kenttäpelaaja oli Kai Nurminen, mutta hänelläkään ei ollut onnea maalitilanteissa.',
'events':
{
'event_id': ['E1', 'E2', 'E3'],
'event_type': ['game result', 'penalty', 'goal'],
'text': ['HPK kukisti TPS:n vieraissa 1–0 (1–0, 0–0, 0–0).', '', 'HPK hyödynsi ylivoimaa mennen jo ensimmäisessä erässä Mikko Mäenpään maalilla 1–0 -johtoon.'],
'home_team': ['TPS', '', ''],
'guest_team': ['HPK', '', ''],
'score': ['0–1', '', '0–1'],
'periods': [['0–1', '0–0', '0–0'], [], []],
'features': [[], [], ['power play']],
'player': ['', 'Fredrik Svensson', 'Mikko Mäenpää'],
'assist': [[], [], ['Jani Keinänen', 'Toni Mäkiaho']],
'team': ['', 'guest', 'guest'],
'team_name': ['', 'HPK', 'HPK'],
'time': ['', '9.28', '14.57'],
'penalty_minutes': ['', '2', ''],
'saves': ['', '', ''],
'multi_reference': [false, false, false]
}
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
The corpus include 3 splits: train, 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 -->
The dataset was created to develop machine learned text generation models for Finnish ice hockey news, where the generation would reflect the natural language variation found from the game reports written by professional journalists. While the original game reports often include additional information not derivable from the game statistics, the corpus was fully manually curated to remove all such information from the natural language descriptions. The rationale of such curation was to prevent model 'hallucinating' additional facts.
#### 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 -->
This is the only data2text corpus for Finnish in GEM.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
morphological inflection, language variation
### 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 -->
`data points modified`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
Structural data was translated into English.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `METEOR`, `ROUGE`, `WER`
#### 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: BLEU, NIST, METEOR, ROUGE-L, CIDEr
Manual evaluation: factual mistakes, grammatical errors, minimum edit distance to an acceptable game report (using WER)
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The dataset is designed for text generation (data2text), where the original source of natural language descriptions is news articles written by journalists. While the link between structural data (ice hockey game statistics) and the news articles describing the game was quite weak (news articles including a lot of information not derivable from the statistics, while leaving many events unmentioned), the corpus includes full manual annotation aligning the events extracted from game statistics and the corresponding natural language passages extracted from the news articles.
Each event is manually aligned into a sentence-like passage, and in case a suitable passage was not found, the annotation is left empty (with value `None`). The extracted passages were manually modified not to include additional information not derivable from the game statistics, or not considered as world knowledge. The manual curation of passages is designed to prevent model hallucination, i.e. model learning to generate facts not derivable from the input data.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Describing the given events (structural data) in natural language, and therefore generating ice hockey game reports.
#### 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`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The initial data, both game statistics and news articles, were obtained from the Finnish News Agency STT news archives released for academic use (http://urn.fi/urn:nbn:fi:lb-2019041501). The original news articles are written by professional journalists.
We (TurkuNLP) gratefully acknowledge the collaboration of Maija Paikkala, Salla Salmela and Pihla Lehmusjoki from the Finnish News Agency STT while creating the corpus.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
Ice hockey, news
#### 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 -->
Include only games, where both game statistics and a news article describing the game were available (based on timestamps and team names).
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
expert created
#### Number of Raters
<!-- info: What is the number of raters -->
<!-- scope: telescope -->
1
#### Rater Qualifications
<!-- info: Describe the qualifications required of an annotator. -->
<!-- scope: periscope -->
Members of the TurkuNLP research group, native speakers of Finnish.
#### 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 -->
1
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
Manual alignment of events and their natural language descriptions. Removing information not derivable from the input data or world knowledge in order to prevent the model 'hallucination'.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated by data curators
#### Quality Control Details
<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
Manual inspection of examples during the initial annotation training phrase.
### 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 corpus license was agreed with the providers of the source material.
### 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 -->
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 -->
The dataset represents only written standard language.
## 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 -->
`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 -->
`non-commercial use only`
### Known Technical Limitations
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|
open-llm-leaderboard/details_llm-agents__tora-code-7b-v1.0 | open-llm-leaderboard | 2023-10-28T11:51:10Z | 409 | 0 | [
"region:us"
]
| null | 2023-10-10T14:13:09Z | ---
pretty_name: Evaluation run of llm-agents/tora-code-7b-v1.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [llm-agents/tora-code-7b-v1.0](https://huggingface.co/llm-agents/tora-code-7b-v1.0)\
\ 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_llm-agents__tora-code-7b-v1.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T11:50:58.128612](https://huggingface.co/datasets/open-llm-leaderboard/details_llm-agents__tora-code-7b-v1.0/blob/main/results_2023-10-28T11-50-58.128612.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\": 0.0010486577181208054,\n\
\ \"em_stderr\": 0.00033145814652192884,\n \"f1\": 0.04895343959731551,\n\
\ \"f1_stderr\": 0.0011757746481772687,\n \"acc\": 0.33245361192407963,\n\
\ \"acc_stderr\": 0.009816859128324334\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.00033145814652192884,\n\
\ \"f1\": 0.04895343959731551,\n \"f1_stderr\": 0.0011757746481772687\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04927975739196361,\n \
\ \"acc_stderr\": 0.005962150655812473\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6156274664561957,\n \"acc_stderr\": 0.013671567600836194\n\
\ }\n}\n```"
repo_url: https://huggingface.co/llm-agents/tora-code-7b-v1.0
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_10T14_12_45.914011
path:
- '**/details_harness|arc:challenge|25_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_28T11_50_58.128612
path:
- '**/details_harness|drop|3_2023-10-28T11-50-58.128612.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T11-50-58.128612.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T11_50_58.128612
path:
- '**/details_harness|gsm8k|5_2023-10-28T11-50-58.128612.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T11-50-58.128612.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hellaswag|10_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T14-12-45.914011.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T14-12-45.914011.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T14-12-45.914011.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T11_50_58.128612
path:
- '**/details_harness|winogrande|5_2023-10-28T11-50-58.128612.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T11-50-58.128612.parquet'
- config_name: results
data_files:
- split: 2023_10_10T14_12_45.914011
path:
- results_2023-10-10T14-12-45.914011.parquet
- split: 2023_10_28T11_50_58.128612
path:
- results_2023-10-28T11-50-58.128612.parquet
- split: latest
path:
- results_2023-10-28T11-50-58.128612.parquet
---
# Dataset Card for Evaluation run of llm-agents/tora-code-7b-v1.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/llm-agents/tora-code-7b-v1.0
- **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 [llm-agents/tora-code-7b-v1.0](https://huggingface.co/llm-agents/tora-code-7b-v1.0) 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_llm-agents__tora-code-7b-v1.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T11:50:58.128612](https://huggingface.co/datasets/open-llm-leaderboard/details_llm-agents__tora-code-7b-v1.0/blob/main/results_2023-10-28T11-50-58.128612.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": 0.0010486577181208054,
"em_stderr": 0.00033145814652192884,
"f1": 0.04895343959731551,
"f1_stderr": 0.0011757746481772687,
"acc": 0.33245361192407963,
"acc_stderr": 0.009816859128324334
},
"harness|drop|3": {
"em": 0.0010486577181208054,
"em_stderr": 0.00033145814652192884,
"f1": 0.04895343959731551,
"f1_stderr": 0.0011757746481772687
},
"harness|gsm8k|5": {
"acc": 0.04927975739196361,
"acc_stderr": 0.005962150655812473
},
"harness|winogrande|5": {
"acc": 0.6156274664561957,
"acc_stderr": 0.013671567600836194
}
}
```
### 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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|
valurank/News_Articles_Categorization | valurank | 2023-08-27T05:49:31Z | 408 | 0 | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
]
| [
"text-classification"
]
| 2022-05-25T21:46:45Z | ---
license:
- other
language:
- en
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for News_Articles_Categorization
## Table of Contents
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Source Data](#source-data)
## Dataset Description
3722 News Articles classified into different categories namely: World, Politics, Tech, Entertainment, Sport, Business, Health, and Science
## Languages
The text in the dataset is in English
## Dataset Structure
The dataset consists of two columns namely Text and Category.
The Text column consists of the news article and the Category column consists of the class each article belongs to
## Source Data
The dataset is scrapped across different news platforms
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|
indonlp/nusaparagraph_topic | indonlp | 2023-09-07T13:04:43Z | 408 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-09-07T13:01:09Z | ---
license: apache-2.0
---
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|
labr | null | 2023-01-25T14:34:10Z | 407 | 0 | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"license:unknown",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: labr
pretty_name: LABR
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '1'
'1': '2'
'2': '3'
'3': '4'
'4': '5'
config_name: plain_text
splits:
- name: train
num_bytes: 7051103
num_examples: 11760
- name: test
num_bytes: 1703399
num_examples: 2935
download_size: 39953712
dataset_size: 8754502
---
# Dataset Card for LABR
## Table of Contents
- [Dataset Card for LABR](#dataset-card-for-labr)
- [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)
- [|split|num examples|](#splitnum-examples)
- [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)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [LABR](https://github.com/mohamedadaly/LABR)
- **Paper:** [LABR: Large-scale Arabic Book Reviews Dataset](https://aclanthology.org/P13-2088/)
- **Point of Contact:** [Mohammed Aly](mailto:[email protected])
### Dataset Summary
This dataset contains over 63,000 book reviews in Arabic. It is the largest sentiment analysis dataset for Arabic to-date. The book reviews were harvested from the website Goodreads during the month or March 2013. Each book review comes with the goodreads review id, the user id, the book id, the rating (1 to 5) and the text of the review.
### Supported Tasks and Leaderboards
The dataset was published on this [paper](https://www.aclweb.org/anthology/P13-2088.pdf).
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
A typical data point comprises a rating from 1 to 5 where the higher the rating the better the review.
### Data Fields
- `text` (str): Review text.
- `label` (int): Review rating.
### Data Splits
The data is split into a training and testing. The split is organized as the following
| | train | test |
|---------- |-------:|------:|
|data split | 11,760 | 2,935 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
downloaded over 220,000 reviews from the
book readers social network www.goodreads.com
during the month of March 2013
#### Who are the source language producers?
Reviews.
### 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
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{aly2013labr,
title={Labr: A large scale arabic book reviews dataset},
author={Aly, Mohamed and Atiya, Amir},
booktitle={Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages={494--498},
year={2013}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. | [
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|
mwsc | null | 2023-04-05T13:33:22Z | 407 | 0 | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-coreference-resolution",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:extended|winograd_wsc",
"language:en",
"license:cc-by-4.0",
"arxiv:1806.08730",
"region:us"
]
| [
"multiple-choice"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Modified Winograd Schema Challenge (MWSC)
size_categories:
- n<1K
source_datasets:
- extended|winograd_wsc
task_categories:
- multiple-choice
task_ids:
- multiple-choice-coreference-resolution
paperswithcode_id: null
dataset_info:
features:
- name: sentence
dtype: string
- name: question
dtype: string
- name: options
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 11022
num_examples: 80
- name: test
num_bytes: 15220
num_examples: 100
- name: validation
num_bytes: 13109
num_examples: 82
download_size: 19197
dataset_size: 39351
---
# Dataset Card for The modified Winograd Schema Challenge (MWSC)
## 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://decanlp.com](http://decanlp.com)
- **Repository:** https://github.com/salesforce/decaNLP
- **Paper:** [The Natural Language Decathlon: Multitask Learning as Question Answering](https://arxiv.org/abs/1806.08730)
- **Point of Contact:** [Bryan McCann](mailto:[email protected]), [Nitish Shirish Keskar](mailto:[email protected])
- **Size of downloaded dataset files:** 19.20 kB
- **Size of the generated dataset:** 39.35 kB
- **Total amount of disk used:** 58.55 kB
### Dataset Summary
Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.
This Modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 0.02 MB
- **Size of the generated dataset:** 0.04 MB
- **Total amount of disk used:** 0.06 MB
An example looks as follows:
```
{
"sentence": "The city councilmen refused the demonstrators a permit because they feared violence.",
"question": "Who feared violence?",
"options": [ "councilmen", "demonstrators" ],
"answer": "councilmen"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `sentence`: a `string` feature.
- `question`: a `string` feature.
- `options`: a `list` of `string` features.
- `answer`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 80| 82| 100|
## 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
Our code for running decaNLP has been open sourced under BSD-3-Clause.
We chose to restrict decaNLP to datasets that were free and publicly accessible for research, but you should check their individual terms if you deviate from this use case.
From the [Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html):
> Both versions of the collections are licenced under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
### Citation Information
If you use this in your work, please cite:
```
@article{McCann2018decaNLP,
title={The Natural Language Decathlon: Multitask Learning as Question Answering},
author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
journal={arXiv preprint arXiv:1806.08730},
year={2018}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@ghomasHudson](https://github.com/ghomasHudson), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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|
thu-coai/Safety-Prompts | thu-coai | 2023-08-25T15:02:51Z | 407 | 32 | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:apache-2.0",
"arxiv:2304.10436",
"region:us"
]
| [
"text-generation"
]
| 2023-04-19T06:41:55Z | ---
license: apache-2.0
task_categories:
- text-generation
language:
- zh
pretty_name: Safety-Prompts
size_categories:
- 100K<n<1M
---
# Dataset Card for Dataset Name
GitHub Repository: https://github.com/thu-coai/Safety-Prompts
Paper: https://arxiv.org/abs/2304.10436
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|
emozilla/yarn-train-tokenized-8k-llama | emozilla | 2023-11-16T14:50:57Z | 407 | 0 | [
"region:us"
]
| null | 2023-11-16T14:39:08Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 22643813816
num_examples: 212602
download_size: 6260733414
dataset_size: 22643813816
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "yarn-train-tokenized-8k-llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
emotone_ar | null | 2023-01-25T14:29:56Z | 406 | 6 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"license:unknown",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Emotional Tone in Arabic
dataset_info:
features:
- name: tweet
dtype: string
- name: label
dtype:
class_label:
names:
'0': none
'1': anger
'2': joy
'3': sadness
'4': love
'5': sympathy
'6': surprise
'7': fear
splits:
- name: train
num_bytes: 1541746
num_examples: 10065
download_size: 1563138
dataset_size: 1541746
---
# Dataset Card for Emotional Tone in Arabic
## Table of Contents
- [Dataset Card for Emotional Tone in Arabic](#dataset-card-for-emotional-tone-in-arabic)
- [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)
- [|split|num examples|](#splitnum-examples)
- [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)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Repository](https://github.com/AmrMehasseb/Emotional-Tone)
- **Paper:** [Emotional Tone Detection in Arabic Tweets](https://www.researchgate.net/publication/328164296_Emotional_Tone_Detection_in_Arabic_Tweets_18th_International_Conference_CICLing_2017_Budapest_Hungary_April_17-23_2017_Revised_Selected_Papers_Part_II)
- **Point of Contact:** [Amr Al-Khatib](https://github.com/AmrMehasseb)
### Dataset Summary
Dataset of 10065 tweets in Arabic for Emotion detection in Arabic text
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
example:
```
>>> {'label': 0, 'tweet': 'الاوليمبياد الجايه هكون لسه ف الكليه ..'}
```
### Data Fields
- "tweet": plain text tweet in Arabic
- "label": emotion class label
the dataset distribution and balance for each class looks like the following
|label||Label description | Count |
|---------|---------| ------- |
|0 |none | 1550 |
|1 |anger | 1444 |
|2 |joy | 1281 |
|3 |sadness | 1256 |
|4 |love | 1220 |
|5 |sympathy | 1062 |
|6 |surprise | 1045 |
|7 |fear | 1207 |
### Data Splits
The dataset is not split.
| | train |
|----------|--------:|
| no split | 10,065 |
## 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
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inbook{inbook,
author = {Al-Khatib, Amr and El-Beltagy, Samhaa},
year = {2018},
month = {01},
pages = {105-114},
title = {Emotional Tone Detection in Arabic Tweets: 18th International Conference, CICLing 2017, Budapest, Hungary, April 17–23, 2017, Revised Selected Papers, Part II},
isbn = {978-3-319-77115-1},
doi = {10.1007/978-3-319-77116-8_8}
}
```
### Contributions
Thanks to [@abdulelahsm](https://github.com/abdulelahsm) for adding this dataset. | [
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|
kor_hate | null | 2023-01-25T14:33:47Z | 406 | 4 | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ko",
"license:cc-by-sa-4.0",
"arxiv:2005.12503",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: korean-hatespeech-dataset
pretty_name: Korean HateSpeech Dataset
dataset_info:
features:
- name: comments
dtype: string
- name: contain_gender_bias
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: bias
dtype:
class_label:
names:
'0': none
'1': gender
'2': others
- name: hate
dtype:
class_label:
names:
'0': hate
'1': offensive
'2': none
splits:
- name: train
num_bytes: 983608
num_examples: 7896
- name: test
num_bytes: 58913
num_examples: 471
download_size: 968449
dataset_size: 1042521
---
# 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:** [Korean HateSpeech Dataset](https://github.com/kocohub/korean-hate-speech)
- **Repository:** [Korean HateSpeech Dataset](https://github.com/kocohub/korean-hate-speech)
- **Paper:** [BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection](https://arxiv.org/abs/2005.12503)
- **Point of Contact:** [Steven Liu]([email protected])
### Dataset Summary
The Korean HateSpeech Dataset is a dataset of 8367 human-labeled entertainment news comments from a popular Korean news aggregation platform. Each comment was evaluated for either social bias (labels: `gender`, `others` `none`), hate speech (labels: `hate`, `offensive`, `none`) or gender bias (labels: `True`, `False`). The dataset was created to support the identification of toxic comments on online platforms where users can remain anonymous.
### Supported Tasks and Leaderboards
* `multi-label classification`: The dataset can be used to train a model for hate speech detection. A BERT model can be presented with a Korean entertainment news comment and be asked to label whether it contains social bias, gender bias and hate speech. Users can participate in a Kaggle leaderboard [here](https://www.kaggle.com/c/korean-hate-speech-detection/overview).
### Languages
The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`.
## Dataset Structure
### Data Instances
An example data instance contains a `comments` containing the text of the news comment and then labels for each of the following fields: `contain_gender_bias`, `bias` and `hate`.
```python
{'comments':'설마 ㅈ 현정 작가 아니지??'
'contain_gender_bias': 'True',
'bias': 'gender',
'hate': 'hate'
}
```
### Data Fields
* `comments`: text from the Korean news comment
* `contain_gender_bias`: a binary `True`/`False` label for the presence of gender bias
* `bias`: determines the type of social bias, which can be:
* `gender`: if the text includes bias for gender role, sexual orientation, sexual identity, and any thoughts on gender-related acts
* `others`: other kinds of factors that are considered not gender-related but social bias, including race, background, nationality, ethnic group, political stance, skin color, religion, handicaps, age, appearance, richness, occupations, the absence of military service experience
* `none`: a comment that does not incorporate the bias
* `hate`: determines how aggressive the comment is, which can be:
* `hate`: if the text is defined as an expression that display aggressive stances towards individuals/groups with certain characteristics (gender role, sexual orientation, sexual identity, any thoughts on gender-related acts, race, background, nationality, ethnic group, political stance, skin color, religion, handicaps, age, appearance, richness, occupations, the absence of military service experience, etc.)
* `offensive`: if the text contains rude or aggressive contents, can emit sarcasm through rhetorical question or irony, encompass an unethical expression or conveys unidentified rumors
* `none`: a comment that does not incorporate hate
### Data Splits
The data is split into a training and development (test) set. It contains 8371 annotated comments that are split into 7896 comments in the training set and 471 comments in the test set.
## Dataset Creation
### Curation Rationale
The dataset was created to provide the first human-labeled Korean corpus for toxic speech detection from a Korean online entertainment news aggregator. Recently, two young Korean celebrities suffered from a series of tragic incidents that led to two major Korean web portals to close the comments section on their platform. However, this only serves as a temporary solution, and the fundamental issue has not been solved yet. This dataset hopes to improve Korean hate speech detection.
### Source Data
#### Initial Data Collection and Normalization
A total of 10.4 million comments were collected from an online Korean entertainment news aggregator between Jan. 1, 2018 and Feb. 29, 2020. 1,580 articles were drawn using stratified sampling and the top 20 comments were extracted ranked in order of their Wilson score on the downvote for each article. Duplicate comments, single token comments and comments with more than 100 characters were removed (because they could convey various opinions). From here, 10K comments were randomly chosen for annotation.
#### Who are the source language producers?
The language producers are users of the Korean online news platform between 2018 and 2020.
### Annotations
#### Annotation process
Each comment was assigned to three random annotators to assign a majority decision. For more ambiguous comments, annotators were allowed to skip the comment. See Appendix A in the [paper](https://arxiv.org/pdf/2005.12503.pdf) for more detailed guidelines.
#### Who are the annotators?
Annotation was performed by 32 annotators, consisting of 29 annotators from the crowdsourcing platform DeepNatural AI and three NLP researchers.
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to tackle the social issue of users creating toxic comments on online platforms. This dataset aims to improve detection of toxic comments online.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset is curated by Jihyung Moon, Won Ik Cho and Junbum Lee.
### Licensing Information
[N/A]
### Citation Information
```
@inproceedings
{moon-et-al-2020-beep
title = "{BEEP}! {K}orean Corpus of Online News Comments for Toxic Speech Detection",
author = "Moon, Jihyung and
Cho, Won Ik and
Lee, Junbum",
booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.socialnlp-1.4",
pages = "25--31",
abstract = "Toxic comments in online platforms are an unavoidable social issue under the cloak of anonymity. Hate speech detection has been actively done for languages such as English, German, or Italian, where manually labeled corpus has been released. In this work, we first present 9.4K manually labeled entertainment news comments for identifying Korean toxic speech, collected from a widely used online news platform in Korea. The comments are annotated regarding social bias and hate speech since both aspects are correlated. The inter-annotator agreement Krippendorff{'}s alpha score is 0.492 and 0.496, respectively. We provide benchmarks using CharCNN, BiLSTM, and BERT, where BERT achieves the highest score on all tasks. The models generally display better performance on bias identification, since the hate speech detection is a more subjective issue. Additionally, when BERT is trained with bias label for hate speech detection, the prediction score increases, implying that bias and hate are intertwined. We make our dataset publicly available and open competitions with the corpus and benchmarks.",
}
```
### Contributions
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset. | [
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|
result-kand2-sdxl-wuerst-karlo/2dde8d06 | result-kand2-sdxl-wuerst-karlo | 2023-11-08T00:00:01Z | 406 | 0 | [
"region:us"
]
| null | 2023-11-08T00:00:00Z | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 169
num_examples: 10
download_size: 1357
dataset_size: 169
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "2dde8d06"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
id_clickbait | null | 2023-01-25T14:32:36Z | 405 | 0 | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:id",
"license:cc-by-4.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- id
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: Indonesian Clickbait Headlines
dataset_info:
- config_name: annotated
features:
- name: id
dtype: string
- name: title
dtype: string
- name: label
dtype:
class_label:
names:
'0': non-clickbait
'1': clickbait
splits:
- name: train
num_bytes: 1268698
num_examples: 15000
download_size: 150769127
dataset_size: 1268698
- config_name: raw
features:
- name: id
dtype: string
- name: title
dtype: string
- name: source
dtype: string
- name: date
dtype: string
- name: category
dtype: string
- name: sub-category
dtype: string
- name: content
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 81669386
num_examples: 38655
download_size: 150769127
dataset_size: 81669386
---
# Dataset Card for Indonesian Clickbait 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://data.mendeley.com/datasets/k42j7x2kpn/1
- **Repository:**
- **Paper:** [CLICK-ID: A Novel Dataset for Indonesian Clickbait Headlines](https://www.sciencedirect.com/science/article/pii/S2352340920311252#!)
- **Leaderboard:**
- **Point of Contact:** [Andika William](mailto:[email protected]), [Yunita Sari](mailto:[email protected])
### Dataset Summary
The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news
publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo,
Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article data, and (ii)
15,000 clickbait annotated sample headlines. Annotation was conducted with 3 annotator examining each headline.
Judgment were based only on the headline. The majority then is considered as the ground truth. In the annotated
sample, our annotation shows 6,290 clickbait and 8,710 non-clickbait.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Indonesian
## Dataset Structure
### Data Instances
An example of the annotated article:
```
{
'id': '100',
'label': 1,
'title': "SAH! Ini Daftar Nama Menteri Kabinet Jokowi - Ma'ruf Amin"
}
>
```
### Data Fields
#### Annotated
- `id`: id of the sample
- `title`: the title of the news article
- `label`: the label of the article, either non-clickbait or clickbait
#### Raw
- `id`: id of the sample
- `title`: the title of the news article
- `source`: the name of the publisher/newspaper
- `date`: date
- `category`: the category of the article
- `sub-category`: the sub category of the article
- `content`: the content of the article
- `url`: the url of the article
### Data Splits
The dataset contains train 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
[More Information Needed]
### Licensing Information
Creative Commons Attribution 4.0 International license
### Citation Information
```
@article{WILLIAM2020106231,
title = "CLICK-ID: A novel dataset for Indonesian clickbait headlines",
journal = "Data in Brief",
volume = "32",
pages = "106231",
year = "2020",
issn = "2352-3409",
doi = "https://doi.org/10.1016/j.dib.2020.106231",
url = "http://www.sciencedirect.com/science/article/pii/S2352340920311252",
author = "Andika William and Yunita Sari",
keywords = "Indonesian, Natural Language Processing, News articles, Clickbait, Text-classification",
abstract = "News analysis is a popular task in Natural Language Processing (NLP). In particular, the problem of clickbait in news analysis has gained attention in recent years [1, 2]. However, the majority of the tasks has been focused on English news, in which there is already a rich representative resource. For other languages, such as Indonesian, there is still a lack of resource for clickbait tasks. Therefore, we introduce the CLICK-ID dataset of Indonesian news headlines extracted from 12 Indonesian online news publishers. It is comprised of 15,000 annotated headlines with clickbait and non-clickbait labels. Using the CLICK-ID dataset, we then developed an Indonesian clickbait classification model achieving favourable performance. We believe that this corpus will be useful for replicable experiments in clickbait detection or other experiments in NLP areas."
}
```
### Contributions
Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.
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|
multi_nli_mismatch | null | 2023-04-05T10:10:18Z | 404 | 2 | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"license:cc-by-sa-3.0",
"license:mit",
"license:other",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-3.0
- cc-by-sa-3.0
- mit
- other
license_details: Open Portion of the American National Corpus
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
paperswithcode_id: multinli
pretty_name: Multi-Genre Natural Language Inference
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 75601459
num_examples: 392702
- name: validation
num_bytes: 2009444
num_examples: 10000
download_size: 226850426
dataset_size: 77610903
---
# Dataset Card for Multi-Genre Natural Language Inference (Mismatched only)
## 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.nyu.edu/projects/bowman/multinli/](https://www.nyu.edu/projects/bowman/multinli/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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 downloaded dataset files:** 226.85 MB
- **Size of the generated dataset:** 77.62 MB
- **Total amount of disk used:** 304.46 MB
### Dataset Summary
The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
crowd-sourced collection of 433k sentence pairs annotated with textual
entailment information. The corpus is modeled on the SNLI corpus, but differs in
that covers a range of genres of spoken and written text, and supports a
distinctive cross-genre generalization evaluation. The corpus served as the
basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 226.85 MB
- **Size of the generated dataset:** 77.62 MB
- **Total amount of disk used:** 304.46 MB
An example of 'train' looks as follows.
```
{
"hypothesis": "independence",
"label": "contradiction",
"premise": "correlation"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a `string` feature.
### Data Splits
| name |train |validation|
|----------|-----:|---------:|
|plain_text|392702| 10000|
## 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{N18-1101,
author = "Williams, Adina
and Nangia, Nikita
and Bowman, Samuel",
title = "A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference",
booktitle = "Proceedings of the 2018 Conference of
the North American Chapter of the
Association for Computational Linguistics:
Human Language Technologies, Volume 1 (Long
Papers)",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "1112--1122",
location = "New Orleans, Louisiana",
url = "http://aclweb.org/anthology/N18-1101"
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. | [
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|
EddieChen372/devign_with_vul_lines | EddieChen372 | 2023-02-04T15:24:46Z | 404 | 0 | [
"region:us"
]
| null | 2023-02-01T12:14:23Z | ---
dataset_info:
features:
- name: id
dtype: int32
- name: func
dtype: string
- name: target
dtype: bool
- name: project
dtype: string
- name: commit_id
dtype: string
- name: func_clean
dtype: string
- name: vul_lines
struct:
- name: code
sequence: string
- name: line_no
sequence: int64
- name: normalized_func
dtype: string
splits:
- name: validation
num_bytes: 16112369
num_examples: 2732
- name: train
num_bytes: 132054560
num_examples: 21854
- name: test
num_bytes: 16328301
num_examples: 2732
download_size: 60272537
dataset_size: 164495230
---
# Dataset Card for "devign_with_vul_lines"
[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_PulsarAI__GenAI-Nova-13B | open-llm-leaderboard | 2023-10-29T14:59:11Z | 404 | 0 | [
"region:us"
]
| null | 2023-10-08T15:05:43Z | ---
pretty_name: Evaluation run of PulsarAI/GenAI-Nova-13B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [PulsarAI/GenAI-Nova-13B](https://huggingface.co/PulsarAI/GenAI-Nova-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_PulsarAI__GenAI-Nova-13B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-29T14:58:59.300779](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__GenAI-Nova-13B/blob/main/results_2023-10-29T14-58-59.300779.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\": 0.10769714765100671,\n\
\ \"em_stderr\": 0.003174664916131534,\n \"f1\": 0.18815016778523358,\n\
\ \"f1_stderr\": 0.0033317211011039192,\n \"acc\": 0.4254059872915611,\n\
\ \"acc_stderr\": 0.009560931288960338\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.10769714765100671,\n \"em_stderr\": 0.003174664916131534,\n\
\ \"f1\": 0.18815016778523358,\n \"f1_stderr\": 0.0033317211011039192\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07733131159969674,\n \
\ \"acc_stderr\": 0.007357713523222347\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.01176414905469833\n\
\ }\n}\n```"
repo_url: https://huggingface.co/PulsarAI/GenAI-Nova-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_10_08T15_05_19.512883
path:
- '**/details_harness|arc:challenge|25_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_29T14_58_59.300779
path:
- '**/details_harness|drop|3_2023-10-29T14-58-59.300779.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-29T14-58-59.300779.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_29T14_58_59.300779
path:
- '**/details_harness|gsm8k|5_2023-10-29T14-58-59.300779.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-29T14-58-59.300779.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hellaswag|10_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-08T15-05-19.512883.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-08T15-05-19.512883.parquet'
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- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-08T15-05-19.512883.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-08T15-05-19.512883.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-08T15-05-19.512883.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-08T15-05-19.512883.parquet'
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- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-08T15-05-19.512883.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-08T15-05-19.512883.parquet'
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- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-08T15-05-19.512883.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-08T15-05-19.512883.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_29T14_58_59.300779
path:
- '**/details_harness|winogrande|5_2023-10-29T14-58-59.300779.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-29T14-58-59.300779.parquet'
- config_name: results
data_files:
- split: 2023_10_08T15_05_19.512883
path:
- results_2023-10-08T15-05-19.512883.parquet
- split: 2023_10_29T14_58_59.300779
path:
- results_2023-10-29T14-58-59.300779.parquet
- split: latest
path:
- results_2023-10-29T14-58-59.300779.parquet
---
# Dataset Card for Evaluation run of PulsarAI/GenAI-Nova-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PulsarAI/GenAI-Nova-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 [PulsarAI/GenAI-Nova-13B](https://huggingface.co/PulsarAI/GenAI-Nova-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_PulsarAI__GenAI-Nova-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T14:58:59.300779](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__GenAI-Nova-13B/blob/main/results_2023-10-29T14-58-59.300779.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": 0.10769714765100671,
"em_stderr": 0.003174664916131534,
"f1": 0.18815016778523358,
"f1_stderr": 0.0033317211011039192,
"acc": 0.4254059872915611,
"acc_stderr": 0.009560931288960338
},
"harness|drop|3": {
"em": 0.10769714765100671,
"em_stderr": 0.003174664916131534,
"f1": 0.18815016778523358,
"f1_stderr": 0.0033317211011039192
},
"harness|gsm8k|5": {
"acc": 0.07733131159969674,
"acc_stderr": 0.007357713523222347
},
"harness|winogrande|5": {
"acc": 0.7734806629834254,
"acc_stderr": 0.01176414905469833
}
}
```
### 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_breadlicker45__dough-instruct-base-001 | open-llm-leaderboard | 2023-09-16T20:08:46Z | 403 | 0 | [
"region:us"
]
| null | 2023-08-18T11:08:23Z | ---
pretty_name: Evaluation run of breadlicker45/dough-instruct-base-001
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [breadlicker45/dough-instruct-base-001](https://huggingface.co/breadlicker45/dough-instruct-base-001)\
\ 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_breadlicker45__dough-instruct-base-001\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-16T20:08:34.854052](https://huggingface.co/datasets/open-llm-leaderboard/details_breadlicker45__dough-instruct-base-001/blob/main/results_2023-09-16T20-08-34.854052.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\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 0.0029163171140939564,\n \"f1_stderr\"\
: 0.00019355490209304062,\n \"acc\": 0.255327545382794,\n \"acc_stderr\"\
: 0.007024647268145198\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n\
\ \"em_stderr\": 0.0,\n \"f1\": 0.0029163171140939564,\n \"\
f1_stderr\": 0.00019355490209304062\n },\n \"harness|gsm8k|5\": {\n \
\ \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.510655090765588,\n \"acc_stderr\": 0.014049294536290396\n\
\ }\n}\n```"
repo_url: https://huggingface.co/breadlicker45/dough-instruct-base-001
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_02T14_53_33.597217
path:
- '**/details_harness|arc:challenge|25_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_16T20_08_34.854052
path:
- '**/details_harness|drop|3_2023-09-16T20-08-34.854052.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-16T20-08-34.854052.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_16T20_08_34.854052
path:
- '**/details_harness|gsm8k|5_2023-09-16T20-08-34.854052.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-16T20-08-34.854052.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hellaswag|10_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
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- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T14:53:33.597217.parquet'
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- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-02T14:53:33.597217.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-02T14:53:33.597217.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-02T14:53:33.597217.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_16T20_08_34.854052
path:
- '**/details_harness|winogrande|5_2023-09-16T20-08-34.854052.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-16T20-08-34.854052.parquet'
- config_name: results
data_files:
- split: 2023_08_02T14_53_33.597217
path:
- results_2023-08-02T14:53:33.597217.parquet
- split: 2023_09_16T20_08_34.854052
path:
- results_2023-09-16T20-08-34.854052.parquet
- split: latest
path:
- results_2023-09-16T20-08-34.854052.parquet
---
# Dataset Card for Evaluation run of breadlicker45/dough-instruct-base-001
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/breadlicker45/dough-instruct-base-001
- **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 [breadlicker45/dough-instruct-base-001](https://huggingface.co/breadlicker45/dough-instruct-base-001) 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_breadlicker45__dough-instruct-base-001",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-16T20:08:34.854052](https://huggingface.co/datasets/open-llm-leaderboard/details_breadlicker45__dough-instruct-base-001/blob/main/results_2023-09-16T20-08-34.854052.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": 0.0,
"em_stderr": 0.0,
"f1": 0.0029163171140939564,
"f1_stderr": 0.00019355490209304062,
"acc": 0.255327545382794,
"acc_stderr": 0.007024647268145198
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0029163171140939564,
"f1_stderr": 0.00019355490209304062
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.510655090765588,
"acc_stderr": 0.014049294536290396
}
}
```
### 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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|
TerminatorJ/localization_multiRNA | TerminatorJ | 2023-11-20T22:37:14Z | 403 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-11-20T20:38:27Z | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: "Train_fold0.csv"
- split: test
path: "Test_fold0.csv"
- split: validation
path: "Val_fold0.csv"
---
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|
conv_ai | null | 2022-11-03T16:30:55Z | 402 | 2 | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"evaluating-dialogue-systems",
"region:us"
]
| [
"conversational",
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- conversational
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: ConvAi
tags:
- evaluating-dialogue-systems
dataset_info:
features:
- name: id
dtype: int32
- name: dialogId
dtype: int32
- name: context
dtype: string
- name: users
list:
- name: userType
dtype: string
- name: id
dtype: string
- name: evaluation
list:
- name: breadth
dtype: int32
- name: userId
dtype: string
- name: quality
dtype: int32
- name: engagement
dtype: int32
- name: thread
list:
- name: evaluation
dtype: int32
- name: text
dtype: string
- name: userId
dtype: string
- name: time
dtype: int32
config_name: conv_ai
splits:
- name: train
num_bytes: 3924265
num_examples: 2778
download_size: 5804611
dataset_size: 3924265
---
# Dataset Card for ConvAi
## 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:** [Add homepage URL here if available (unless it's a GitHub repository)]()
- **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]()
- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
### 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 [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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|
bsd_ja_en | null | 2022-11-18T19:24:36Z | 401 | 4 | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:ja",
"license:cc-by-nc-sa-4.0",
"business-conversations-translation",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- ja
license:
- cc-by-nc-sa-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: business-scene-dialogue
pretty_name: Business Scene Dialogue
tags:
- business-conversations-translation
dataset_info:
features:
- name: id
dtype: string
- name: tag
dtype: string
- name: title
dtype: string
- name: original_language
dtype: string
- name: 'no'
dtype: int32
- name: en_speaker
dtype: string
- name: ja_speaker
dtype: string
- name: en_sentence
dtype: string
- name: ja_sentence
dtype: string
splits:
- name: train
num_bytes: 4778409
num_examples: 20000
- name: test
num_bytes: 493038
num_examples: 2120
- name: validation
num_bytes: 477964
num_examples: 2051
download_size: 8135045
dataset_size: 5749411
---
# Dataset Card for Business Scene Dialogue
## 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:** [Github](https://raw.githubusercontent.com/tsuruoka-lab/BSD/)
- **Repository:** [Github](https://raw.githubusercontent.com/tsuruoka-lab/BSD/)
- **Paper:** [Rikters et al., 2019](https://www.aclweb.org/anthology/D19-5204)
- **Leaderboard:**
- **Point of Contact:** Matīss Rikters
### Dataset Summary
This is the Business Scene Dialogue (BSD) dataset,
a Japanese-English parallel corpus containing written conversations
in various business scenarios.
The dataset was constructed in 3 steps:
1) selecting business scenes,
2) writing monolingual conversation scenarios according to the selected scenes, and
3) translating the scenarios into the other language.
Half of the monolingual scenarios were written in Japanese
and the other half were written in English.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English, Japanese.
## Dataset Structure
### Data Instances
Each instance contains a conversation identifier, a sentence number that indicates its
position within the conversation, speaker name in English and Japanese,
text in English and Japanese, original language, scene of the scenario (tag),
and title of the scenario (title).
```python
{
"id": "190315_E004_13",
"no": 14,
"speaker": "Mr. Sam Lee",
"ja_speaker": "サム リーさん",
"en_sentence": "Would you guys consider a different scheme?",
"ja_sentence": "別の事業案も考慮されますか？",
"original_language": "en",
"tag": "phone call",
"title": "Phone: Review spec and scheme"
}
```
### Data Fields
- id: dialogue identifier
- no: sentence pair number within a dialogue
- en_speaker: speaker name in English
- ja_speaker: speaker name in Japanese
- en_sentence: sentence in English
- ja_sentence: sentence in Japanese
- original_language: language in which monolingual scenario was written
- tag: scenario
- title: scenario title
### Data Splits
- There are a total of 24171 sentences / 808 business scenarios.
- Train: 20000 sentences / 670 scenarios
- Dev: 2051 sentences / 69 scenarios
- Test: 2120 sentences / 69 scenarios
## 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
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This dataset was released under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license.
### Citation Information
```
@inproceedings{rikters-etal-2019-designing,
title = "Designing the Business Conversation Corpus",
author = "Rikters, Mat{\=\i}ss and
Ri, Ryokan and
Li, Tong and
Nakazawa, Toshiaki",
booktitle = "Proceedings of the 6th Workshop on Asian Translation",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-5204",
doi = "10.18653/v1/D19-5204",
pages = "54--61"
}
```
### Contributions
Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset. | [
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|
eurlex | null | 2022-11-18T20:01:34Z | 401 | 4 | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"legal-topic-classification",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: eurlex57k
pretty_name: the EUR-Lex dataset
tags:
- legal-topic-classification
dataset_info:
features:
- name: celex_id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: eurovoc_concepts
sequence: string
config_name: eurlex57k
splits:
- name: train
num_bytes: 167603718
num_examples: 45000
- name: test
num_bytes: 22046706
num_examples: 6000
- name: validation
num_bytes: 21942574
num_examples: 6000
download_size: 50289403
dataset_size: 211592998
---
# Dataset Card for the EUR-Lex 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:** http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/
- **Repository:** http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/
- **Paper:** https://www.aclweb.org/anthology/P19-1636/
- **Leaderboard:** N/A
- **Point of Contact:** [Ilias Chalkidis](mailto:[email protected])
### Dataset Summary
EURLEX57K can be viewed as an improved version of the dataset released by Mencia and Furnkranzand (2007), which has been widely used in Large-scale Multi-label Text Classification (LMTC) research, but is less than half the size of EURLEX57K (19.6k documents, 4k EUROVOC labels) and more than ten years old.
EURLEX57K contains 57k legislative documents in English from EUR-Lex (https://eur-lex.europa.eu) with an average length of 727 words. Each document contains four major zones:
- the header, which includes the title and name of the legal body enforcing the legal act;
- the recitals, which are legal background references; and
- the main body, usually organized in articles.
**Labeling / Annotation**
All the documents of the dataset have been annotated by the Publications Office of EU (https://publications.europa.eu/en) with multiple concepts from EUROVOC (http://eurovoc.europa.eu/).
While EUROVOC includes approx. 7k concepts (labels), only 4,271 (59.31%) are present in EURLEX57K, from which only 2,049 (47.97%) have been assigned to more than 10 documents. The 4,271 labels are also divided into frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively.
### Supported Tasks and Leaderboards
The dataset supports:
**Multi-label Text Classification:** Given the text of a document, a model predicts the relevant EUROVOC concepts.
**Few-shot and Zero-shot learning:** As already noted, the labels can be divided into three groups: frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively.
### Languages
All documents are written in English.
## Dataset Structure
### Data Instances
```json
{
"celex_id": "31979D0509",
"title": "79/509/EEC: Council Decision of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain",
"text": "COUNCIL DECISION of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain (79/509/EEC)\nTHE COUNCIL OF THE EUROPEAN COMMUNITIES\nHaving regard to the Treaty establishing the European Economic Community, and in particular Article 43 thereof,\nHaving regard to the proposal from the Commission (1),\nHaving regard to the opinion of the European Parliament (2),\nWhereas the Community should take all appropriate measures to protect itself against the appearance of African swine fever on its territory;\nWhereas to this end the Community has undertaken, and continues to undertake, action designed to contain outbreaks of this type of disease far from its frontiers by helping countries affected to reinforce their preventive measures ; whereas for this purpose Community subsidies have already been granted to Spain;\nWhereas these measures have unquestionably made an effective contribution to the protection of Community livestock, especially through the creation and maintenance of a buffer zone north of the river Ebro;\nWhereas, however, in the opinion of the Spanish authorities themselves, the measures so far implemented must be reinforced if the fundamental objective of eradicating the disease from the entire country is to be achieved;\nWhereas the Spanish authorities have asked the Community to contribute to the expenses necessary for the efficient implementation of a total eradication programme;\nWhereas a favourable response should be given to this request by granting aid to Spain, having regard to the undertaking given by that country to protect the Community against African swine fever and to eliminate completely this disease by the end of a five-year eradication plan;\nWhereas this eradication plan must include certain measures which guarantee the effectiveness of the action taken, and it must be possible to adapt these measures to developments in the situation by means of a procedure establishing close cooperation between the Member States and the Commission;\nWhereas it is necessary to keep the Member States regularly informed as to the progress of the action undertaken,",
"eurovoc_concepts": ["192", "2356", "2560", "862", "863"]
}
```
### Data Fields
The following data fields are provided for documents (`train`, `dev`, `test`):
`celex_id`: (**str**) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR.\
`title`: (**str**) The title of the document.\
`text`: (**str**) The full content of each document, which is represented by its `header`, `recitals` and `main_body`.\
`eurovoc_concepts`: (**List[str]**) The relevant EUROVOC concepts (labels).
If you want to use the descriptors of EUROVOC concepts, similar to Chalkidis et al. (2020), please load: https://archive.org/download/EURLEX57K/eurovoc_concepts.jsonl
```python
import json
with open('./eurovoc_concepts.jsonl') as jsonl_file:
eurovoc_concepts = {json.loads(concept) for concept in jsonl_file.readlines()}
```
### Data Splits
| Split | No of Documents | Avg. words | Avg. labels |
| ------------------- | ------------------------------------ | --- | --- |
| Train | 45,000 | 729 | 5 |
|Development | 6,000 | 714 | 5 |
|Test | 6,000 | 725 | 5 |
## Dataset Creation
### Curation Rationale
The dataset was curated by Chalkidis et al. (2019).\
The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en).
### Source Data
#### Initial Data Collection and Normalization
The original data are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed format.
The documents were downloaded from EUR-Lex portal in HTML format.
The relevant metadata and EUROVOC concepts were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql).
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
* The original documents are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed HTML format. The HTML code was striped and the documents split into sections.
* The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en).
#### Who are the annotators?
Publications Office of EU (https://publications.europa.eu/en)
### Personal and Sensitive Information
The dataset does not include personal or sensitive information.
## 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
Chalkidis et al. (2019)
### Licensing Information
© European Union, 1998-2021
The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.
The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \
Read more: https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html
### Citation Information
*Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis and Ion Androutsopoulos.*
*Large-Scale Multi-Label Text Classification on EU Legislation.*
*Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Florence, Italy. 2019*
```
@inproceedings{chalkidis-etal-2019-large,
title = "Large-Scale Multi-Label Text Classification on {EU} Legislation",
author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Androutsopoulos, Ion",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1636",
doi = "10.18653/v1/P19-1636",
pages = "6314--6322"
}
```
### Contributions
Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset. | [
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|
jeopardy | null | 2023-04-05T10:07:53Z | 401 | 4 | [
"language:en",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
paperswithcode_id: null
pretty_name: jeopardy
dataset_info:
features:
- name: category
dtype: string
- name: air_date
dtype: string
- name: question
dtype: string
- name: value
dtype: int32
- name: answer
dtype: string
- name: round
dtype: string
- name: show_number
dtype: int32
splits:
- name: train
num_bytes: 35916080
num_examples: 216930
download_size: 55554625
dataset_size: 35916080
---
# Dataset Card for "jeopardy"
## 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.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file/](https://www.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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 downloaded dataset files:** 12.72 MB
- **Size of the generated dataset:** 36.13 MB
- **Total amount of disk used:** 48.85 MB
### Dataset Summary
Dataset containing 216,930 Jeopardy questions, answers and other data.
The json file is an unordered list of questions where each question has
'category' : the question category, e.g. "HISTORY"
'value' : integer $ value of the question as string, e.g. "200"
Note: This is "None" for Final Jeopardy! and Tiebreaker questions
'question' : text of question
Note: This sometimes contains hyperlinks and other things messy text such as when there's a picture or video question
'answer' : text of answer
'round' : one of "Jeopardy!","Double Jeopardy!","Final Jeopardy!" or "Tiebreaker"
Note: Tiebreaker questions do happen but they're very rare (like once every 20 years)
'show_number' : int of show number, e.g '4680'
'air_date' : string of the show air date in format YYYY-MM-DD
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 12.72 MB
- **Size of the generated dataset:** 36.13 MB
- **Total amount of disk used:** 48.85 MB
An example of 'train' looks as follows.
```
{
"air_date": "2004-12-31",
"answer": "Hattie McDaniel (for her role in Gone with the Wind)",
"category": "EPITAPHS & TRIBUTES",
"question": "'1939 Oscar winner: \"...you are a credit to your craft, your race and to your family\"'",
"round": "Jeopardy!",
"show_number": 4680,
"value": 2000
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `category`: a `string` feature.
- `air_date`: a `string` feature.
- `question`: a `string` feature.
- `value`: a `int32` feature.
- `answer`: a `string` feature.
- `round`: a `string` feature.
- `show_number`: a `int32` feature.
### Data Splits
| name |train |
|-------|-----:|
|default|216930|
## 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
```
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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|
offcombr | null | 2023-01-25T14:41:55Z | 401 | 4 | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pt",
"license:unknown",
"hate-speech-detection",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: offcombr
pretty_name: Offensive Comments in the Brazilian Web
tags:
- hate-speech-detection
dataset_info:
- config_name: offcombr-2
features:
- name: label
dtype:
class_label:
names:
'0': 'no'
'1': 'yes'
- name: text
dtype: string
splits:
- name: train
num_bytes: 105703
num_examples: 1250
download_size: 99956
dataset_size: 105703
- config_name: offcombr-3
features:
- name: label
dtype:
class_label:
names:
'0': 'no'
'1': 'yes'
- name: text
dtype: string
splits:
- name: train
num_bytes: 90094
num_examples: 1033
download_size: 85215
dataset_size: 90094
---
# 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://www.inf.ufrgs.br/~rppelle/hatedetector/
- **Repository:** https://github.com/rogersdepelle/OffComBR
- **Paper:** https://sol.sbc.org.br/index.php/brasnam/article/view/3260/3222
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OffComBR: an annotated dataset containing for hate speech detection in Portuguese composed of news comments on the Brazilian Web.
### 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
Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset. | [
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|
qa_zre | null | 2023-04-05T13:37:03Z | 401 | 2 | [
"task_categories:question-answering",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:unknown",
"zero-shot-relation-extraction",
"region:us"
]
| [
"question-answering"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: QaZre
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: null
tags:
- zero-shot-relation-extraction
dataset_info:
features:
- name: relation
dtype: string
- name: question
dtype: string
- name: subject
dtype: string
- name: context
dtype: string
- name: answers
sequence: string
splits:
- name: test
num_bytes: 29410194
num_examples: 120000
- name: validation
num_bytes: 1481430
num_examples: 6000
- name: train
num_bytes: 2054954011
num_examples: 8400000
download_size: 516061636
dataset_size: 2085845635
---
# Dataset Card for QaZre
## 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.cs.washington.edu/zeroshot](http://nlp.cs.washington.edu/zeroshot)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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 downloaded dataset files:** 516.06 MB
- **Size of the generated dataset:** 2.09 GB
- **Total amount of disk used:** 2.60 GB
### Dataset Summary
A dataset reducing relation extraction to simple reading comprehension questions
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 516.06 MB
- **Size of the generated dataset:** 2.09 GB
- **Total amount of disk used:** 2.60 GB
An example of 'validation' looks as follows.
```
{
"answers": [],
"context": "answer",
"question": "What is XXX in this question?",
"relation": "relation_name",
"subject": "Some entity Here is a bit of context which will explain the question in some way"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `relation`: a `string` feature.
- `question`: a `string` feature.
- `subject`: a `string` feature.
- `context`: a `string` feature.
- `answers`: a `list` of `string` features.
### Data Splits
| name | train | validation | test |
|---------|--------:|-----------:|-------:|
| default | 8400000 | 6000 | 120000 |
## 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
Unknown.
### Citation Information
```
@inproceedings{levy-etal-2017-zero,
title = "Zero-Shot Relation Extraction via Reading Comprehension",
author = "Levy, Omer and
Seo, Minjoon and
Choi, Eunsol and
Zettlemoyer, Luke",
booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/K17-1034",
doi = "10.18653/v1/K17-1034",
pages = "333--342",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@ghomasHudson](https://github.com/ghomasHudson), [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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|
open-llm-leaderboard/details_jondurbin__airoboros-c34b-2.2.1 | open-llm-leaderboard | 2023-10-27T01:27:41Z | 401 | 0 | [
"region:us"
]
| null | 2023-10-01T18:58:33Z | ---
pretty_name: Evaluation run of jondurbin/airoboros-c34b-2.2.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jondurbin/airoboros-c34b-2.2.1](https://huggingface.co/jondurbin/airoboros-c34b-2.2.1)\
\ 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_jondurbin__airoboros-c34b-2.2.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-27T01:27:27.321829](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-c34b-2.2.1/blob/main/results_2023-10-27T01-27-27.321829.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\": 0.020343959731543623,\n\
\ \"em_stderr\": 0.0014457533435412132,\n \"f1\": 0.08045826342281848,\n\
\ \"f1_stderr\": 0.0019040548084027437,\n \"acc\": 0.4627435340326824,\n\
\ \"acc_stderr\": 0.011782817513813687\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.020343959731543623,\n \"em_stderr\": 0.0014457533435412132,\n\
\ \"f1\": 0.08045826342281848,\n \"f1_stderr\": 0.0019040548084027437\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2001516300227445,\n \
\ \"acc_stderr\": 0.011021119022510194\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7253354380426204,\n \"acc_stderr\": 0.01254451600511718\n\
\ }\n}\n```"
repo_url: https://huggingface.co/jondurbin/airoboros-c34b-2.2.1
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_10_01T18_58_09.868261
path:
- '**/details_harness|arc:challenge|25_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_27T01_27_27.321829
path:
- '**/details_harness|drop|3_2023-10-27T01-27-27.321829.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-27T01-27-27.321829.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_27T01_27_27.321829
path:
- '**/details_harness|gsm8k|5_2023-10-27T01-27-27.321829.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-27T01-27-27.321829.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hellaswag|10_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T18-58-09.868261.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T18-58-09.868261.parquet'
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- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T18-58-09.868261.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-01T18-58-09.868261.parquet'
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- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T18-58-09.868261.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T18-58-09.868261.parquet'
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- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T18-58-09.868261.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T18-58-09.868261.parquet'
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path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T18-58-09.868261.parquet'
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- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T18-58-09.868261.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-01T18-58-09.868261.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-01T18-58-09.868261.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_27T01_27_27.321829
path:
- '**/details_harness|winogrande|5_2023-10-27T01-27-27.321829.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-27T01-27-27.321829.parquet'
- config_name: results
data_files:
- split: 2023_10_01T18_58_09.868261
path:
- results_2023-10-01T18-58-09.868261.parquet
- split: 2023_10_27T01_27_27.321829
path:
- results_2023-10-27T01-27-27.321829.parquet
- split: latest
path:
- results_2023-10-27T01-27-27.321829.parquet
---
# Dataset Card for Evaluation run of jondurbin/airoboros-c34b-2.2.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jondurbin/airoboros-c34b-2.2.1
- **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 [jondurbin/airoboros-c34b-2.2.1](https://huggingface.co/jondurbin/airoboros-c34b-2.2.1) 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_jondurbin__airoboros-c34b-2.2.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T01:27:27.321829](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-c34b-2.2.1/blob/main/results_2023-10-27T01-27-27.321829.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": 0.020343959731543623,
"em_stderr": 0.0014457533435412132,
"f1": 0.08045826342281848,
"f1_stderr": 0.0019040548084027437,
"acc": 0.4627435340326824,
"acc_stderr": 0.011782817513813687
},
"harness|drop|3": {
"em": 0.020343959731543623,
"em_stderr": 0.0014457533435412132,
"f1": 0.08045826342281848,
"f1_stderr": 0.0019040548084027437
},
"harness|gsm8k|5": {
"acc": 0.2001516300227445,
"acc_stderr": 0.011021119022510194
},
"harness|winogrande|5": {
"acc": 0.7253354380426204,
"acc_stderr": 0.01254451600511718
}
}
```
### 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_PulsarAI__2x-LoRA-Assemble-13B | open-llm-leaderboard | 2023-10-28T12:51:44Z | 401 | 0 | [
"region:us"
]
| null | 2023-10-28T12:51:37Z | ---
pretty_name: Evaluation run of PulsarAI/2x-LoRA-Assemble-13B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [PulsarAI/2x-LoRA-Assemble-13B](https://huggingface.co/PulsarAI/2x-LoRA-Assemble-13B)\
\ 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_PulsarAI__2x-LoRA-Assemble-13B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T12:51:33.520951](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__2x-LoRA-Assemble-13B/blob/main/results_2023-10-28T12-51-33.520951.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\": 0.01740771812080537,\n\
\ \"em_stderr\": 0.0013393597649753585,\n \"f1\": 0.12005662751677883,\n\
\ \"f1_stderr\": 0.002244767452564408,\n \"acc\": 0.42864652552428745,\n\
\ \"acc_stderr\": 0.009950202519105519\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.01740771812080537,\n \"em_stderr\": 0.0013393597649753585,\n\
\ \"f1\": 0.12005662751677883,\n \"f1_stderr\": 0.002244767452564408\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09249431387414708,\n \
\ \"acc_stderr\": 0.007980396874560173\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7647987371744278,\n \"acc_stderr\": 0.011920008163650865\n\
\ }\n}\n```"
repo_url: https://huggingface.co/PulsarAI/2x-LoRA-Assemble-13B
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_28T12_51_33.520951
path:
- '**/details_harness|drop|3_2023-10-28T12-51-33.520951.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T12-51-33.520951.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T12_51_33.520951
path:
- '**/details_harness|gsm8k|5_2023-10-28T12-51-33.520951.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T12-51-33.520951.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T12_51_33.520951
path:
- '**/details_harness|winogrande|5_2023-10-28T12-51-33.520951.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T12-51-33.520951.parquet'
- config_name: results
data_files:
- split: 2023_10_28T12_51_33.520951
path:
- results_2023-10-28T12-51-33.520951.parquet
- split: latest
path:
- results_2023-10-28T12-51-33.520951.parquet
---
# Dataset Card for Evaluation run of PulsarAI/2x-LoRA-Assemble-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PulsarAI/2x-LoRA-Assemble-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 [PulsarAI/2x-LoRA-Assemble-13B](https://huggingface.co/PulsarAI/2x-LoRA-Assemble-13B) 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_PulsarAI__2x-LoRA-Assemble-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T12:51:33.520951](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__2x-LoRA-Assemble-13B/blob/main/results_2023-10-28T12-51-33.520951.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": 0.01740771812080537,
"em_stderr": 0.0013393597649753585,
"f1": 0.12005662751677883,
"f1_stderr": 0.002244767452564408,
"acc": 0.42864652552428745,
"acc_stderr": 0.009950202519105519
},
"harness|drop|3": {
"em": 0.01740771812080537,
"em_stderr": 0.0013393597649753585,
"f1": 0.12005662751677883,
"f1_stderr": 0.002244767452564408
},
"harness|gsm8k|5": {
"acc": 0.09249431387414708,
"acc_stderr": 0.007980396874560173
},
"harness|winogrande|5": {
"acc": 0.7647987371744278,
"acc_stderr": 0.011920008163650865
}
}
```
### 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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|
NbAiLab/norwegian_parliament | NbAiLab | 2022-07-01T19:51:13Z | 400 | 1 | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:no",
"license:cc-by-4.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- no
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
---
# Dataset Card Creation Guide
## 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:** N/A
- **Repository:** [GitHub](https://github.com/ltgoslo/NorBERT/)
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** -
### Dataset Summary
The Norwegian Parliament Speeches is a collection of text passages from 1998 to 2016 and pronounced at the Norwegian Parliament (Storting) by members of the two major parties: Fremskrittspartiet and Sosialistisk Venstreparti. The dataset is annotated with the party the speaker was associated with at the time (dates of speeches are also included).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in Norwegian.
## Dataset Structure
### Data Instances
Example of one instance in the dataset.
```{'label': 0, 'text': 'Verre er det med slagsmålene .'}```
### Data Fields
- `id`: index of the example
- `text`: Text of a speech
- `date`: Date (`YYYY-MM-DD`) the speech was produced
- `label`: Political party the speaker was associated with at the time
- 0 = Fremskrittspartiet
- 1 = Sosialistisk Venstreparti
### Data Splits
The dataset is split into a `train`, `validation`, and `test` split with the following sizes:
| | Tain | Valid | Test |
| ----- | ------ | ----- | ----- |
| Number of examples | 3600 | 1200 | 1200 |
The dataset is balanced on political party.
## Dataset Creation
This dataset is based on the publicly available information by Norwegian Parliament (Storting) and created by the National Library of Norway AI-Lab to benchmark their language models.
## Additional Information
### Licensing Information
This work is licensed under a Creative Commons Attribution 4.0 International License
### Citation Information
```latex
@misc{--,
title={--},
author={--},
year={2021},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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|
range3/cc100-ja | range3 | 2023-02-04T05:43:32Z | 400 | 9 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"language:ja",
"license:unknown",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2023-02-04T05:10:34Z | ---
license: unknown
task_categories:
- text-generation
- fill-mask
language:
- ja
---
# range3/cc100-ja
This dataset consists of parquet files from the cc100 dataset with only the Japanese language extracted and sharded.
このデータセットは、cc100データセットの日本語のみを抽出し、シャーディングしたparquetファイルで構成されます。 | [
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|
open-llm-leaderboard/details_AlekseyKorshuk__pygmalion-6b-vicuna-chatml | open-llm-leaderboard | 2023-12-02T15:52:26Z | 400 | 0 | [
"region:us"
]
| null | 2023-08-18T00:14:00Z | ---
pretty_name: Evaluation run of AlekseyKorshuk/pygmalion-6b-vicuna-chatml
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [AlekseyKorshuk/pygmalion-6b-vicuna-chatml](https://huggingface.co/AlekseyKorshuk/pygmalion-6b-vicuna-chatml)\
\ 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 5 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 aggregated 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_AlekseyKorshuk__pygmalion-6b-vicuna-chatml\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T15:52:11.848314](https://huggingface.co/datasets/open-llm-leaderboard/details_AlekseyKorshuk__pygmalion-6b-vicuna-chatml/blob/main/results_2023-12-02T15-52-11.848314.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\": 0.04397270659590599,\n\
\ \"acc_stderr\": 0.005647666449126458\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.04397270659590599,\n \"acc_stderr\": 0.005647666449126458\n\
\ }\n}\n```"
repo_url: https://huggingface.co/AlekseyKorshuk/pygmalion-6b-vicuna-chatml
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_58_39.640665
path:
- '**/details_harness|arc:challenge|25_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|arc:challenge|25_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_09T13_36_28.958118
path:
- '**/details_harness|drop|3_2023-09-09T13-36-28.958118.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-09T13-36-28.958118.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_09T13_36_28.958118
path:
- '**/details_harness|gsm8k|5_2023-09-09T13-36-28.958118.parquet'
- split: 2023_12_02T15_52_11.848314
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-52-11.848314.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T15-52-11.848314.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hellaswag|10_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hellaswag|10_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T10:58:39.640665.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T10:58:39.640665.parquet'
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- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T10:58:39.640665.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-07-24T10:58:39.640665.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
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- '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T14:16:25.052724.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T14:16:25.052724.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T14:16:25.052724.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T14:16:25.052724.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T14:16:25.052724.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T14:16:25.052724.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T14:16:25.052724.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T14:16:25.052724.parquet'
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- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T14:16:25.052724.parquet'
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T14:16:25.052724.parquet'
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- split: latest
path:
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- '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T14:16:25.052724.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T14:16:25.052724.parquet'
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- '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T14:16:25.052724.parquet'
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- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T14:16:25.052724.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T14:16:25.052724.parquet'
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- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T14:16:25.052724.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T14:16:25.052724.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T14:16:25.052724.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T14:16:25.052724.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T14:16:25.052724.parquet'
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- '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T14:16:25.052724.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-01T14:16:25.052724.parquet'
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- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T10:58:39.640665.parquet'
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T10:58:39.640665.parquet'
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path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T10:58:39.640665.parquet'
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path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T10:58:39.640665.parquet'
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T10:58:39.640665.parquet'
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T10:58:39.640665.parquet'
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T10:58:39.640665.parquet'
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T10:58:39.640665.parquet'
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path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T10:58:39.640665.parquet'
- split: 2023_08_01T14_16_25.052724
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-01T14:16:25.052724.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-01T14:16:25.052724.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_09T13_36_28.958118
path:
- '**/details_harness|winogrande|5_2023-09-09T13-36-28.958118.parquet'
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path:
- '**/details_harness|winogrande|5_2023-09-09T13-36-28.958118.parquet'
- config_name: results
data_files:
- split: 2023_07_24T10_58_39.640665
path:
- results_2023-07-24T10:58:39.640665.parquet
- split: 2023_08_01T14_16_25.052724
path:
- results_2023-08-01T14:16:25.052724.parquet
- split: 2023_09_09T13_36_28.958118
path:
- results_2023-09-09T13-36-28.958118.parquet
- split: 2023_12_02T15_52_04.252951
path:
- results_2023-12-02T15-52-04.252951.parquet
- split: 2023_12_02T15_52_11.848314
path:
- results_2023-12-02T15-52-11.848314.parquet
- split: latest
path:
- results_2023-12-02T15-52-11.848314.parquet
---
# Dataset Card for Evaluation run of AlekseyKorshuk/pygmalion-6b-vicuna-chatml
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/AlekseyKorshuk/pygmalion-6b-vicuna-chatml
- **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 [AlekseyKorshuk/pygmalion-6b-vicuna-chatml](https://huggingface.co/AlekseyKorshuk/pygmalion-6b-vicuna-chatml) 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 5 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 aggregated 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_AlekseyKorshuk__pygmalion-6b-vicuna-chatml",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T15:52:11.848314](https://huggingface.co/datasets/open-llm-leaderboard/details_AlekseyKorshuk__pygmalion-6b-vicuna-chatml/blob/main/results_2023-12-02T15-52-11.848314.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": 0.04397270659590599,
"acc_stderr": 0.005647666449126458
},
"harness|gsm8k|5": {
"acc": 0.04397270659590599,
"acc_stderr": 0.005647666449126458
}
}
```
### 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_ajibawa-2023__carl-7b | open-llm-leaderboard | 2023-10-27T01:33:08Z | 400 | 0 | [
"region:us"
]
| null | 2023-10-27T01:33:01Z | ---
pretty_name: Evaluation run of ajibawa-2023/carl-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ajibawa-2023/carl-7b](https://huggingface.co/ajibawa-2023/carl-7b) 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_ajibawa-2023__carl-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-27T01:32:56.729607](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__carl-7b/blob/main/results_2023-10-27T01-32-56.729607.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\": 0.0350251677852349,\n\
\ \"em_stderr\": 0.001882728759888054,\n \"f1\": 0.09129823825503375,\n\
\ \"f1_stderr\": 0.002205658806843774,\n \"acc\": 0.35468739621810547,\n\
\ \"acc_stderr\": 0.008609150192858253\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0350251677852349,\n \"em_stderr\": 0.001882728759888054,\n\
\ \"f1\": 0.09129823825503375,\n \"f1_stderr\": 0.002205658806843774\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.02350265352539803,\n \
\ \"acc_stderr\": 0.004172883669643945\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6858721389108129,\n \"acc_stderr\": 0.01304541671607256\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ajibawa-2023/carl-7b
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_27T01_32_56.729607
path:
- '**/details_harness|drop|3_2023-10-27T01-32-56.729607.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-27T01-32-56.729607.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_27T01_32_56.729607
path:
- '**/details_harness|gsm8k|5_2023-10-27T01-32-56.729607.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-27T01-32-56.729607.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_27T01_32_56.729607
path:
- '**/details_harness|winogrande|5_2023-10-27T01-32-56.729607.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-27T01-32-56.729607.parquet'
- config_name: results
data_files:
- split: 2023_10_27T01_32_56.729607
path:
- results_2023-10-27T01-32-56.729607.parquet
- split: latest
path:
- results_2023-10-27T01-32-56.729607.parquet
---
# Dataset Card for Evaluation run of ajibawa-2023/carl-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ajibawa-2023/carl-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 [ajibawa-2023/carl-7b](https://huggingface.co/ajibawa-2023/carl-7b) 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_ajibawa-2023__carl-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T01:32:56.729607](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__carl-7b/blob/main/results_2023-10-27T01-32-56.729607.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": 0.0350251677852349,
"em_stderr": 0.001882728759888054,
"f1": 0.09129823825503375,
"f1_stderr": 0.002205658806843774,
"acc": 0.35468739621810547,
"acc_stderr": 0.008609150192858253
},
"harness|drop|3": {
"em": 0.0350251677852349,
"em_stderr": 0.001882728759888054,
"f1": 0.09129823825503375,
"f1_stderr": 0.002205658806843774
},
"harness|gsm8k|5": {
"acc": 0.02350265352539803,
"acc_stderr": 0.004172883669643945
},
"harness|winogrande|5": {
"acc": 0.6858721389108129,
"acc_stderr": 0.01304541671607256
}
}
```
### 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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|
fake_news_filipino | null | 2023-01-25T14:30:21Z | 399 | 0 | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:tl",
"license:unknown",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- tl
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: fake-news-filipino-dataset
pretty_name: Fake News Filipino
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: article
dtype: string
splits:
- name: train
num_bytes: 3623685
num_examples: 3206
download_size: 1313458
dataset_size: 3623685
---
# Dataset Card for Fake News Filipino
## 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:** [Fake News Filipino homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Repository:** [Fake News Filipino repository](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Paper:** [LREC 2020 paper](http://www.lrec-conf.org/proceedings/lrec2020/index.html)
- **Leaderboard:**
- **Point of Contact:** [Jan Christian Cruz](mailto:[email protected])
### Dataset Summary
Low-Resource Fake News Detection Corpora in Filipino. The first of its kind. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is primarily in Filipino, with the addition of some English words commonly used in Filipino vernacular.
## Dataset Structure
### Data Instances
Sample data:
```
{
"label": "0",
"article": "Sa 8-pahinang desisyon, pinaboran ng Sandiganbayan First Division ang petition for Writ of Preliminary Attachment/Garnishment na inihain ng prosekusyon laban sa mambabatas."
}
```
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
Fake news articles were sourced from online sites that were tagged as fake news sites by the non-profit independent media fact-checking organization Verafiles and the National Union of Journalists in the Philippines (NUJP). Real news articles were sourced from mainstream news websites in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera.
### Curation Rationale
We remedy the lack of a proper, curated benchmark dataset for fake news detection in Filipino by constructing and producing what we call “Fake News Filipino.”
### Source Data
#### Initial Data Collection and Normalization
We construct the dataset by scraping our source websites, encoding all characters into UTF-8. Preprocessing was light to keep information intact: we retain capitalization and punctuation, and do not correct any misspelled words.
#### Who are the source language producers?
Jan Christian Blaise Cruz, Julianne Agatha Tan, and Charibeth Cheng
### 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
[Jan Christian Cruz](mailto:[email protected]), Julianne Agatha Tan, and Charibeth Cheng
### Licensing Information
[More Information Needed]
### Citation Information
@inproceedings{cruz2020localization,
title={Localization of Fake News Detection via Multitask Transfer Learning},
author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
pages={2596--2604},
year={2020}
}
### Contributions
Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset. | [
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|
open-llm-leaderboard/details_aisquared__dlite-v2-124m | open-llm-leaderboard | 2023-10-27T09:27:32Z | 399 | 0 | [
"region:us"
]
| null | 2023-08-18T11:34:35Z | ---
pretty_name: Evaluation run of aisquared/dlite-v2-124m
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [aisquared/dlite-v2-124m](https://huggingface.co/aisquared/dlite-v2-124m) 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_aisquared__dlite-v2-124m\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-27T09:27:20.533537](https://huggingface.co/datasets/open-llm-leaderboard/details_aisquared__dlite-v2-124m/blob/main/results_2023-10-27T09-27-20.533537.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\": 0.0050335570469798654,\n\
\ \"em_stderr\": 0.0007247385547751906,\n \"f1\": 0.05289324664429539,\n\
\ \"f1_stderr\": 0.001460860471625635,\n \"acc\": 0.2521704814522494,\n\
\ \"acc_stderr\": 0.007025978032038446\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0050335570469798654,\n \"em_stderr\": 0.0007247385547751906,\n\
\ \"f1\": 0.05289324664429539,\n \"f1_stderr\": 0.001460860471625635\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5043409629044988,\n\
\ \"acc_stderr\": 0.014051956064076892\n }\n}\n```"
repo_url: https://huggingface.co/aisquared/dlite-v2-124m
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_53_19.147655
path:
- '**/details_harness|arc:challenge|25_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_27T09_27_20.533537
path:
- '**/details_harness|drop|3_2023-10-27T09-27-20.533537.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-27T09-27-20.533537.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_27T09_27_20.533537
path:
- '**/details_harness|gsm8k|5_2023-10-27T09-27-20.533537.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-27T09-27-20.533537.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hellaswag|10_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:53:19.147655.parquet'
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- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:53:19.147655.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:53:19.147655.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T13:53:19.147655.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T13:53:19.147655.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_27T09_27_20.533537
path:
- '**/details_harness|winogrande|5_2023-10-27T09-27-20.533537.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-27T09-27-20.533537.parquet'
- config_name: results
data_files:
- split: 2023_07_19T13_53_19.147655
path:
- results_2023-07-19T13:53:19.147655.parquet
- split: 2023_10_27T09_27_20.533537
path:
- results_2023-10-27T09-27-20.533537.parquet
- split: latest
path:
- results_2023-10-27T09-27-20.533537.parquet
---
# Dataset Card for Evaluation run of aisquared/dlite-v2-124m
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/aisquared/dlite-v2-124m
- **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 [aisquared/dlite-v2-124m](https://huggingface.co/aisquared/dlite-v2-124m) 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_aisquared__dlite-v2-124m",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T09:27:20.533537](https://huggingface.co/datasets/open-llm-leaderboard/details_aisquared__dlite-v2-124m/blob/main/results_2023-10-27T09-27-20.533537.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": 0.0050335570469798654,
"em_stderr": 0.0007247385547751906,
"f1": 0.05289324664429539,
"f1_stderr": 0.001460860471625635,
"acc": 0.2521704814522494,
"acc_stderr": 0.007025978032038446
},
"harness|drop|3": {
"em": 0.0050335570469798654,
"em_stderr": 0.0007247385547751906,
"f1": 0.05289324664429539,
"f1_stderr": 0.001460860471625635
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5043409629044988,
"acc_stderr": 0.014051956064076892
}
}
```
### 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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|
BeIR/beir-corpus | BeIR | 2022-10-21T15:30:07Z | 398 | 4 | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| [
"text-retrieval",
"zero-shot-retrieval",
"information-retrieval",
"zero-shot-information-retrieval"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## 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/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## 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
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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|
open-llm-leaderboard/details_facebook__opt-350m | open-llm-leaderboard | 2023-10-29T05:40:13Z | 398 | 0 | [
"region:us"
]
| null | 2023-08-18T11:09:59Z | ---
pretty_name: Evaluation run of facebook/opt-350m
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) 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 3 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_facebook__opt-350m\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-29T05:40:05.173534](https://huggingface.co/datasets/open-llm-leaderboard/details_facebook__opt-350m/blob/main/results_2023-10-29T05-40-05.173534.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\": 0.0006291946308724832,\n\
\ \"em_stderr\": 0.0002568002749723937,\n \"f1\": 0.04159815436241622,\n\
\ \"f1_stderr\": 0.0011509154641292957,\n \"acc\": 0.26473650543660054,\n\
\ \"acc_stderr\": 0.007773698717815887\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0006291946308724832,\n \"em_stderr\": 0.0002568002749723937,\n\
\ \"f1\": 0.04159815436241622,\n \"f1_stderr\": 0.0011509154641292957\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.003032600454890068,\n \
\ \"acc_stderr\": 0.0015145735612245468\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.526440410418311,\n \"acc_stderr\": 0.014032823874407229\n\
\ }\n}\n```"
repo_url: https://huggingface.co/facebook/opt-350m
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_24T09_55_50.700214
path:
- '**/details_harness|arc:challenge|25_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T09_18_34.693204
path:
- '**/details_harness|drop|3_2023-10-24T09-18-34.693204.parquet'
- split: 2023_10_29T05_40_05.173534
path:
- '**/details_harness|drop|3_2023-10-29T05-40-05.173534.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-29T05-40-05.173534.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T09_18_34.693204
path:
- '**/details_harness|gsm8k|5_2023-10-24T09-18-34.693204.parquet'
- split: 2023_10_29T05_40_05.173534
path:
- '**/details_harness|gsm8k|5_2023-10-29T05-40-05.173534.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-29T05-40-05.173534.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hellaswag|10_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T09:55:50.700214.parquet'
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- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T09:55:50.700214.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T09:55:50.700214.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_24T09_55_50.700214
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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- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T09:55:50.700214.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T09:55:50.700214.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T09_18_34.693204
path:
- '**/details_harness|winogrande|5_2023-10-24T09-18-34.693204.parquet'
- split: 2023_10_29T05_40_05.173534
path:
- '**/details_harness|winogrande|5_2023-10-29T05-40-05.173534.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-29T05-40-05.173534.parquet'
- config_name: results
data_files:
- split: 2023_07_24T09_55_50.700214
path:
- results_2023-07-24T09:55:50.700214.parquet
- split: 2023_10_24T09_18_34.693204
path:
- results_2023-10-24T09-18-34.693204.parquet
- split: 2023_10_29T05_40_05.173534
path:
- results_2023-10-29T05-40-05.173534.parquet
- split: latest
path:
- results_2023-10-29T05-40-05.173534.parquet
---
# Dataset Card for Evaluation run of facebook/opt-350m
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/facebook/opt-350m
- **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 [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) 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 3 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_facebook__opt-350m",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T05:40:05.173534](https://huggingface.co/datasets/open-llm-leaderboard/details_facebook__opt-350m/blob/main/results_2023-10-29T05-40-05.173534.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": 0.0006291946308724832,
"em_stderr": 0.0002568002749723937,
"f1": 0.04159815436241622,
"f1_stderr": 0.0011509154641292957,
"acc": 0.26473650543660054,
"acc_stderr": 0.007773698717815887
},
"harness|drop|3": {
"em": 0.0006291946308724832,
"em_stderr": 0.0002568002749723937,
"f1": 0.04159815436241622,
"f1_stderr": 0.0011509154641292957
},
"harness|gsm8k|5": {
"acc": 0.003032600454890068,
"acc_stderr": 0.0015145735612245468
},
"harness|winogrande|5": {
"acc": 0.526440410418311,
"acc_stderr": 0.014032823874407229
}
}
```
### 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_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model | open-llm-leaderboard | 2023-10-29T09:16:12Z | 398 | 0 | [
"region:us"
]
| null | 2023-08-27T11:52:41Z | ---
pretty_name: Evaluation run of TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model](https://huggingface.co/TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model)\
\ 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_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-29T09:16:00.873424](https://huggingface.co/datasets/open-llm-leaderboard/details_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model/blob/main/results_2023-10-29T09-16-00.873424.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\": 0.29247063758389263,\n\
\ \"em_stderr\": 0.004658574242541351,\n \"f1\": 0.34158871644295397,\n\
\ \"f1_stderr\": 0.004610159225684241,\n \"acc\": 0.40783419789572956,\n\
\ \"acc_stderr\": 0.009578253696730769\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.29247063758389263,\n \"em_stderr\": 0.004658574242541351,\n\
\ \"f1\": 0.34158871644295397,\n \"f1_stderr\": 0.004610159225684241\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06823351023502654,\n \
\ \"acc_stderr\": 0.006945358944067431\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7474348855564326,\n \"acc_stderr\": 0.012211148449394105\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model
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_24T06_39_02.499923
path:
- '**/details_harness|arc:challenge|25_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_29T09_16_00.873424
path:
- '**/details_harness|drop|3_2023-10-29T09-16-00.873424.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-29T09-16-00.873424.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_29T09_16_00.873424
path:
- '**/details_harness|gsm8k|5_2023-10-29T09-16-00.873424.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-29T09-16-00.873424.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hellaswag|10_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T06:39:02.499923.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_24T06_39_02.499923
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-24T06:39:02.499923.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-24T06:39:02.499923.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_29T09_16_00.873424
path:
- '**/details_harness|winogrande|5_2023-10-29T09-16-00.873424.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-29T09-16-00.873424.parquet'
- config_name: results
data_files:
- split: 2023_10_29T09_16_00.873424
path:
- results_2023-10-29T09-16-00.873424.parquet
- split: latest
path:
- results_2023-10-29T09-16-00.873424.parquet
---
# Dataset Card for Evaluation run of TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model
- **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 [TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model](https://huggingface.co/TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model) 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_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T09:16:00.873424](https://huggingface.co/datasets/open-llm-leaderboard/details_TaylorAI__FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model/blob/main/results_2023-10-29T09-16-00.873424.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": 0.29247063758389263,
"em_stderr": 0.004658574242541351,
"f1": 0.34158871644295397,
"f1_stderr": 0.004610159225684241,
"acc": 0.40783419789572956,
"acc_stderr": 0.009578253696730769
},
"harness|drop|3": {
"em": 0.29247063758389263,
"em_stderr": 0.004658574242541351,
"f1": 0.34158871644295397,
"f1_stderr": 0.004610159225684241
},
"harness|gsm8k|5": {
"acc": 0.06823351023502654,
"acc_stderr": 0.006945358944067431
},
"harness|winogrande|5": {
"acc": 0.7474348855564326,
"acc_stderr": 0.012211148449394105
}
}
```
### 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_Mikivis__gpt2-large-lora-sft | open-llm-leaderboard | 2023-10-27T18:03:55Z | 398 | 0 | [
"region:us"
]
| null | 2023-09-05T03:15:54Z | ---
pretty_name: Evaluation run of Mikivis/gpt2-large-lora-sft
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Mikivis/gpt2-large-lora-sft](https://huggingface.co/Mikivis/gpt2-large-lora-sft)\
\ 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_Mikivis__gpt2-large-lora-sft\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-27T18:03:42.739284](https://huggingface.co/datasets/open-llm-leaderboard/details_Mikivis__gpt2-large-lora-sft/blob/main/results_2023-10-27T18-03-42.739284.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\": 0.0026216442953020135,\n\
\ \"em_stderr\": 0.0005236685642965714,\n \"f1\": 0.05463401845637592,\n\
\ \"f1_stderr\": 0.001420933825490078,\n \"acc\": 0.27545382794001577,\n\
\ \"acc_stderr\": 0.006989729694570417\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0026216442953020135,\n \"em_stderr\": 0.0005236685642965714,\n\
\ \"f1\": 0.05463401845637592,\n \"f1_stderr\": 0.001420933825490078\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5509076558800315,\n\
\ \"acc_stderr\": 0.013979459389140834\n }\n}\n```"
repo_url: https://huggingface.co/Mikivis/gpt2-large-lora-sft
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_05T03_15_39.228135
path:
- '**/details_harness|arc:challenge|25_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_27T18_03_42.739284
path:
- '**/details_harness|drop|3_2023-10-27T18-03-42.739284.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-27T18-03-42.739284.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_27T18_03_42.739284
path:
- '**/details_harness|gsm8k|5_2023-10-27T18-03-42.739284.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-27T18-03-42.739284.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hellaswag|10_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T03:15:39.228135.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-05T03:15:39.228135.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-05T03:15:39.228135.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_27T18_03_42.739284
path:
- '**/details_harness|winogrande|5_2023-10-27T18-03-42.739284.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-27T18-03-42.739284.parquet'
- config_name: results
data_files:
- split: 2023_09_05T03_15_39.228135
path:
- results_2023-09-05T03:15:39.228135.parquet
- split: 2023_10_27T18_03_42.739284
path:
- results_2023-10-27T18-03-42.739284.parquet
- split: latest
path:
- results_2023-10-27T18-03-42.739284.parquet
---
# Dataset Card for Evaluation run of Mikivis/gpt2-large-lora-sft
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Mikivis/gpt2-large-lora-sft
- **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 [Mikivis/gpt2-large-lora-sft](https://huggingface.co/Mikivis/gpt2-large-lora-sft) 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_Mikivis__gpt2-large-lora-sft",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T18:03:42.739284](https://huggingface.co/datasets/open-llm-leaderboard/details_Mikivis__gpt2-large-lora-sft/blob/main/results_2023-10-27T18-03-42.739284.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": 0.0026216442953020135,
"em_stderr": 0.0005236685642965714,
"f1": 0.05463401845637592,
"f1_stderr": 0.001420933825490078,
"acc": 0.27545382794001577,
"acc_stderr": 0.006989729694570417
},
"harness|drop|3": {
"em": 0.0026216442953020135,
"em_stderr": 0.0005236685642965714,
"f1": 0.05463401845637592,
"f1_stderr": 0.001420933825490078
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5509076558800315,
"acc_stderr": 0.013979459389140834
}
}
```
### 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] | [
-0.4520045816898346,
-0.7377020716667175,
0.25111526250839233,
0.19574913382530212,
-0.22408263385295868,
0.05538714677095413,
-0.45299479365348816,
-0.23157866299152374,
0.45481395721435547,
0.5725128054618835,
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|
open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3 | open-llm-leaderboard | 2023-10-29T14:08:57Z | 398 | 0 | [
"region:us"
]
| null | 2023-09-13T22:12:42Z | ---
pretty_name: Evaluation run of Weyaxi/Luban-Marcoroni-13B-v3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/Luban-Marcoroni-13B-v3](https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3)\
\ 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_Weyaxi__Luban-Marcoroni-13B-v3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-29T14:08:44.787529](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3/blob/main/results_2023-10-29T14-08-44.787529.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\": 0.00776006711409396,\n\
\ \"em_stderr\": 0.0008986296432392762,\n \"f1\": 0.10252936241610805,\n\
\ \"f1_stderr\": 0.0019829740048614144,\n \"acc\": 0.4340313659926291,\n\
\ \"acc_stderr\": 0.010044205768767243\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.00776006711409396,\n \"em_stderr\": 0.0008986296432392762,\n\
\ \"f1\": 0.10252936241610805,\n \"f1_stderr\": 0.0019829740048614144\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09931766489764973,\n \
\ \"acc_stderr\": 0.008238371412683977\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.01185004012485051\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-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_13T22_12_25.570871
path:
- '**/details_harness|arc:challenge|25_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_29T14_08_44.787529
path:
- '**/details_harness|drop|3_2023-10-29T14-08-44.787529.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-29T14-08-44.787529.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_29T14_08_44.787529
path:
- '**/details_harness|gsm8k|5_2023-10-29T14-08-44.787529.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-29T14-08-44.787529.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hellaswag|10_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T22-12-25.570871.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-13T22-12-25.570871.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-13T22-12-25.570871.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_29T14_08_44.787529
path:
- '**/details_harness|winogrande|5_2023-10-29T14-08-44.787529.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-29T14-08-44.787529.parquet'
- config_name: results
data_files:
- split: 2023_09_13T22_12_25.570871
path:
- results_2023-09-13T22-12-25.570871.parquet
- split: 2023_10_29T14_08_44.787529
path:
- results_2023-10-29T14-08-44.787529.parquet
- split: latest
path:
- results_2023-10-29T14-08-44.787529.parquet
---
# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-13B-v3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-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 [Weyaxi/Luban-Marcoroni-13B-v3](https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3) 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_Weyaxi__Luban-Marcoroni-13B-v3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T14:08:44.787529](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3/blob/main/results_2023-10-29T14-08-44.787529.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": 0.00776006711409396,
"em_stderr": 0.0008986296432392762,
"f1": 0.10252936241610805,
"f1_stderr": 0.0019829740048614144,
"acc": 0.4340313659926291,
"acc_stderr": 0.010044205768767243
},
"harness|drop|3": {
"em": 0.00776006711409396,
"em_stderr": 0.0008986296432392762,
"f1": 0.10252936241610805,
"f1_stderr": 0.0019829740048614144
},
"harness|gsm8k|5": {
"acc": 0.09931766489764973,
"acc_stderr": 0.008238371412683977
},
"harness|winogrande|5": {
"acc": 0.7687450670876085,
"acc_stderr": 0.01185004012485051
}
}
```
### 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_Linly-AI__Chinese-LLaMA-2-7B-hf | open-llm-leaderboard | 2023-10-29T10:13:20Z | 398 | 0 | [
"region:us"
]
| null | 2023-10-01T14:35:42Z | ---
pretty_name: Evaluation run of Linly-AI/Chinese-LLaMA-2-7B-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Linly-AI/Chinese-LLaMA-2-7B-hf](https://huggingface.co/Linly-AI/Chinese-LLaMA-2-7B-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_Linly-AI__Chinese-LLaMA-2-7B-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-29T10:13:08.335270](https://huggingface.co/datasets/open-llm-leaderboard/details_Linly-AI__Chinese-LLaMA-2-7B-hf/blob/main/results_2023-10-29T10-13-08.335270.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\": 0.20123741610738255,\n\
\ \"em_stderr\": 0.004105848061320724,\n \"f1\": 0.24458682885905994,\n\
\ \"f1_stderr\": 0.00409620440356687,\n \"acc\": 0.38191288394439116,\n\
\ \"acc_stderr\": 0.009754960327281063\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.20123741610738255,\n \"em_stderr\": 0.004105848061320724,\n\
\ \"f1\": 0.24458682885905994,\n \"f1_stderr\": 0.00409620440356687\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0621683093252464,\n \
\ \"acc_stderr\": 0.006651035644531703\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7016574585635359,\n \"acc_stderr\": 0.012858885010030425\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Linly-AI/Chinese-LLaMA-2-7B-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_10_01T14_35_23.324449
path:
- '**/details_harness|arc:challenge|25_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_29T10_13_08.335270
path:
- '**/details_harness|drop|3_2023-10-29T10-13-08.335270.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-29T10-13-08.335270.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_29T10_13_08.335270
path:
- '**/details_harness|gsm8k|5_2023-10-29T10-13-08.335270.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-29T10-13-08.335270.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hellaswag|10_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-35-23.324449.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-35-23.324449.parquet'
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- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-35-23.324449.parquet'
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path:
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- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-35-23.324449.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-01T14-35-23.324449.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-01T14-35-23.324449.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_29T10_13_08.335270
path:
- '**/details_harness|winogrande|5_2023-10-29T10-13-08.335270.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-29T10-13-08.335270.parquet'
- config_name: results
data_files:
- split: 2023_10_01T14_35_23.324449
path:
- results_2023-10-01T14-35-23.324449.parquet
- split: 2023_10_29T10_13_08.335270
path:
- results_2023-10-29T10-13-08.335270.parquet
- split: latest
path:
- results_2023-10-29T10-13-08.335270.parquet
---
# Dataset Card for Evaluation run of Linly-AI/Chinese-LLaMA-2-7B-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Linly-AI/Chinese-LLaMA-2-7B-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 [Linly-AI/Chinese-LLaMA-2-7B-hf](https://huggingface.co/Linly-AI/Chinese-LLaMA-2-7B-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_Linly-AI__Chinese-LLaMA-2-7B-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T10:13:08.335270](https://huggingface.co/datasets/open-llm-leaderboard/details_Linly-AI__Chinese-LLaMA-2-7B-hf/blob/main/results_2023-10-29T10-13-08.335270.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": 0.20123741610738255,
"em_stderr": 0.004105848061320724,
"f1": 0.24458682885905994,
"f1_stderr": 0.00409620440356687,
"acc": 0.38191288394439116,
"acc_stderr": 0.009754960327281063
},
"harness|drop|3": {
"em": 0.20123741610738255,
"em_stderr": 0.004105848061320724,
"f1": 0.24458682885905994,
"f1_stderr": 0.00409620440356687
},
"harness|gsm8k|5": {
"acc": 0.0621683093252464,
"acc_stderr": 0.006651035644531703
},
"harness|winogrande|5": {
"acc": 0.7016574585635359,
"acc_stderr": 0.012858885010030425
}
}
```
### 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_Undi95__MLewd-ReMM-L2-Chat-20B-Inverted | open-llm-leaderboard | 2023-10-29T11:23:43Z | 398 | 0 | [
"region:us"
]
| null | 2023-10-08T21:13:29Z | ---
pretty_name: Evaluation run of Undi95/MLewd-ReMM-L2-Chat-20B-Inverted
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Undi95/MLewd-ReMM-L2-Chat-20B-Inverted](https://huggingface.co/Undi95/MLewd-ReMM-L2-Chat-20B-Inverted)\
\ 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_Undi95__MLewd-ReMM-L2-Chat-20B-Inverted\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-29T11:23:30.940403](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__MLewd-ReMM-L2-Chat-20B-Inverted/blob/main/results_2023-10-29T11-23-30.940403.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\": 0.04079278523489933,\n\
\ \"em_stderr\": 0.0020257579367794474,\n \"f1\": 0.12161703020134187,\n\
\ \"f1_stderr\": 0.002493984929248759,\n \"acc\": 0.4235474125060661,\n\
\ \"acc_stderr\": 0.009995123061460923\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.04079278523489933,\n \"em_stderr\": 0.0020257579367794474,\n\
\ \"f1\": 0.12161703020134187,\n \"f1_stderr\": 0.002493984929248759\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09097801364670205,\n \
\ \"acc_stderr\": 0.007921322844013656\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7561168113654302,\n \"acc_stderr\": 0.01206892327890819\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Undi95/MLewd-ReMM-L2-Chat-20B-Inverted
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_08T21_13_04.392733
path:
- '**/details_harness|arc:challenge|25_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_29T11_23_30.940403
path:
- '**/details_harness|drop|3_2023-10-29T11-23-30.940403.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-29T11-23-30.940403.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_29T11_23_30.940403
path:
- '**/details_harness|gsm8k|5_2023-10-29T11-23-30.940403.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-29T11-23-30.940403.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hellaswag|10_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-08T21-13-04.392733.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-08T21-13-04.392733.parquet'
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- '**/details_harness|hendrycksTest-econometrics|5_2023-10-08T21-13-04.392733.parquet'
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- '**/details_harness|hendrycksTest-global_facts|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-08T21-13-04.392733.parquet'
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- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-08T21-13-04.392733.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-10-08T21-13-04.392733.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-08T21-13-04.392733.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_29T11_23_30.940403
path:
- '**/details_harness|winogrande|5_2023-10-29T11-23-30.940403.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-29T11-23-30.940403.parquet'
- config_name: results
data_files:
- split: 2023_10_08T21_13_04.392733
path:
- results_2023-10-08T21-13-04.392733.parquet
- split: 2023_10_29T11_23_30.940403
path:
- results_2023-10-29T11-23-30.940403.parquet
- split: latest
path:
- results_2023-10-29T11-23-30.940403.parquet
---
# Dataset Card for Evaluation run of Undi95/MLewd-ReMM-L2-Chat-20B-Inverted
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Undi95/MLewd-ReMM-L2-Chat-20B-Inverted
- **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 [Undi95/MLewd-ReMM-L2-Chat-20B-Inverted](https://huggingface.co/Undi95/MLewd-ReMM-L2-Chat-20B-Inverted) 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_Undi95__MLewd-ReMM-L2-Chat-20B-Inverted",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T11:23:30.940403](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__MLewd-ReMM-L2-Chat-20B-Inverted/blob/main/results_2023-10-29T11-23-30.940403.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": 0.04079278523489933,
"em_stderr": 0.0020257579367794474,
"f1": 0.12161703020134187,
"f1_stderr": 0.002493984929248759,
"acc": 0.4235474125060661,
"acc_stderr": 0.009995123061460923
},
"harness|drop|3": {
"em": 0.04079278523489933,
"em_stderr": 0.0020257579367794474,
"f1": 0.12161703020134187,
"f1_stderr": 0.002493984929248759
},
"harness|gsm8k|5": {
"acc": 0.09097801364670205,
"acc_stderr": 0.007921322844013656
},
"harness|winogrande|5": {
"acc": 0.7561168113654302,
"acc_stderr": 0.01206892327890819
}
}
```
### 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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|
SUSTech/wildchat_zh | SUSTech | 2023-11-30T16:33:11Z | 398 | 0 | [
"region:us"
]
| null | 2023-11-30T07:45:58Z | ---
dataset_info:
features:
- name: conversation_id
dtype: string
- name: conversations
list:
- name: content
dtype: string
- name: language
dtype: string
- name: redacted
dtype: bool
- name: role
dtype: string
- name: toxic
dtype: bool
- name: model
dtype: string
splits:
- name: train
num_bytes: 424092565
num_examples: 104301
download_size: 211445530
dataset_size: 424092565
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
bazyl/GTSRB | bazyl | 2022-10-25T10:39:19Z | 397 | 0 | [
"task_categories:image-classification",
"task_ids:multi-label-image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"size_categories:10K<n<100K",
"source_datasets:original",
"license:gpl-3.0",
"region:us"
]
| [
"image-classification"
]
| 2022-06-25T00:30:19Z | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language: []
license:
- gpl-3.0
multilinguality: []
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-label-image-classification
pretty_name: GTSRB
---
# Dataset Card for GTSRB
## 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:** http://www.sciencedirect.com/science/article/pii/S0893608012000457
- **Repository:** https://github.com/bazylhorsey/gtsrb/
- **Paper:** Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
- **Leaderboard:** https://benchmark.ini.rub.de/gtsrb_results.html
- **Point of Contact:** [email protected]
### Dataset Summary
The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. Our benchmark has the following properties:
- Single-image, multi-class classification problem
- More than 40 classes
- More than 50,000 images in total
- Large, lifelike database
### Supported Tasks and Leaderboards
[Kaggle](https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign) \
[Original](https://benchmark.ini.rub.de/gtsrb_results.html)
## Dataset Structure
### Data Instances
```
{
"Width": 31,
"Height": 31,
"Roi.X1": 6,
"Roi.Y1": 6,
"Roi.X2": 26,
"Roi.Y2": 26,
"ClassId": 20,
"Path": "Train/20/00020_00004_00002.png",
}
```
### Data Fields
- Width: width of image
- Height: Height of image
- Roi.X1: Upper left X coordinate
- Roi.Y1: Upper left Y coordinate
- Roi.X2: Lower right t X coordinate
- Roi.Y2: Lower right Y coordinate
- ClassId: Class of image
- Path: Path of image
### Data Splits
Categories: 42
Train: 39209
Test: 12630
## Dataset Creation
### Curation Rationale
Recognition of traffic signs is a challenging real-world problem of high industrial relevance. Although commercial systems have reached the market and several studies on this topic have been published, systematic unbiased comparisons of different approaches are missing and comprehensive benchmark datasets are not freely available.
Traffic sign recognition is a multi-class classification problem with unbalanced class frequencies. Traffic signs can provide a wide range of variations between classes in terms of color, shape, and the presence of pictograms or text. However, there exist subsets of classes (e. g., speed limit signs) that are very similar to each other.
The classifier has to cope with large variations in visual appearances due to illumination changes, partial occlusions, rotations, weather conditions, etc.
Humans are capable of recognizing the large variety of existing road signs with close to 100% correctness. This does not only apply to real-world driving, which provides both context and multiple views of a single traffic sign, but also to the recognition from single images.
<!-- ### 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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|
C-MTEB/PAWSX | C-MTEB | 2023-07-28T13:43:08Z | 397 | 0 | [
"region:us"
]
| null | 2023-07-28T13:42:34Z | ---
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: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: int32
splits:
- name: train
num_bytes: 10420251
num_examples: 49401
- name: validation
num_bytes: 457128
num_examples: 2000
- name: test
num_bytes: 458674
num_examples: 2000
download_size: 8881168
dataset_size: 11336053
---
# Dataset Card for "PAWSX"
[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_frank098__WizardLM_13B_juniper | open-llm-leaderboard | 2023-10-29T18:19:07Z | 397 | 0 | [
"region:us"
]
| null | 2023-08-18T12:00:54Z | ---
pretty_name: Evaluation run of frank098/WizardLM_13B_juniper
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [frank098/WizardLM_13B_juniper](https://huggingface.co/frank098/WizardLM_13B_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__WizardLM_13B_juniper\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-29T18:18:55.569728](https://huggingface.co/datasets/open-llm-leaderboard/details_frank098__WizardLM_13B_juniper/blob/main/results_2023-10-29T18-18-55.569728.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\": 0.00576761744966443,\n\
\ \"em_stderr\": 0.0007755000442814698,\n \"f1\": 0.07442428691275203,\n\
\ \"f1_stderr\": 0.001635445995042788,\n \"acc\": 0.39574628120487826,\n\
\ \"acc_stderr\": 0.010113249922128762\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.00576761744966443,\n \"em_stderr\": 0.0007755000442814698,\n\
\ \"f1\": 0.07442428691275203,\n \"f1_stderr\": 0.001635445995042788\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0803639120545868,\n \
\ \"acc_stderr\": 0.007488258573239077\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7111286503551697,\n \"acc_stderr\": 0.012738241271018446\n\
\ }\n}\n```"
repo_url: https://huggingface.co/frank098/WizardLM_13B_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_24T12_54_22.349435
path:
- '**/details_harness|arc:challenge|25_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_29T18_18_55.569728
path:
- '**/details_harness|drop|3_2023-10-29T18-18-55.569728.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-29T18-18-55.569728.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_29T18_18_55.569728
path:
- '**/details_harness|gsm8k|5_2023-10-29T18-18-55.569728.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-29T18-18-55.569728.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hellaswag|10_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T12:54:22.349435.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T12:54:22.349435.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T12:54:22.349435.parquet'
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T12:54:22.349435.parquet'
- 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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- split: latest
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_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_24T12_54_22.349435
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
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:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T12:54:22.349435.parquet'
- split: latest
path:
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- 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_2023-07-24T12:54:22.349435.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_29T18_18_55.569728
path:
- '**/details_harness|winogrande|5_2023-10-29T18-18-55.569728.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-29T18-18-55.569728.parquet'
- config_name: results
data_files:
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path:
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- split: 2023_10_29T18_18_55.569728
path:
- results_2023-10-29T18-18-55.569728.parquet
- split: latest
path:
- results_2023-10-29T18-18-55.569728.parquet
---
# Dataset Card for Evaluation run of frank098/WizardLM_13B_juniper
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/frank098/WizardLM_13B_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/WizardLM_13B_juniper](https://huggingface.co/frank098/WizardLM_13B_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__WizardLM_13B_juniper",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T18:18:55.569728](https://huggingface.co/datasets/open-llm-leaderboard/details_frank098__WizardLM_13B_juniper/blob/main/results_2023-10-29T18-18-55.569728.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": 0.00576761744966443,
"em_stderr": 0.0007755000442814698,
"f1": 0.07442428691275203,
"f1_stderr": 0.001635445995042788,
"acc": 0.39574628120487826,
"acc_stderr": 0.010113249922128762
},
"harness|drop|3": {
"em": 0.00576761744966443,
"em_stderr": 0.0007755000442814698,
"f1": 0.07442428691275203,
"f1_stderr": 0.001635445995042788
},
"harness|gsm8k|5": {
"acc": 0.0803639120545868,
"acc_stderr": 0.007488258573239077
},
"harness|winogrande|5": {
"acc": 0.7111286503551697,
"acc_stderr": 0.012738241271018446
}
}
```
### 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_uukuguy__speechless-codellama-orca-airoboros-13b-0.10e | open-llm-leaderboard | 2023-12-03T19:30:52Z | 397 | 0 | [
"region:us"
]
| null | 2023-09-05T03:40:32Z | ---
pretty_name: Evaluation run of uukuguy/speechless-codellama-orca-airoboros-13b-0.10e
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [uukuguy/speechless-codellama-orca-airoboros-13b-0.10e](https://huggingface.co/uukuguy/speechless-codellama-orca-airoboros-13b-0.10e)\
\ 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 8 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 aggregated 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_uukuguy__speechless-codellama-orca-airoboros-13b-0.10e\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T19:30:46.049775](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-codellama-orca-airoboros-13b-0.10e/blob/main/results_2023-12-03T19-30-46.049775.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\": 0.0,\n \"\
acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \
\ \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/uukuguy/speechless-codellama-orca-airoboros-13b-0.10e
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_05T03_40_07.595318
path:
- '**/details_harness|arc:challenge|25_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|arc:challenge|25_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T02_12_43.735016
path:
- '**/details_harness|drop|3_2023-10-18T02-12-43.735016.parquet'
- split: 2023_10_28T09_11_54.446220
path:
- '**/details_harness|drop|3_2023-10-28T09-11-54.446220.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T09-11-54.446220.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T02_12_43.735016
path:
- '**/details_harness|gsm8k|5_2023-10-18T02-12-43.735016.parquet'
- split: 2023_10_28T09_11_54.446220
path:
- '**/details_harness|gsm8k|5_2023-10-28T09-11-54.446220.parquet'
- split: 2023_12_03T19_08_06.034957
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-08-06.034957.parquet'
- split: 2023_12_03T19_08_12.373310
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-08-12.373310.parquet'
- split: 2023_12_03T19_30_19.333310
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-30-19.333310.parquet'
- split: 2023_12_03T19_30_46.049775
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-30-46.049775.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-30-46.049775.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hellaswag|10_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hellaswag|10_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_5
data_files:
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path:
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- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T03:40:07.595318.parquet'
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- split: 2023_09_12T14_42_21.510480
path:
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- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T14-42-21.510480.parquet'
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- split: latest
path:
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- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T14-42-21.510480.parquet'
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- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T14-42-21.510480.parquet'
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- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T14-42-21.510480.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T14-42-21.510480.parquet'
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- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T14-42-21.510480.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T14-42-21.510480.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T03:40:07.595318.parquet'
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path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_05T03_40_07.595318
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-09-05T03:40:07.595318.parquet'
- split: 2023_09_12T14_42_21.510480
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-12T14-42-21.510480.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-12T14-42-21.510480.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_18T02_12_43.735016
path:
- '**/details_harness|winogrande|5_2023-10-18T02-12-43.735016.parquet'
- split: 2023_10_28T09_11_54.446220
path:
- '**/details_harness|winogrande|5_2023-10-28T09-11-54.446220.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T09-11-54.446220.parquet'
- config_name: results
data_files:
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path:
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- split: 2023_09_12T14_42_21.510480
path:
- results_2023-09-12T14-42-21.510480.parquet
- split: 2023_10_18T02_12_43.735016
path:
- results_2023-10-18T02-12-43.735016.parquet
- split: 2023_10_28T09_11_54.446220
path:
- results_2023-10-28T09-11-54.446220.parquet
- split: 2023_12_03T19_08_06.034957
path:
- results_2023-12-03T19-08-06.034957.parquet
- split: 2023_12_03T19_08_12.373310
path:
- results_2023-12-03T19-08-12.373310.parquet
- split: 2023_12_03T19_30_19.333310
path:
- results_2023-12-03T19-30-19.333310.parquet
- split: 2023_12_03T19_30_46.049775
path:
- results_2023-12-03T19-30-46.049775.parquet
- split: latest
path:
- results_2023-12-03T19-30-46.049775.parquet
---
# Dataset Card for Evaluation run of uukuguy/speechless-codellama-orca-airoboros-13b-0.10e
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/uukuguy/speechless-codellama-orca-airoboros-13b-0.10e
- **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 [uukuguy/speechless-codellama-orca-airoboros-13b-0.10e](https://huggingface.co/uukuguy/speechless-codellama-orca-airoboros-13b-0.10e) 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 8 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 aggregated 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_uukuguy__speechless-codellama-orca-airoboros-13b-0.10e",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:30:46.049775](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-codellama-orca-airoboros-13b-0.10e/blob/main/results_2023-12-03T19-30-46.049775.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": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### 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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]
|
HAERAE-HUB/HAE_RAE_BENCH | HAERAE-HUB | 2023-12-03T08:23:34Z | 397 | 4 | [
"task_categories:multiple-choice",
"language:ko",
"license:cc-by-nc-nd-4.0",
"arxiv:2309.02706",
"region:us"
]
| [
"multiple-choice"
]
| 2023-09-25T04:16:13Z | ---
license: cc-by-nc-nd-4.0
extra_gated_prompt: To request access to the dataset, please fill out this form, and
we'll review and let you know if your use case is approved.
extra_gated_fields:
First Name: text
Last Name: text
Institution: text
Intended Use: text
I agree to use this dataset for non-commercial research ONLY: checkbox
task_categories:
- multiple-choice
language:
- ko
configs:
- config_name: correct_definition_matching
data_files:
- split: test
path: "data/correct_definition_matching-00000-of-00001-65122a307e90d70a.parquet"
- config_name: csat_geo
data_files:
- split: test
path: "data/csat_geo-00000-of-00001-68ce37d4952173a9.parquet"
- config_name: csat_law
data_files:
- split: test
path: "data/csat_law-00000-of-00001-ef0ec0fac2f2238f.parquet"
- config_name: csat_socio
data_files:
- split: test
path: "data/csat_socio-00000-of-00001-0cfadd93827d0d48.parquet"
- config_name: date_understanding
data_files:
- split: test
path: "data/date_understanding-00000-of-00001-36887d05afd4c57f.parquet"
- config_name: general_knowledge
data_files:
- split: test
path: "data/general_knowledge-00000-of-00001-7418d1a0b8c4a615.parquet"
- config_name: history
data_files:
- split: test
path: "data/history-00000-of-00001-9ad551d475d58413.parquet"
- config_name: loan_words
data_files:
- split: test
path: "data/loan_words-00000-of-00001-96ed08632b9a2ae8.parquet"
- config_name: lyrics_denoising
data_files:
- split: test
path: "data/lyrics_denoising-00000-of-00001-ede1ce975e9bad54.parquet"
- config_name: proverbs_denoising
data_files:
- split: test
path: "data/proverbs_denoising-00000-of-00001-f274ad136676b2e3.parquet"
- config_name: rare_words
data_files:
- split: test
path: "data/rare_words-00000-of-00001-4f34314c09209524.parquet"
- config_name: standard_nomenclature
data_files:
- split: test
path: "data/standard_nomenclature-00000-of-00001-52814989a6843328.parquet"
- config_name: reading_comprehension
data_files:
- split: test
path: "data/reading_comprehension-00000-of-00001-f9c8df20c22e46d0.parquet"
---
<span style="color:red"> 🔴 IMPORTANT❗🔴 HAE-RAE is now LM-Eval supported❗ We are adding evaluation results to our documentation so let us know if you want your model to be added.</span>
The HAE_RAE_BENCH is an ongoing project to develop a suite of evaluation tasks designed to test the
understanding of models regarding Korean cultural and contextual nuances.
Currently, it comprises 13 distinct tasks, with a total of 4900 instances.
Please note that although this repository contains datasets from the original [HAE-RAE BENCH paper](https://arxiv.org/abs/2309.02706),
the contents are not completely identical. Specifically, the reading comprehension subset from the original version has been removed due to copyright constraints.
In its place, an updated reading comprehension subset has been introduced, sourced from the CSAT, the Korean university entrance examination.
To replicate the studies from the paper, please use this [code](https://github.com/EleutherAI/lm-evaluation-harness/blob/master/lm_eval/tasks/haerae.py).
For the latest version of the code, refer to [this](https://github.com/guijinSON/HAE-RAE-Bench.v2/blob/main/HAE_RAE_Bench_Evaluation.ipynb).
### Dataset Overview
| Task | Instances | Version | Explanation |
|-----------------------------|-----------|---------|---------------------------------------------------------------------|
| standard_nomenclature | 153 | v1.0 | Multiple-choice questions about Korean standard nomenclatures from NIKL. |
| loan_word | 169 | v1.0 | Multiple-choice questions about Korean loan words from NIKL. |
| rare_word | 405 | v1.0 | Multiple-choice questions about rare Korean words from NIKL. |
| general_knowledge | 176 | v1.0 | Multiple-choice questions on Korean cultural knowledge. |
| history | 188 | v1.0 | Multiple-choice questions on Korean history. |
| date_understanding | 475 | v1.1 | Multiple-choice questions about understanding Korean date expressions. |
| proverbs_denoising | 672 | v1.1 | Questions for generating Korean proverbs. |
| correct_definition_matching | 439 | v1.1 | Multiple-choice questions on matching the correct definition to words in context. |
| lyrics_denoising | 622 | v1.1 | Questions for reconstructing Korean pop song lyrics. |
| reading_comprehension | 936 | v1.1 | Multiple-choice questions on Korean reading comprehension from the CSAT. |
| csat_law | 217 | v1.1 | Multiple-choice questions on Korean law from the CSAT. |
| csat_geo | 150 | v1.1 | Multiple-choice questions on Korean geography from the CSAT. |
| csat_socio | 298 | v1.1 | Multiple-choice questions on Korean social studies from the CSAT. |
| **Total** | **4900** | | |
### Evaluation Results
| Models | correct_definition_matching| csat_geo | csat_law | csat_socio | date_understanding | general_knowledge | history | loan_words | reading_comprehension | rare_words | standard_nomenclature |
|----------|---------|----------|----------|----------|--------|--------|--------|--------|--------|--------|--------|
| daekeun-ml/Llama-2-ko-DPO-13B | 0.5421 | 0.1800 | 0.1613 | 0.2181 | 0.4905 | 0.3523 | 0.7500 | 0.8107 | 0.2382 | 0.6963 | 0.7908 |
### How to use (LM-Eval-Harness)
```Python
!git clone https://github.com/guijinSON/lm-evaluation-harness.git
!pip install sentencepiece
%cd lm-evaluation-harness
!pip install -e .
!pip install -e ".[multilingual]"
!pip install huggingface_hub
!python -c "from huggingface_hub.hf_api import HfFolder; HfFolder.save_token('<YOUR_HF_TOKEN>')"
!python main.py \
--model hf-causal \
--model_args pretrained=daekeun-ml/Llama-2-ko-DPO-13B,dtype=bfloat16\
--num_fewshot 1\
--batch_size 2\
--tasks hr2_cdm,hr2_cgeo,hr2_claw,hr2_csoc,hr2_du,hr2_gk,hr2_hi,hr2_lw,hr2_rw,hr2_rc,hr2_sn\
--alteration ""\
--device cuda:0
```
### Release Notes
__2023.12.03__: All errors fixed! 11 Tasks available via LM-Eval Harness refer to the code above to run evaluation. (List of Available Tasks: correct_definition_matching, csat_geo, csat_law, csat_socio, date_understanding, general_knowledge, history, loan_words, rare_words, reading_comprehension, standard_nomenclature)
__2023.11.06__: 3 tasks added (csat_geo, csat_law, csat_socio)
__2023.09.28__: [LM-Eval-Harness](https://github.com/EleutherAI/lm-evaluation-harness) support added for the following 8 tasks:
Loan Words, Rare Words, Standard Nomenclature, History, General Knowledge,correct_definition_matching, date_understanding,reading_comprehension.
Refer to the following [document](https://github.com/guijinSON/HAE-RAE-Bench.v2/blob/main/HAE_RAE_Bench_Evaluation.ipynb) to run the evaluation yourself.
__2023.09.16__: 10 tasks added, 5 from original HAE-RAE Bench(Loan Words, Rare Words, Standard Nomenclature, History, General Knowledge),
5 new tasks (correct_definition_matching, date_understanding, lyrics_denoising, proverbs_denoising, reading_comprehension)
### Point of Contact
For any questions contact us via the following email:)
```
[email protected]
``` | [
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|
open-llm-leaderboard/details_wtang06__mpt-125m-c4 | open-llm-leaderboard | 2023-12-02T13:54:34Z | 397 | 0 | [
"region:us"
]
| null | 2023-10-03T23:05:09Z | ---
pretty_name: Evaluation run of wtang06/mpt-125m-c4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [wtang06/mpt-125m-c4](https://huggingface.co/wtang06/mpt-125m-c4) 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 3 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 aggregated 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_wtang06__mpt-125m-c4\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:54:27.484252](https://huggingface.co/datasets/open-llm-leaderboard/details_wtang06__mpt-125m-c4/blob/main/results_2023-12-02T13-54-27.484252.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\": 0.0,\n \"\
acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \
\ \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/wtang06/mpt-125m-c4
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_03T23_04_52.221778
path:
- '**/details_harness|arc:challenge|25_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_28T21_05_22.097768
path:
- '**/details_harness|drop|3_2023-10-28T21-05-22.097768.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T21-05-22.097768.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T21_05_22.097768
path:
- '**/details_harness|gsm8k|5_2023-10-28T21-05-22.097768.parquet'
- split: 2023_12_02T13_54_27.484252
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-54-27.484252.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-54-27.484252.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hellaswag|10_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T23-04-52.221778.parquet'
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T23-04-52.221778.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T23-04-52.221778.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T23-04-52.221778.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T21_05_22.097768
path:
- '**/details_harness|winogrande|5_2023-10-28T21-05-22.097768.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T21-05-22.097768.parquet'
- config_name: results
data_files:
- split: 2023_10_03T23_04_52.221778
path:
- results_2023-10-03T23-04-52.221778.parquet
- split: 2023_10_28T21_05_22.097768
path:
- results_2023-10-28T21-05-22.097768.parquet
- split: 2023_12_02T13_54_27.484252
path:
- results_2023-12-02T13-54-27.484252.parquet
- split: latest
path:
- results_2023-12-02T13-54-27.484252.parquet
---
# Dataset Card for Evaluation run of wtang06/mpt-125m-c4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/wtang06/mpt-125m-c4
- **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 [wtang06/mpt-125m-c4](https://huggingface.co/wtang06/mpt-125m-c4) 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 3 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 aggregated 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_wtang06__mpt-125m-c4",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:54:27.484252](https://huggingface.co/datasets/open-llm-leaderboard/details_wtang06__mpt-125m-c4/blob/main/results_2023-12-02T13-54-27.484252.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": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### 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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|
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