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hard | null | 2023-01-25T14:31:26Z | 294 | 1 | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"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:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: hard
pretty_name: Hotel Arabic-Reviews Dataset
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: 27507085
num_examples: 105698
download_size: 8508677
dataset_size: 27507085
---
# Dataset Card for Hard
## 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:** [Hard](https://github.com/elnagara/HARD-Arabic-Dataset)
- **Repository:** [Hard](https://github.com/elnagara/HARD-Arabic-Dataset)
- **Paper:** [Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications](https://link.springer.com/chapter/10.1007/978-3-319-67056-0_3)
- **Point of Contact:** [Ashraf Elnagar]([email protected])
### Dataset Summary
This dataset contains 93,700 hotel reviews in Arabic language.The hotel reviews were collected from Booking.com website during June/July 2016.The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.The following table summarize some tatistics on the HARD Dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
A typical data point comprises a rating from 1 to 5 for hotels.
### Data Fields
[More Information Needed]
### Data Splits
The dataset is not split.
## 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
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. | [
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|
laroseda | null | 2022-11-18T20:18:11Z | 294 | 0 | [
"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:ro",
"license:cc-by-4.0",
"arxiv:2101.04197",
"arxiv:1901.06543",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- ro
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: null
pretty_name: LaRoSeDa
dataset_info:
features:
- name: index
dtype: string
- name: title
dtype: string
- name: content
dtype: string
- name: starRating
dtype: int64
config_name: laroseda
splits:
- name: train
num_bytes: 2932819
num_examples: 12000
- name: test
num_bytes: 700834
num_examples: 3000
download_size: 5257183
dataset_size: 3633653
---
# Dataset Card for LaRoSeDa
## 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://github.com/ancatache/LaRoSeDa)
- **Repository:** [Github](https://github.com/ancatache/LaRoSeDa)
- **Paper:** [Arxiv](https://arxiv.org/pdf/2101.04197.pdf)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [email protected]
### Dataset Summary
LaRoSeDa - A **La**rge and **Ro**manian **Se**ntiment **Da**ta Set. LaRoSeDa contains 15,000 reviews written in Romanian, of which 7,500 are positive and 7,500 negative.
The samples have one of four star ratings: 1 or 2 - for reviews that can be considered of negative polarity, and 4 or 5 for the positive ones.
The 15,000 samples featured in the corpus and labelled with the star rating, are splitted in a train and test subsets, with 12,000 and 3,000 samples in each subset.
### Supported Tasks and Leaderboards
[LiRo Benchmark and Leaderboard](https://eemlcommunity.github.io/ro_benchmark_leaderboard/site/)
### Languages
The text dataset is in Romanian (`ro`).
## Dataset Structure
### Data Instances
Below we have an example of sample from LaRoSeDa:
```
{
"index": "9675",
"title": "Nu recomand",
"content": "probleme cu localizarea, mari...",
"starRating": 1,
}
```
where "9675" is the sample index, followed by the title of the review, review content and then the star rating given by the user.
### Data Fields
- `index`: string, the unique indentifier of a sample.
- `title`: string, the review title.
- `content`: string, the content of the review.
- `starRating`: integer, with values in the following set {1, 2, 4, 5}.
### Data Splits
The train/test split contains 12,000/3,000 samples tagged with the star rating assigned to each sample in the dataset.
## Dataset Creation
### Curation Rationale
The samples are preprocessed in order to eliminate named entities. This is required to prevent classifiers from taking the decision based on features that are not related to the topics.
For example, named entities that refer to politicians or football players names can provide clues about the topic. For more details, please read the [paper](https://arxiv.org/abs/1901.06543).
### Source Data
#### Data Collection and Normalization
For the data collection, one of the largest Romanian e-commerce platform was targetted. Along with the textual content of each review, the associated star ratings was also collected in order to automatically assign labels to
the collected text samples.
#### Who are the source language producers?
The original text comes from one of the largest e-commerce platforms in Romania.
### Annotations
#### Annotation process
As mentioned above, LaRoSeDa is composed of product reviews from one of the largest e-commerce websites in Romania. The resulting samples are automatically tagged with the star rating assigned by the users.
#### Who are the annotators?
N/A
### Personal and Sensitive Information
The textual data collected for LaRoSeDa consists in product reviews freely available on the Internet.
To the best of authors' knowledge, there is no personal or sensitive information that needed to be considered in the said textual inputs collected.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is part of an effort to encourage text classification research in languages other than English. Such work increases the accessibility of natural language technology to more regions and cultures.
In the past three years there was a growing interest for studying Romanian from a Computational Linguistics perspective. However, we are far from having enough datasets and resources in this particular language.
### Discussion of Biases
*We note that most of the negative reviews (5,561) are rated with one star. Similarly, most of the positive reviews (6,238) are rated with five stars. Hence, the corpus is highly polarized.*
### Other Known Limitations
*The star rating might not always reflect the polarity of the text. We thus acknowledge that the automatic labeling process is not optimal, i.e. some labels might be noisy.*
## Additional Information
### Dataset Curators
Published and managed by Anca Tache, Mihaela Gaman and Radu Tudor Ionescu.
### Licensing Information
CC BY-SA 4.0 License
### Citation Information
```
@article{
tache2101clustering,
title={Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set},
author={Anca Maria Tache and Mihaela Gaman and Radu Tudor Ionescu},
journal={ArXiv},
year = {2021}
}
```
### Contributions
Thanks to [@MihaelaGaman](https://github.com/MihaelaGaman) for adding this dataset. | [
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|
moroco | null | 2023-01-25T14:40:41Z | 294 | 0 | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ro",
"license:cc-by-4.0",
"arxiv:1901.06543",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- ro
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: moroco
pretty_name: 'MOROCO: The Moldavian and Romanian Dialectal Corpus'
language_bcp47:
- ro-MD
dataset_info:
features:
- name: id
dtype: string
- name: category
dtype:
class_label:
names:
'0': culture
'1': finance
'2': politics
'3': science
'4': sports
'5': tech
- name: sample
dtype: string
config_name: moroco
splits:
- name: train
num_bytes: 39314292
num_examples: 21719
- name: test
num_bytes: 10877813
num_examples: 5924
- name: validation
num_bytes: 10721304
num_examples: 5921
download_size: 60711985
dataset_size: 60913409
---
# Dataset Card for MOROCO
## 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://github.com/butnaruandrei/MOROCO)
- **Repository:** [Github](https://github.com/butnaruandrei/MOROCO)
- **Paper:** [Arxiv](https://arxiv.org/abs/1901.06543)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [email]([email protected])
### Dataset Summary
Introducing MOROCO - The **Mo**ldavian and **Ro**manian Dialectal **Co**rpus. The MOROCO data set contains Moldavian and Romanian samples of text collected from the news domain. The samples belong to one of the following six topics: (0) culture, (1) finance, (2) politics, (3) science, (4) sports, (5) tech. The corpus features a total of 33,564 samples labelled with one of the fore mentioned six categories. We are also including a train/validation/test split with 21,719/5,921/5,924 samples in each subset.
### Supported Tasks and Leaderboards
[LiRo Benchmark and Leaderboard](https://eemlcommunity.github.io/ro_benchmark_leaderboard/site/)
### Languages
The text dataset is in Romanian (`ro`)
## Dataset Structure
### Data Instances
Below we have an example of sample from MOROCO:
```
{'id': , '48482',
'category': 2,
'sample': '“$NE$ cum am spus, nu este un sfârşit de drum . Vom continua lupta cu toate instrumentele şi cu toate mijloacele legale, parlamentare şi civice pe care le avem la dispoziţie . Evident că vom contesta la $NE$ această lege, au anunţat şi colegii de la $NE$ o astfel de contestaţie . Practic trebuie utilizat orice instrument pe care îl identificăm pentru a bloca intrarea în vigoare a acestei legi . Bineînţeles, şi preşedintele are punctul său de vedere . ( . . . ) $NE$ legi sunt împănate de motive de neconstituţionalitate . Colegii mei de la departamentul juridic lucrează în prezent pentru a definitiva textul contestaţiei”, a declarat $NE$ $NE$ citat de news . ro . Senatul a adoptat, marţi, în calitate de for decizional, $NE$ privind statutul judecătorilor şi procurorilor, cu 80 de voturi ”pentru” şi niciun vot ”împotrivă”, în condiţiile în care niciun partid din opoziţie nu a fost prezent în sală .',
}
```
where 48482 is the sample ID, followed by the category ground truth label, and then the text representing the actual content to be classified by topic.
Note: The category label has integer values ranging from 0 to 5.
### Data Fields
- `id`: string, the unique indentifier of a sample
- `category_label`: integer in the range [0, 5]; the category assigned to a sample.
- `sample`: a string, news report to be classified / used in classification.
### Data Splits
The train/validation/test split contains 21,719/5,921/5,924 samples tagged with the category assigned to each sample in the dataset.
## Dataset Creation
### Curation Rationale
The samples are preprocessed in order to eliminate named entities. This is required to prevent classifiers from taking the decision based on features that are not related to the topics.
For example, named entities that refer to politicians or football players names can provide clues about the topic. For more details, please read the [paper](https://arxiv.org/abs/1901.06543).
### Source Data
#### Data Collection and Normalization
For the data collection, five of the most popular news websites in Romania and the Republic of Moldova were targetted. Given that the data set was obtained through a web scraping technique, all the HTML tags needed to be removed, as well as replace consecutive white spaces with a single space.
As part of the pre-processing, we remove named entities, such as country names, cities, public figures, etc. The named entities have been replaced with $NE$. The necessity to remove them, comes also from the scope of this dataset: categorization by topic. Thus, the authors decided to remove named entities in order to prevent classifiers from taking the decision based on features that are not truly indicative of the topics.
#### Who are the source language producers?
The original text comes from news websites from Romania and the Republic of Moldova.
### Annotations
#### Annotation process
As mentioned above, MOROCO is composed of text samples from the top five most popular news websites in Romania and the Republic of Moldova, respectively. Since there are topic tags in the news websites targetd, the text samples can be automatically labeled with the corresponding category.
#### Who are the annotators?
N/A
### Personal and Sensitive Information
The textual data collected for MOROCO consists in news reports freely available on the Internet and of public interest.
To the best of authors' knowledge, there is no personal or sensitive information that needed to be considered in the said textual inputs collected.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is part of an effort to encourage text classification research in languages other than English. Such work increases the accessibility of natural language technology to more regions and cultures.
In the past three years there was a growing interest for studying Romanian from a Computational Linguistics perspective. However, we are far from having enough datasets and resources in this particular language.
### Discussion of Biases
The data included in MOROCO spans a well defined time frame of a few years. Part of the topics that were of interest then in the news landscape, might not show up nowadays or a few years from now in news websites.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Published and managed by Radu Tudor Ionescu and Andrei Butnaru.
### Licensing Information
CC BY-SA 4.0 License
### Citation Information
```
@inproceedings{ Butnaru-ACL-2019,
author = {Andrei M. Butnaru and Radu Tudor Ionescu},
title = "{MOROCO: The Moldavian and Romanian Dialectal Corpus}",
booktitle = {Proceedings of ACL},
year = {2019},
pages={688--698},
}
```
### Contributions
Thanks to [@MihaelaGaman](https://github.com/MihaelaGaman) for adding this dataset. | [
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|
erwanlc/cocktails_recipe | erwanlc | 2022-10-25T09:17:00Z | 294 | 3 | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:2M<n<3M",
"language:en",
"license:other",
"region:us"
]
| []
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 2M<n<3M
source_datasets: []
task_categories: []
task_ids: []
pretty_name: cocktails_recipe
language_bcp47:
- en
- en-US
---
# Dataset Card for cocktails_recipe
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains a list of cocktails and how to do them.
### Languages
The language is english.
## Dataset Structure
### Data Fields
- Title: name of the cocktail
- Glass: type of glass to use
- Garnish: garnish to use for the glass
- Recipe: how to do the cocktail
- Ingredients: ingredients required
### Data Splits
Currently, there is no splits.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The dataset was created by scraping the Diffords cocktail website.
### Personal and Sensitive Information
It should not contain any personal or sensitive information.
### Contributions
Thanks to [@github-erwanlc](https://github.com/erwanlc) for adding this dataset. | [
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|
eugenesiow/Set14 | eugenesiow | 2022-10-21T04:00:31Z | 294 | 0 | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"license:other",
"other-image-super-resolution",
"region:us"
]
| [
"other"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: Set14
tags:
- other-image-super-resolution
---
# Dataset Card for Set14
## 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/site/romanzeyde/research-interests
- **Repository**: https://huggingface.co/datasets/eugenesiow/Set14
- **Paper**: http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-get.cgi/2010/CS/CS-2010-12.pdf
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
Set14 is an evaluation dataset with 14 RGB images for the image super resolution task. It was first used as the test set of the paper "On single image scale-up using sparse-representations" by [Zeyde et al. (2010)](http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-get.cgi/2010/CS/CS-2010-12.pdf).
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/Set14', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `validation` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/Set14_HR/baboon.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/Set14_LR_x2/baboon.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |validation|
|-------|---:|
|bicubic_x2|14|
|bicubic_x3|14|
|bicubic_x4|14|
## 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
No annotations.
#### Who are the annotators?
No annotators.
### 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
- **Original Authors**: [Zeyde et al.](http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-get.cgi/2010/CS/CS-2010-12.pdf)
### Licensing Information
Academic use only.
### Citation Information
```bibtex
@inproceedings{zeyde2010single,
title={On single image scale-up using sparse-representations},
author={Zeyde, Roman and Elad, Michael and Protter, Matan},
booktitle={International conference on curves and surfaces},
pages={711--730},
year={2010},
organization={Springer}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
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|
mozilla-foundation/common_voice_7_0 | mozilla-foundation | 2023-07-29T16:00:09Z | 294 | 23 | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
]
| [
"automatic-speech-recognition"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
ab:
- 1K<n<10K
ar:
- 100K<n<1M
as:
- n<1K
az:
- n<1K
ba:
- 100K<n<1M
bas:
- 1K<n<10K
be:
- 100K<n<1M
bg:
- 1K<n<10K
br:
- 10K<n<100K
ca:
- 100K<n<1M
cnh:
- 1K<n<10K
cs:
- 10K<n<100K
cv:
- 10K<n<100K
cy:
- 100K<n<1M
de:
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dv:
- 10K<n<100K
el:
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en:
- 1M<n<10M
eo:
- 100K<n<1M
es:
- 100K<n<1M
et:
- 10K<n<100K
eu:
- 10K<n<100K
fa:
- 100K<n<1M
fi:
- 1K<n<10K
fr:
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fy-NL:
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ga-IE:
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gl:
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gn:
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ha:
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hi:
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hsb:
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hu:
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hy-AM:
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ia:
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id:
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it:
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ja:
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ka:
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source_datasets:
- extended|common_voice
paperswithcode_id: common-voice
pretty_name: Common Voice Corpus 7.0
language_bcp47:
- ab
- ar
- as
- az
- ba
- bas
- be
- bg
- br
- ca
- cnh
- cs
- cv
- cy
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy-NL
- ga-IE
- gl
- gn
- ha
- hi
- hsb
- hu
- hy-AM
- ia
- id
- it
- ja
- ka
- kab
- kk
- kmr
- ky
- lg
- lt
- lv
- mn
- mt
- nl
- or
- pa-IN
- pl
- pt
- rm-sursilv
- rm-vallader
- ro
- ru
- rw
- sah
- sk
- sl
- sr
- sv-SE
- ta
- th
- tr
- tt
- ug
- uk
- ur
- uz
- vi
- vot
- zh-CN
- zh-HK
- zh-TW
extra_gated_prompt: By clicking on “Access repository” below, you also agree to not
attempt to determine the identity of speakers in the Common Voice dataset.
task_categories:
- automatic-speech-recognition
---
# Dataset Card for Common Voice Corpus 7.0
## 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://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Anton Lozhkov](mailto:[email protected])
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 13905 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 11192 validated hours in 76 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
### Languages
```
Abkhaz, Arabic, Armenian, Assamese, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Breton, Bulgarian, Catalan, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Dhivehi, Dutch, English, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hindi, Hungarian, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Maltese, Mongolian, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swedish, Tamil, Tatar, Thai, Turkish, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_7_0", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## 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
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
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|
Sao10K/HarmfulQA_RedTeam_Only | Sao10K | 2023-11-18T11:45:28Z | 294 | 0 | [
"language:en",
"region:us"
]
| null | 2023-11-17T12:09:26Z | ---
language:
- en
---
I kept the red team entries only, and split the nested 4 different chats in 1 "conversation" field to their own entry each. (1 Question, 4 Different Conversations)
No other effort has been done to filter or rate or clean the entries. This is all I did.
Total:
<br>Topic: Social Sciences, Count: 841
<br>Topic: Mathematics and Logic, Count: 937
<br>Topic: Business and Economics, Count: 915
<br>Topic: Science and Technology, Count: 455
<br>Topic: Health and Medicine, Count: 415
<br>Topic: Geography and Environmental Studies, Count: 842
<br>Topic: Philosophy and Ethics, Count: 852
<br>Topic: Literature and Language, Count: 835
<br>Topic: Education and Pedagogy, Count: 577
<br>Topic: History and Culture, Count: 757
Original: https://huggingface.co/datasets/declare-lab/HarmfulQA | [
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|
myanmar_news | null | 2023-01-25T14:41:11Z | 293 | 1 | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:my",
"license:gpl-3.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- my
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: MyanmarNews
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype:
class_label:
names:
'0': Sport
'1': Politic
'2': Business
'3': Entertainment
splits:
- name: train
num_bytes: 3797368
num_examples: 8116
download_size: 610592
dataset_size: 3797368
---
# Dataset Card for Myanmar_News
## Dataset Description
- **Repository:** https://github.com/ayehninnkhine/MyanmarNewsClassificationSystem
### Dataset Summary
The Myanmar news dataset contains article snippets in four categories:
Business, Entertainment, Politics, and Sport.
These were collected in October 2017 by Aye Hninn Khine
### Languages
Myanmar/Burmese language
## Dataset Structure
### Data Fields
- text - text from article
- category - a topic: Business, Entertainment, **Politic**, or **Sport** (note spellings)
### Data Splits
One training set (8,116 total rows)
### Source Data
#### Initial Data Collection and Normalization
Data was collected by Aye Hninn Khine
and shared on GitHub with a GPL-3.0 license.
Multiple text files were consolidated into one labeled CSV file by Nick Doiron.
## Additional Information
### Dataset Curators
Contributors to original GitHub repo:
- https://github.com/ayehninnkhine
### Licensing Information
GPL-3.0
### Citation Information
See https://github.com/ayehninnkhine/MyanmarNewsClassificationSystem
### Contributions
Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset. | [
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|
refresd | null | 2023-01-25T14:43:11Z | 293 | 0 | [
"task_categories:text-classification",
"task_categories:translation",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|other-wikimatrix",
"language:en",
"language:fr",
"license:mit",
"arxiv:1907.05791",
"region:us"
]
| [
"text-classification",
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
- fr
license:
- mit
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-wikimatrix
task_categories:
- text-classification
- translation
task_ids:
- semantic-similarity-classification
- semantic-similarity-scoring
- text-scoring
paperswithcode_id: refresd
pretty_name: Rationalized English-French Semantic Divergences
dataset_info:
features:
- name: sentence_en
dtype: string
- name: sentence_fr
dtype: string
- name: label
dtype:
class_label:
names:
'0': divergent
'1': equivalent
- name: all_labels
dtype:
class_label:
names:
'0': unrelated
'1': some_meaning_difference
'2': no_meaning_difference
- name: rationale_en
dtype: string
- name: rationale_fr
dtype: string
splits:
- name: train
num_bytes: 501562
num_examples: 1039
download_size: 503977
dataset_size: 501562
---
# Dataset Card for REFreSD 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:** [Github](https://github.com/Elbria/xling-SemDiv/tree/master/REFreSD)
- **Repository:** [Github](https://github.com/Elbria/xling-SemDiv/)
- **Paper:** [Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank](https://www.aclweb.org/anthology/2020.emnlp-main.121)
- **Leaderboard:**
- **Point of Contact:** [Eleftheria Briakou](mailto:[email protected])
- **Additional Documentation:** [Annotation workflow, data statement, DataSheet, and IRB documentation](https://elbria.github.io/post/refresd/)
### Dataset Summary
The Rationalized English-French Semantic Divergences (REFreSD) dataset consists of 1,039 English-French sentence-pairs annotated with sentence-level divergence judgments and token-level rationales. The project under which REFreSD was collected aims to advance our fundamental understanding of computational representations and methods for comparing and contrasting text meaning across languages.
### Supported Tasks and Leaderboards
`semantic-similarity-classification` and `semantic-similarity-scoring`: This dataset can by used to assess the ability of computational methods to detect meaning mismatches between languages. The model performance is measured in terms of accuracy by comparing the model predictions with the human judgments in REFreSD. Details about the results of a BERT-based model, Divergent mBERT, over this dataset can be found in the [paper](https://www.aclweb.org/anthology/2020.emnlp-main.121).
### Languages
The text is in English and French as found on Wikipedia. The associated BCP-47 codes are `en` and `fr`.
## Dataset Structure
### Data Instances
Each data point looks like this:
```python
{
'sentence_pair': {'en': 'The invention of farming some 10,000 years ago led to the development of agrarian societies , whether nomadic or peasant , the latter in particular almost always dominated by a strong sense of traditionalism .',
'fr': "En quelques décennies , l' activité économique de la vallée est passée d' une mono-activité agricole essentiellement vivrière , à une quasi mono-activité touristique , si l' on excepte un artisanat du bâtiment traditionnel important , en partie saisonnier ."}
'label': 0,
'all_labels': 0,
'rationale_en': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'rationale_fr': [2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3],
}
```
### Data Fields
- `sentence_pair`: Dictionary of sentences containing the following field.
- `en`: The English sentence.
- `fr`: The corresponding (or not) French sentence.
- `label`: Binary. Whether both sentences correspond. `{0:divergent, 1:equivalent}`
- `all_labels`: 3-class label `{0: "unrelated", 1: "some_meaning_difference", 2:"no_meaning_difference"}`. The first two are sub-classes of the `divergent` label.
- `rationale_en`: A list of integers from 0-3 indicating the number of annotators who highlighted the token of the text in the English sentence during annotation. Word-aligned rationale for the divergent/equivalent label, from English.
- `rationale_fr`: A list of integers from 0-3 indicating the number of annotators who highlighted the token of the text in the French sentence during annotation. Word-aligned rationale for the divergent/equivalent label, from French.
### Data Splits
The dataset contains 1039 sentence pairs in a single `"train"` split. Of these pairs, 64% are annotated as divergent, and 40% contain fine-grained meaning divergences.
| Label | Number of Instances |
| ----------------------- | ------------------- |
| Unrelated | 252 |
| Some meaning difference | 418 |
| No meaning different | 369 |
## Dataset Creation
### Curation Rationale
The curators chose the English-French section of the WikiMatrix corpus because (1) it is likely to contain diverse, interesting divergence types since it consists of mined parallel sentences of diverse topics which are not necessarily generated by (human) translations, and (2) Wikipedia and WikiMatrix are widely used resources to train semantic representations and perform cross-lingual transfer in NLP.
### Source Data
#### Initial Data Collection and Normalization
The source for this corpus is the English and French portion of the [WikiMatrix corpus](https://arxiv.org/abs/1907.05791), which itself was extracted from Wikipedia articles. The curators excluded noisy samples by filtering out sentence pairs that a) were too short or too long, b) consisted mostly of numbers, or c) had a small token-level edit difference.
#### Who are the source language producers?
Some content of Wikipedia articles has been (human) translated from existing articles in another language while others have been written or edited independently in each language. Therefore, information on how the original text is created is not available.
### Annotations
#### Annotation process
The annotations were collected over the span of three weeks in April 2020. Annotators were presented with an English sentence and a French sentence. First, they highlighted spans and labeled them as 'added', 'changed', or 'other', where added spans contain information not contained in the other sentence, changed spans contain some information that is in the other sentence but whose meaning is not the same, and other spans have some different meaning not covered in the previous two cases, such as idioms. They then assessed the relation between the two sentences as either 'unrelated', 'some meaning differences', or 'no meaning difference'. See the [annotation guidelines](https://elbria.github.io/post/refresd/files/REFreSD_Annotation_Guidelines.pdf) for more information about the task and the annotation interface, and see the [DataSheet](https://elbria.github.io/post/refresd/files/REFreSD_Datasheet.pdf) for information about the annotator compensation.
The following table contains Inter-Annotator Agreement metrics for the dataset:
| Granularity | Method | IAA |
| ----------- | --------------- | ------------ |
| Sentence | Krippendorf's α | 0.60 |
| Span | macro F1 | 45.56 ± 7.60 |
| Token | macro F1 | 33.94 ± 8.24 |
#### Who are the annotators?
This dataset includes annotations from 6 participants recruited from the University of Maryland, College Park (UMD) educational institution. Participants ranged in age from 20–25 years, including one man and five women. For each participant, the curators ensured they were proficient in both languages of interest: three of them self-reported as English native speakers, one as a French native speaker, and two as bilingual English-French speakers.
### Personal and Sensitive Information
The dataset contains discussions of people as they appear in Wikipedia articles. It does not contain confidential information, nor does it contain identifying information about the source language producers or the annotators.
## Considerations for Using the Data
### Social Impact of Dataset
Models that are successful in the supported task require sophisticated semantic representations at the sentence level beyond the combined representations of the individual tokens in isolation. Such models could be used to curate parallel corpora for tasks like machine translation, cross-lingual transfer learning, or semantic modeling.
The statements in the dataset, however, are not necessarily representative of the world and may overrepresent one worldview if one language is primarily translated to, rather than an equal distribution of translations between the languages.
### Discussion of Biases
The English Wikipedia is known to have significantly more [contributors](https://en.wikipedia.org/wiki/Wikipedia:Who_writes_Wikipedia%3F) who identify as male than any other gender and who reside in either North America or Europe. This leads to an overrepresentation of male perspectives from these locations in the corpus in terms of both the topics covered and the language used to talk about those topics. It's not clear to what degree this holds true for the French Wikipedia. The REFreSD dataset itself has not yet been examined for the degree to which it contains the gender and other biases seen in the larger Wikipedia datasets.
### Other Known Limitations
It is unknown how many of the sentences in the dataset were written independently, and how many were written as [translations](https://en.wikipedia.org/wiki/Wikipedia:Translation) by either humans or machines from some other language to the languages of interest in this dataset.
## Additional Information
### Dataset Curators
The dataset curators are Eleftheria Briakou and Marine Carpuat, who are both affiliated with the University of Maryland, College Park's Department of Computer Science.
### Licensing Information
The project is licensed under the [MIT License](https://github.com/Elbria/xling-SemDiv/blob/master/LICENSE).
### Citation Information
```BibTeX
@inproceedings{briakou-carpuat-2020-detecting,
title = "Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank",
author = "Briakou, Eleftheria and Carpuat, Marine",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.121",
pages = "1563--1580",
}
```
### Contributions
Thanks to [@mpariente](https://github.com/mpariente) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset. | [
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|
ARTeLab/mlsum-it | ARTeLab | 2022-11-17T02:51:00Z | 293 | 1 | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"language:it",
"region:us"
]
| [
"summarization"
]
| 2022-03-02T23:29:22Z | ---
language:
- it
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
task_categories:
- summarization
---
# Dataset Card for mlsum-it
## 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:** [https://huggingface.co/datasets/mlsum]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The MLSum-it dataset is the translated version (Helsinki-NLP/opus-mt-es-it) of the spanish portion of MLSum, containing news articles taken from BBC/mundo.
More informations on the official dataset page [HuggingFace page](https://huggingface.co/datasets/mlsum).
There are two features:
- source: Input news article.
- target: Summary of the article.
### Supported Tasks and Leaderboards
- `abstractive-summarization`, `summarization`
### Languages
The text in the dataset is in Italian
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## 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
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
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0.06948109716176987,
0.009194495156407356,
0.8062975406646729,
-0.3084857761859894,
0.07579736411571503,
-0.41393259167671204,
0.18224984407424927,
-0.21048161387443542,
-0.3232065439224243,
-0.1293586939573288,
-0.025323400273919106,
-0.2409263402223587,
-0.07240606844425201
]
|
open-llm-leaderboard/details_Corianas__gpt-j-6B-Dolly | open-llm-leaderboard | 2023-09-23T01:24:03Z | 293 | 0 | [
"region:us"
]
| null | 2023-08-17T23:44:37Z | ---
pretty_name: Evaluation run of Corianas/gpt-j-6B-Dolly
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Corianas/gpt-j-6B-Dolly](https://huggingface.co/Corianas/gpt-j-6B-Dolly) 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_Corianas__gpt-j-6B-Dolly\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T01:23:52.389948](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__gpt-j-6B-Dolly/blob/main/results_2023-09-23T01-23-52.389948.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\": 0.0005236685642965902,\n \"f1\": 0.08095008389261764,\n\
\ \"f1_stderr\": 0.0017837058432559263,\n \"acc\": 0.32814795356315596,\n\
\ \"acc_stderr\": 0.008022527306282314\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0026216442953020135,\n \"em_stderr\": 0.0005236685642965902,\n\
\ \"f1\": 0.08095008389261764,\n \"f1_stderr\": 0.0017837058432559263\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.009097801364670205,\n \
\ \"acc_stderr\": 0.002615326510775673\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6471981057616417,\n \"acc_stderr\": 0.013429728101788956\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Corianas/gpt-j-6B-Dolly
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_19T15_40_52.841362
path:
- '**/details_harness|arc:challenge|25_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T01_23_52.389948
path:
- '**/details_harness|drop|3_2023-09-23T01-23-52.389948.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T01-23-52.389948.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T01_23_52.389948
path:
- '**/details_harness|gsm8k|5_2023-09-23T01-23-52.389948.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T01-23-52.389948.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hellaswag|10_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:40:52.841362.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T15:40:52.841362.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T15:40:52.841362.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T01_23_52.389948
path:
- '**/details_harness|winogrande|5_2023-09-23T01-23-52.389948.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T01-23-52.389948.parquet'
- config_name: results
data_files:
- split: 2023_07_19T15_40_52.841362
path:
- results_2023-07-19T15:40:52.841362.parquet
- split: 2023_09_23T01_23_52.389948
path:
- results_2023-09-23T01-23-52.389948.parquet
- split: latest
path:
- results_2023-09-23T01-23-52.389948.parquet
---
# Dataset Card for Evaluation run of Corianas/gpt-j-6B-Dolly
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Corianas/gpt-j-6B-Dolly
- **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 [Corianas/gpt-j-6B-Dolly](https://huggingface.co/Corianas/gpt-j-6B-Dolly) 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_Corianas__gpt-j-6B-Dolly",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T01:23:52.389948](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__gpt-j-6B-Dolly/blob/main/results_2023-09-23T01-23-52.389948.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": 0.0005236685642965902,
"f1": 0.08095008389261764,
"f1_stderr": 0.0017837058432559263,
"acc": 0.32814795356315596,
"acc_stderr": 0.008022527306282314
},
"harness|drop|3": {
"em": 0.0026216442953020135,
"em_stderr": 0.0005236685642965902,
"f1": 0.08095008389261764,
"f1_stderr": 0.0017837058432559263
},
"harness|gsm8k|5": {
"acc": 0.009097801364670205,
"acc_stderr": 0.002615326510775673
},
"harness|winogrande|5": {
"acc": 0.6471981057616417,
"acc_stderr": 0.013429728101788956
}
}
```
### 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_jerryjalapeno__nart-100k-7b | open-llm-leaderboard | 2023-09-23T00:09:53Z | 293 | 0 | [
"region:us"
]
| null | 2023-08-18T11:43:56Z | ---
pretty_name: Evaluation run of jerryjalapeno/nart-100k-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jerryjalapeno/nart-100k-7b](https://huggingface.co/jerryjalapeno/nart-100k-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_jerryjalapeno__nart-100k-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T00:09:38.629020](https://huggingface.co/datasets/open-llm-leaderboard/details_jerryjalapeno__nart-100k-7b/blob/main/results_2023-09-23T00-09-38.629020.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\": 0.09867869127516779,\n\
\ \"em_stderr\": 0.003054155613095924,\n \"f1\": 0.1502359479865761,\n\
\ \"f1_stderr\": 0.0031707924833711204,\n \"acc\": 0.3702237889195194,\n\
\ \"acc_stderr\": 0.008962759297749477\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.09867869127516779,\n \"em_stderr\": 0.003054155613095924,\n\
\ \"f1\": 0.1502359479865761,\n \"f1_stderr\": 0.0031707924833711204\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0356330553449583,\n \
\ \"acc_stderr\": 0.00510610785374419\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.7048145224940805,\n \"acc_stderr\": 0.012819410741754763\n\
\ }\n}\n```"
repo_url: https://huggingface.co/jerryjalapeno/nart-100k-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_24T11_14_45.628566
path:
- '**/details_harness|arc:challenge|25_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T00_09_38.629020
path:
- '**/details_harness|drop|3_2023-09-23T00-09-38.629020.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T00-09-38.629020.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T00_09_38.629020
path:
- '**/details_harness|gsm8k|5_2023-09-23T00-09-38.629020.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T00-09-38.629020.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hellaswag|10_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
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- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:14:45.628566.parquet'
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- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:14:45.628566.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:14:45.628566.parquet'
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path:
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- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:14:45.628566.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:14:45.628566.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:14:45.628566.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:14:45.628566.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:14:45.628566.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:14:45.628566.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T11:14:45.628566.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T11:14:45.628566.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T00_09_38.629020
path:
- '**/details_harness|winogrande|5_2023-09-23T00-09-38.629020.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T00-09-38.629020.parquet'
- config_name: results
data_files:
- split: 2023_07_24T11_14_45.628566
path:
- results_2023-07-24T11:14:45.628566.parquet
- split: 2023_09_23T00_09_38.629020
path:
- results_2023-09-23T00-09-38.629020.parquet
- split: latest
path:
- results_2023-09-23T00-09-38.629020.parquet
---
# Dataset Card for Evaluation run of jerryjalapeno/nart-100k-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jerryjalapeno/nart-100k-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 [jerryjalapeno/nart-100k-7b](https://huggingface.co/jerryjalapeno/nart-100k-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_jerryjalapeno__nart-100k-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T00:09:38.629020](https://huggingface.co/datasets/open-llm-leaderboard/details_jerryjalapeno__nart-100k-7b/blob/main/results_2023-09-23T00-09-38.629020.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": 0.09867869127516779,
"em_stderr": 0.003054155613095924,
"f1": 0.1502359479865761,
"f1_stderr": 0.0031707924833711204,
"acc": 0.3702237889195194,
"acc_stderr": 0.008962759297749477
},
"harness|drop|3": {
"em": 0.09867869127516779,
"em_stderr": 0.003054155613095924,
"f1": 0.1502359479865761,
"f1_stderr": 0.0031707924833711204
},
"harness|gsm8k|5": {
"acc": 0.0356330553449583,
"acc_stderr": 0.00510610785374419
},
"harness|winogrande|5": {
"acc": 0.7048145224940805,
"acc_stderr": 0.012819410741754763
}
}
```
### 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_elyza__ELYZA-japanese-Llama-2-7b | open-llm-leaderboard | 2023-09-17T01:33:00Z | 293 | 0 | [
"region:us"
]
| null | 2023-08-31T10:22:37Z | ---
pretty_name: Evaluation run of elyza/ELYZA-japanese-Llama-2-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [elyza/ELYZA-japanese-Llama-2-7b](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b)\
\ 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_elyza__ELYZA-japanese-Llama-2-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T01:32:48.448739](https://huggingface.co/datasets/open-llm-leaderboard/details_elyza__ELYZA-japanese-Llama-2-7b/blob/main/results_2023-09-17T01-32-48.448739.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0009437919463087249,\n\
\ \"em_stderr\": 0.00031446531194133837,\n \"f1\": 0.05525901845637585,\n\
\ \"f1_stderr\": 0.0013597649565375795,\n \"acc\": 0.4051552412586848,\n\
\ \"acc_stderr\": 0.010068835191488079\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0009437919463087249,\n \"em_stderr\": 0.00031446531194133837,\n\
\ \"f1\": 0.05525901845637585,\n \"f1_stderr\": 0.0013597649565375795\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08339651250947688,\n \
\ \"acc_stderr\": 0.007615650277106702\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7269139700078927,\n \"acc_stderr\": 0.012522020105869456\n\
\ }\n}\n```"
repo_url: https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|arc:challenge|25_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T01_32_48.448739
path:
- '**/details_harness|drop|3_2023-09-17T01-32-48.448739.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T01-32-48.448739.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T01_32_48.448739
path:
- '**/details_harness|gsm8k|5_2023-09-17T01-32-48.448739.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T01-32-48.448739.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hellaswag|10_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T10:22:13.739402.parquet'
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- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T10:22:13.739402.parquet'
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- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-31T10:22:13.739402.parquet'
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- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T10:22:13.739402.parquet'
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path:
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T10:22:13.739402.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
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path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T10:22:13.739402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
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- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-31T10:22:13.739402.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T01_32_48.448739
path:
- '**/details_harness|winogrande|5_2023-09-17T01-32-48.448739.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T01-32-48.448739.parquet'
- config_name: results
data_files:
- split: 2023_08_31T10_22_13.739402
path:
- results_2023-08-31T10:22:13.739402.parquet
- split: 2023_09_17T01_32_48.448739
path:
- results_2023-09-17T01-32-48.448739.parquet
- split: latest
path:
- results_2023-09-17T01-32-48.448739.parquet
---
# Dataset Card for Evaluation run of elyza/ELYZA-japanese-Llama-2-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b
- **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 [elyza/ELYZA-japanese-Llama-2-7b](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b) 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_elyza__ELYZA-japanese-Llama-2-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T01:32:48.448739](https://huggingface.co/datasets/open-llm-leaderboard/details_elyza__ELYZA-japanese-Llama-2-7b/blob/main/results_2023-09-17T01-32-48.448739.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0009437919463087249,
"em_stderr": 0.00031446531194133837,
"f1": 0.05525901845637585,
"f1_stderr": 0.0013597649565375795,
"acc": 0.4051552412586848,
"acc_stderr": 0.010068835191488079
},
"harness|drop|3": {
"em": 0.0009437919463087249,
"em_stderr": 0.00031446531194133837,
"f1": 0.05525901845637585,
"f1_stderr": 0.0013597649565375795
},
"harness|gsm8k|5": {
"acc": 0.08339651250947688,
"acc_stderr": 0.007615650277106702
},
"harness|winogrande|5": {
"acc": 0.7269139700078927,
"acc_stderr": 0.012522020105869456
}
}
```
### 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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|
arsentd_lev | null | 2023-01-25T14:26:36Z | 292 | 3 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:topic-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:apc",
"language:ajp",
"license:other",
"arxiv:1906.01830",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- apc
- ajp
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- topic-classification
paperswithcode_id: arsentd-lev
pretty_name: ArSenTD-LEV
dataset_info:
features:
- name: Tweet
dtype: string
- name: Country
dtype:
class_label:
names:
'0': jordan
'1': lebanon
'2': syria
'3': palestine
- name: Topic
dtype: string
- name: Sentiment
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
'3': very_negative
'4': very_positive
- name: Sentiment_Expression
dtype:
class_label:
names:
'0': explicit
'1': implicit
'2': none
- name: Sentiment_Target
dtype: string
splits:
- name: train
num_bytes: 1233980
num_examples: 4000
download_size: 392666
dataset_size: 1233980
---
# Dataset Card for ArSenTD-LEV
## 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:** [ArSenTD-LEV homepage](http://oma-project.com/)
- **Paper:** [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://arxiv.org/abs/1906.01830)
### Dataset Summary
The Arabic Sentiment Twitter Dataset for Levantine dialect (ArSenTD-LEV) contains 4,000 tweets written in Arabic and equally retrieved from Jordan, Lebanon, Palestine and Syria.
### Supported Tasks and Leaderboards
Sentriment analysis
### Languages
Arabic Levantine Dualect
## Dataset Structure
### Data Instances
{'Country': 0,
'Sentiment': 3,
'Sentiment_Expression': 0,
'Sentiment_Target': 'هاي سوالف عصابات ارهابية',
'Topic': 'politics',
'Tweet': 'ثلاث تفجيرات في #كركوك الحصيلة قتيل و 16 جريح بدأت اكلاوات كركوك كانت امان قبل دخول القوات العراقية ، هاي سوالف عصابات ارهابية'}
### Data Fields
`Tweet`: the text content of the tweet \
`Country`: the country from which the tweet was collected ('jordan', 'lebanon', 'syria', 'palestine')\
`Topic`: the topic being discussed in the tweet (personal, politics, religion, sports, entertainment and others) \
`Sentiment`: the overall sentiment expressed in the tweet (very_negative, negative, neutral, positive and very_positive) \
`Sentiment_Expression`: the way how the sentiment was expressed: explicit, implicit, or none (the latter when sentiment is neutral) \
`Sentiment_Target`: the segment from the tweet to which sentiment is expressed. If sentiment is neutral, this field takes the 'none' value.
### Data Splits
No standard splits are provided
## 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
Make sure to read and agree to the [license](http://oma-project.com/ArSenL/ArSenTD_Lev_Intro)
### Citation Information
```
@article{baly2019arsentd,
title={Arsentd-lev: A multi-topic corpus for target-based sentiment analysis in arabic levantine tweets},
author={Baly, Ramy and Khaddaj, Alaa and Hajj, Hazem and El-Hajj, Wassim and Shaban, Khaled Bashir},
journal={arXiv preprint arXiv:1906.01830},
year={2019}
}
```
### Contributions
Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset. | [
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|
hausa_voa_ner | null | 2023-01-25T14:31:51Z | 292 | 2 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ha",
"license:cc-by-4.0",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ha
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Hausa VOA NER Corpus
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-DATE
'8': I-DATE
config_name: hausa_voa_ner
splits:
- name: train
num_bytes: 483634
num_examples: 1015
- name: validation
num_bytes: 69673
num_examples: 146
- name: test
num_bytes: 139227
num_examples: 292
download_size: 324962
dataset_size: 692534
---
# Dataset Card for Hausa VOA NER Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.aclweb.org/anthology/2020.emnlp-main.204/
- **Repository:** [Hausa VOA NER](https://github.com/uds-lsv/transfer-distant-transformer-african/tree/master/data/hausa_ner)
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/
- **Leaderboard:**
- **Point of Contact:** [David Adelani](mailto:[email protected])
### Dataset Summary
The Hausa VOA NER is a named entity recognition (NER) dataset for Hausa language based on the [VOA Hausa news](https://www.voahausa.com/) corpus.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Hausa.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [B-PER, 0, 0, B-LOC, 0],
'tokens': ['Trump', 'ya', 'ce', 'Rasha', 'ma']
}
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & times (DATE). (O) is used for tokens not considered part of any named entity.
### Data Splits
Training (1,014 sentences), validation (145 sentences) and test split (291 sentences)
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Hausa.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset is based on the news domain and was crawled from [VOA Hausa news](https://www.voahausa.com/).
[More Information Needed]
#### Who are the source language producers?
The dataset was collected from VOA Hausa news. Most of the texts used in creating the Hausa VOA NER are news stories from Nigeria, Niger Republic, United States, and other parts of the world.
[More Information Needed]
### Annotations
Named entity recognition annotation
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated by Jesujoba Alabi and David Adelani for the paper:
[Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages](https://www.aclweb.org/anthology/2020.emnlp-main.204/).
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The annotated data sets were developed by students of Saarland University, Saarbrücken, Germany .
### Licensing Information
The data is under the [Creative Commons Attribution 4.0 ](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{hedderich-etal-2020-transfer,
title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on {A}frican Languages",
author = "Hedderich, Michael A. and
Adelani, David and
Zhu, Dawei and
Alabi, Jesujoba and
Markus, Udia and
Klakow, Dietrich",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.204",
doi = "10.18653/v1/2020.emnlp-main.204",
pages = "2580--2591",
}
```
### Contributions
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset. | [
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|
kor_sae | null | 2023-01-25T14:34:03Z | 292 | 3 | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ko",
"license:cc-by-sa-4.0",
"arxiv:1912.00342",
"arxiv:1811.04231",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
pretty_name: Structured Argument Extraction for Korean
dataset_info:
features:
- name: intent_pair1
dtype: string
- name: intent_pair2
dtype: string
- name: label
dtype:
class_label:
names:
'0': yes/no
'1': alternative
'2': wh- questions
'3': prohibitions
'4': requirements
'5': strong requirements
splits:
- name: train
num_bytes: 2885167
num_examples: 30837
download_size: 2545926
dataset_size: 2885167
---
# Dataset Card for Structured Argument Extraction for Korean
## 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:** [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k)
- **Repository:** [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k)
- **Paper:** [Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives](https://arxiv.org/abs/1912.00342)
- **Point of Contact:** [Won Ik Cho]([email protected])
### Dataset Summary
The Structured Argument Extraction for Korean dataset is a set of question-argument and command-argument pairs with their respective question type label and negativeness label. Often times, agents like Alexa or Siri, encounter conversations without a clear objective from the user. The goal of this dataset is to extract the intent argument of a given utterance pair without a clear directive. This may yield a more robust agent capable of parsing more non-canonical forms of speech.
### Supported Tasks and Leaderboards
* `intent_classification`: The dataset can be trained with a Transformer like [BERT](https://huggingface.co/bert-base-uncased) to classify the intent argument or a question/command pair in Korean, and it's performance can be measured by it's BERTScore.
### 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 question or command pair and its label:
```
{
"intent_pair1": "내일 오후 다섯시 조별과제 일정 추가해줘"
"intent_pair2": "내일 오후 다섯시 조별과제 일정 추가하기"
"label": 4
}
```
### Data Fields
* `intent_pair1`: a question/command pair
* `intent_pair2`: a corresponding question/command pair
* `label`: determines the intent argument of the pair and can be one of `yes/no` (0), `alternative` (1), `wh- questions` (2), `prohibitions` (3), `requirements` (4) and `strong requirements` (5)
### Data Splits
The corpus contains 30,837 examples.
## Dataset Creation
### Curation Rationale
The Structured Argument Extraction for Korean dataset was curated to help train models extract intent arguments from utterances without a clear objective or when the user uses non-canonical forms of speech. This is especially helpful in Korean because in English, the `Who, what, where, when and why` usually comes in the beginning, but this isn't necessarily the case in the Korean language. So for low-resource languages, this lack of data can be a bottleneck for comprehension performance.
### Source Data
#### Initial Data Collection and Normalization
The corpus was taken from the one constructed by [Cho et al.](https://arxiv.org/abs/1811.04231), a Korean single utterance corpus for identifying directives/non-directives that contains a wide variety of non-canonical directives.
#### Who are the source language producers?
Korean speakers are the source language producers.
### Annotations
#### Annotation process
Utterances were categorized as question or command arguments and then further classified according to their intent argument.
#### Who are the annotators?
The annotation was done by three Korean natives with a background in computational linguistics.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset is curated by Won Ik Cho, Young Ki Moon, Sangwhan Moon, Seok Min Kim and Nam Soo Kim.
### Licensing Information
The dataset is licensed under the CC BY-SA-4.0.
### Citation Information
```
@article{cho2019machines,
title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},
author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1912.00342},
year={2019}
}
```
### Contributions
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset. | [
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]
|
onestop_qa | null | 2023-01-25T14:42:12Z | 292 | 4 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"source_datasets:extended|onestop_english",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2004.14797",
"region:us"
]
| [
"question-answering"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
- extended|onestop_english
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: onestopqa
pretty_name: OneStopQA
language_bcp47:
- en-US
dataset_info:
features:
- name: title
dtype: string
- name: paragraph
dtype: string
- name: level
dtype:
class_label:
names:
'0': Adv
'1': Int
'2': Ele
- name: question
dtype: string
- name: paragraph_index
dtype: int32
- name: answers
sequence: string
length: 4
- name: a_span
sequence: int32
- name: d_span
sequence: int32
splits:
- name: train
num_bytes: 1423090
num_examples: 1458
download_size: 118173
dataset_size: 1423090
---
# Dataset Card for OneStopQA
## 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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [OneStopQA repository](https://github.com/berzak/onestop-qa)
- **Repository:** [OneStopQA repository](https://github.com/berzak/onestop-qa)
- **Paper:** [STARC: Structured Annotations for Reading Comprehension](https://arxiv.org/abs/2004.14797)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
OneStopQA is a multiple choice reading comprehension dataset annotated according to the STARC (Structured Annotations for Reading Comprehension) scheme. The reading materials are Guardian articles taken from the [OneStopEnglish corpus](https://github.com/nishkalavallabhi/OneStopEnglishCorpus). Each article comes in three difficulty levels, Elementary, Intermediate and Advanced. Each paragraph is annotated with three multiple choice reading comprehension questions. The reading comprehension questions can be answered based on any of the three paragraph levels.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English (`en-US`).
The original Guardian articles were manually converted from British to American English.
## Dataset Structure
### Data Instances
An example of instance looks as follows.
```json
{
"title": "101-Year-Old Bottle Message",
"paragraph": "Angela Erdmann never knew her grandfather. He died in 1946, six years before she was born. But, on Tuesday 8th April, 2014, she described the extraordinary moment when she received a message in a bottle, 101 years after he had lobbed it into the Baltic Sea. Thought to be the world’s oldest message in a bottle, it was presented to Erdmann by the museum that is now exhibiting it in Germany.",
"paragraph_index": 1,
"level": "Adv",
"question": "How did Angela Erdmann find out about the bottle?",
"answers": ["A museum told her that they had it",
"She coincidentally saw it at the museum where it was held",
"She found it in her basement on April 28th, 2014",
"A friend told her about it"],
"a_span": [56, 70],
"d_span": [16, 34]
}
```
Where,
| Answer | Description | Textual Span |
|--------|------------------------------------------------------------|-----------------|
| a | Correct answer. | Critical Span |
| b | Incorrect answer. A miscomprehension of the critical span. | Critical Span |
| c | Incorrect answer. Refers to an additional span. | Distractor Span |
| d | Incorrect answer. Has no textual support. | - |
The order of the answers in the `answers` list corresponds to the order of the answers in the table.
### Data Fields
- `title`: A `string` feature. The article title.
- `paragraph`: A `string` feature. The paragraph from the article.
- `paragraph_index`: An `int` feature. Corresponds to the paragraph index in the article.
- `question`: A `string` feature. The given question.
- `answers`: A list of `string` feature containing the four possible answers.
- `a_span`: A list of start and end indices (inclusive) of the critical span.
- `d_span`: A list of start and end indices (inclusive) of the distractor span.
*Span indices are according to word positions after whitespace tokenization.
**In the rare case where a span is spread over multiple sections,
the span list will contain multiple instances of start and stop indices in the format:
[start_1, stop_1, start_2, stop_2,...].
### Data Splits
Articles: 30
Paragraphs: 162
Questions: 486
Question-Paragraph Level pairs: 1,458
No preconfigured split is currently provided.
## 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
The annotation and piloting process of the dataset is described in Appendix A in
[STARC: Structured Annotations for Reading Comprehension](https://aclanthology.org/2020.acl-main.507.pdf).
#### 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
<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
### Citation Information
[STARC: Structured Annotations for Reading Comprehension](http://people.csail.mit.edu/berzak/papers/acl2020.pdf)
```
@inproceedings{starc2020,
author = {Berzak, Yevgeni and Malmaud, Jonathan and Levy, Roger},
title = {STARC: Structured Annotations for Reading Comprehension},
booktitle = {ACL},
year = {2020},
publisher = {Association for Computational Linguistics}
}
```
### Contributions
Thanks to [@scaperex](https://github.com/scaperex) for adding this dataset. | [
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|
per_sent | null | 2023-01-25T14:42:26Z | 292 | 0 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-MPQA-KBP Challenge-MediaRank",
"language:en",
"license:unknown",
"arxiv:2011.06128",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-MPQA-KBP Challenge-MediaRank
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: persent
pretty_name: PerSenT
dataset_info:
features:
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dtype: int64
- name: TITLE
dtype: string
- name: TARGET_ENTITY
dtype: string
- name: DOCUMENT
dtype: string
- name: MASKED_DOCUMENT
dtype: string
- name: TRUE_SENTIMENT
dtype:
class_label:
names:
'0': Negative
'1': Neutral
'2': Positive
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names:
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splits:
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num_bytes: 14595163
num_examples: 3355
- name: test_random
num_bytes: 2629500
num_examples: 579
- name: test_fixed
num_bytes: 3881800
num_examples: 827
- name: validation
num_bytes: 2322922
num_examples: 578
download_size: 23117196
dataset_size: 23429385
---
# Dataset Card for PerSenT
## 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:** [PerSenT](https://stonybrooknlp.github.io/PerSenT/)
- **Repository:** [https://github.com/MHDBST/PerSenT](https://github.com/MHDBST/PerSenT)
- **Paper:** [arXiv](https://arxiv.org/abs/2011.06128)
- **Leaderboard:** NA
- **Point of Contact:** [Mohaddeseh Bastan]([email protected])
### Dataset Summary
PerSenT is a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. This dataset contains annotations for 5.3k documents and 38k paragraphs covering 3.2k unique entities. For each article, annotators judge what the author’s sentiment is towards the main
(target) entity of the article. The annotations also include similar judgments on paragraphs within the article.
### Supported Tasks and Leaderboards
Sentiment Classification: Each document consists of multiple paragraphs. Each paragraph is labeled separately (Positive, Neutral, Negative) and the author’s sentiment towards the whole document is included as a document-level label.
### Languages
English
## Dataset Structure
### Data Instances
```json
{'DOCUMENT': "Germany's Landesbank Baden Wuertemberg won EU approval Tuesday for a state bailout after it promised to shrink its balance sheet by 40 percent and refocus on lending to companies.\n The bank was several state-owned German institutions to run into trouble last year after it ran up more huge losses from investing in high-risk proprietary trading and capital market activities -- a business the EU has now told it to shun.\n Seven current and former managers of the bank are also being investigated by German authorities for risking or damaging the bank's capital by carrying out or failing to block investments in high-risk deals worth hundreds of millions from 2006.\n The European Commission said its Tuesday approval for the state rescue of the bank and its new restructuring plan would allow it become a viable business again -- and that the cutbacks would help limit the unfair advantage over rivals that the bank would get from the state aid.\n Stuttgart-based LBBW earlier this year received a capital injection of (EURO)5 billion from the bank's shareholders all of them public authorities or state-owned including the state of Baden-Wuerttemberg the region's savings bank association and the city of Stuttgart.",
'DOCUMENT_INDEX': 1,
'MASKED_DOCUMENT': "[TGT] won EU approval Tuesday for a state bailout after it promised to shrink its balance sheet by 40 percent and refocus on lending to companies.\n [TGT] was several state-owned German institutions to run into trouble last year after [TGT] ran up more huge losses from investing in high-risk proprietary trading and capital market activities -- a business the EU has now told it to shun.\n Seven current and former managers of [TGT] are also being investigated by German authorities for risking or damaging [TGT]'s capital by carrying out or failing to block investments in high-risk deals worth hundreds of millions from 2006.\n The European Commission said its Tuesday approval for the state rescue of [TGT] and its new restructuring plan would allow it become a viable business again -- and that the cutbacks would help limit the unfair advantage over rivals that [TGT] would get from the state aid.\n Stuttgart-based LBBW earlier this year received a capital injection of (EURO)5 billion from [TGT]'s shareholders all of them public authorities or state-owned including the state of Baden-Wuerttemberg the region's savings bank association and the city of Stuttgart.",
'Paragraph0': 2,
'Paragraph1': 0,
'Paragraph10': -1,
'Paragraph11': -1,
'Paragraph12': -1,
'Paragraph13': -1,
'Paragraph14': -1,
'Paragraph15': -1,
'Paragraph2': 0,
'Paragraph3': 1,
'Paragraph4': 1,
'Paragraph5': -1,
'Paragraph6': -1,
'Paragraph7': -1,
'Paragraph8': -1,
'Paragraph9': -1,
'TARGET_ENTITY': 'Landesbank Baden Wuertemberg',
'TITLE': 'German bank LBBW wins EU bailout approval',
'TRUE_SENTIMENT': 0}
```
### Data Fields
- DOCUMENT_INDEX: ID of the document per original dataset
- TITLE: Title of the article
- DOCUMENT: Text of the article
- MASKED_DOCUMENT: Text of the article with the target entity masked with `[TGT]` token
- TARGET_ENTITY: The entity that the author is expressing opinion about
- TRUE_SENTIMENT: Label for entire article
- Paragraph{0..15}: Label for each paragraph in the article
**Note**: Labels are one of `[Negative, Neutral, Positive]`. Missing labels were replaced with `-1`.
### Data Splits
To split the dataset, entities were split into 4 mutually exclusive sets. Due to the nature of news collections, some entities tend to dominate the collection. In the collection, there were four entities which were the main entity in nearly 800 articles. To avoid these entities from dominating the train or test splits, these were moved them to a separate test collection. The remaining was split into a training, dev, and test sets at random. Thus the collection includes one standard test set consisting of articles drawn at random (Test Standard), while the other is a test set which contains multiple articles about a small number of popular entities (Test Frequent).
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Articles were selected from 3 sources:
1. MPQA (Deng and Wiebe, 2015; Wiebe et al., 2005): This dataset contains news articles manually annotated for opinions, beliefs, emotions, sentiments, speculations, etc. It also has target annotations which are entities and event anchored to the heads of noun or verb phrases. All decisions on this dataset are made on sentence-level and over short spans.
2. KBP Challenge (Ellis et al., 2014): This resource contains TAC 2014 KBP English sentiment slot filling challenge dataset. This is a document-level sentiment filling dataset. In this task, given an entity and a sentiment (positive/negative) from the document, the goal is to find entities toward which
the original entity holds the given sentimental view. We selected documents from this resource which have been used in the following similar work in sentiment analysis task (Choi et al., 2016).
3. Media Rank (Ye and Skiena, 2019): This dataset ranks about 50k news sources along different aspects. It is also used for classifying political ideology of news articles (Kulkarni et al., 2018).
Pre-processing steps:
- First we find all the person entities in each article, using Stanford NER (Name Entity Resolution) tagger (Finkel et al., 2005) and all mentions of them using co-reference resolution (Clark and Manning, 2016; Co, 2017).
- We removed articles which are not likely to have a main entity of focus. We used a simple heuristic of removing articles in which the most frequent person entity is mentioned only three times or less (even when counting co-referent mentions).
- For the articles that remain we deemed the most frequent entity to be the main entity of the article. We also filtered out extremely long and extremely short articles to keep the articles which have at least 3 paragraphs and at most 16 paragraphs.
Documents are randomly separated into train, dev, and two test sets. We ensure that each entity appears in only one of the sets. Our goal here is to avoid easy to learn biases over entities. To avoid the most frequent entities from dominating the training or the test sets, we remove articles that covered the most frequent entities and use them as a separate test set (referred to as frequent test set) in addition to the randomly drawn standard test set.
### Annotations
#### Annotation process
We obtained document and paragraph level annotations with the help of Amazon Mechanical Turk workers. The workers first verified if the target entity we provide is indeed the main entity in the document. Then, they rated each paragraph in a document that contained a direct mention or a reference to the target
entity. Last, they rated the sentiment towards the entity based on the entire document. In both cases, the workers made assessments about the authors view based on what they said about the target entity. For both paragraph and document level sentiment, the workers chose from five rating categories: Negative,
Slightly Negative, Neutral, Slightly Positive, or Positive. We then combine the fine-grained annotations to obtain three coarse-grained classes Negative, Neutral, or Positive.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
[More Information Needed]
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{bastan2020authors,
title={Author's Sentiment Prediction},
author={Mohaddeseh Bastan and Mahnaz Koupaee and Youngseo Son and Richard Sicoli and Niranjan Balasubramanian},
year={2020},
eprint={2011.06128},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@jeromeku](https://github.com/jeromeku) for adding this dataset. | [
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|
thai_toxicity_tweet | null | 2023-01-25T14:45:38Z | 292 | 2 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:th",
"license:cc-by-nc-3.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- cc-by-nc-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: ThaiToxicityTweet
dataset_info:
features:
- name: tweet_id
dtype: string
- name: tweet_text
dtype: string
- name: toxic_votes
dtype: int32
- name: nontoxic_votes
dtype: int32
- name: is_toxic
dtype:
class_label:
names:
'0': neg
'1': pos
config_name: thai_toxicity_tweet
splits:
- name: train
num_bytes: 637387
num_examples: 3300
download_size: 194740
dataset_size: 637387
---
# Dataset Card for `thai_toxicity_tweet`
## 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/tmu-nlp/ThaiToxicityTweetCorpus/
- **Repository:** https://github.com/tmu-nlp/ThaiToxicityTweetCorpus/
- **Paper:** https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf
- **Leaderboard:**
- **Point of Contact:** https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf
### Dataset Summary
Thai Toxicity Tweet Corpus contains 3,300 tweets (506 tweets with texts missing) annotated by humans with guidelines including a 44-word dictionary.
The author obtained 2,027 and 1,273 toxic and non-toxic tweets, respectively; these were labeled by three annotators. The result of corpus
analysis indicates that tweets that include toxic words are not always toxic. Further, it is more likely that a tweet is toxic, if it contains
toxic words indicating their original meaning. Moreover, disagreements in annotation are primarily because of sarcasm, unclear existing
target, and word sense ambiguity.
Notes from data cleaner: The data is included into [huggingface/datasets](https://www.github.com/huggingface/datasets) in Dec 2020. By this time, 506 of the tweets are not available publicly anymore. We denote these by `TWEET_NOT_FOUND` in `tweet_text`.
Processing can be found at [this PR](https://github.com/tmu-nlp/ThaiToxicityTweetCorpus/pull/1).
### Supported Tasks and Leaderboards
text classification
### Languages
Thai (`th`)
## Dataset Structure
### Data Instances
```
{'is_toxic': 0, 'nontoxic_votes': 3, 'toxic_votes': 0, 'tweet_id': '898576382384418817', 'tweet_text': 'วันๆ นี่คุยกะหมา แมว หมู ไก่ ม้า ควาย มากกว่าคุยกับคนไปละ'}
{'is_toxic': 1, 'nontoxic_votes': 0, 'toxic_votes': 3, 'tweet_id': '898573084981985280', 'tweet_text': 'ควายแดงเมิงด่ารัฐบาลจนรองนายกป่วย พวกมึงกำลังทำลายชาติรู้มั้ย มั้ย มั้ย มั้ยยยยยยยยย news.voicetv.co.th/thailand/51672…'}
```
### Data Fields
"tweet_id": Id of tweet on Twitter
"tweet_text": text of the tweet
"toxic_votes": how many annotators say it is toxic, out of 3 annotators
"nontoxic_votes": how many annotators say it is NOT toxic, out of 3 annotators
"is_toxic": 1 if tweet is toxic else 0 (majority rules)
### Data Splits
No explicit split is given.
## Dataset Creation
### Curation Rationale
The dataset is created as part of [Sirihattasak et al (2019)](https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf).
### Source Data
#### Initial Data Collection and Normalization
The authors used the public Twitter Search API to collect 9,819 tweets from January–December 2017 based on our keyword dictionary. Then, they selected 75 tweets for each keyword. In total, they collected 3,300 tweets for annotation. To ensure quality of data, they set the following selection criteria.
1. All tweets are selected by humans to prevent word ambiguity. (The Twitter API selected the tweets based on characters in the keyword. For example, in the case of “บ้า(crazy),” the API will also select “บ้านนอก” (countryside)” which is not our target.)
2. The length of the tweet should be sufficiently long to discern the context of the tweet. Hence, they set five words as the minimum limit.
3. The tweets that contain only extremely toxic words, (for example: “damn, retard, bitch, f*ck, slut!!!”) are not considered.
4. In addition, they allowed tweets with English words if they were not critical elements in the labeling decision, for example, the word “f*ck.” As a result, our corpus contains English words, but they are less than 2% of the total.
All hashtags, re-tweets, and links were removed from these tweets. However, they did not delete emoticons because these emotional icons can imply the real intent of the post owners. Furthermore, only in the case of annotation, some entries such as the names of famous people were replaced with a tag <ไม่ขอเปิดเผยชื่อ>, for anonymity to prevent individual bias.
#### Who are the source language producers?
Twitter users in Thailand
### Annotations
#### Annotation process
We manually annotated our dataset with two labels: Toxic and Non-Toxic. We define a message as toxic if it indicates any harmful, damage, or negative intent based on our definition of toxicity. Furthermore, all the tweets were annotated by three annotators to identify toxicity; the conditions used for this identification are presented in the following list.
- A toxic message is a message that should be deleted or not be allowed in public.
- A message’s target or consequence must exist. It can either be an individual or a generalized group based on a commonality such as religion or ethnicity, or an entire community.
- Self-complain is not considered toxic, because it is not harmful to anyone. However, if self-complain is intended to indicate something bad, it will be considered as toxic.
- Both direct and indirect messages including those with sarcasm are taken into consideration.
We strictly instructed all the annotators about these concepts and asked them to perform a small test to ensure they understood these conditions. The annotation process was divided into two rounds. We asked the candidates to annotate their answers in the first round to learn our annotation standard. Then, we asked them to annotate a different dataset and selected the ones who obtained a full-score for the second round as an annotator. From among these annotators, 20% of the annotators failed the first round and were not involved in the final annotation.
#### Who are the annotators?
Three annotators hired by [Sirihattasak et al (2019)](https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf)
### Personal and Sensitive Information
Despite all tweets being public, due to the nature of toxic tweets, there might be personal attacks and toxic language used.
## Considerations for Using the Data
### Social Impact of Dataset
- toxic social media message classification dataset
### Discussion of Biases
- Users are masked before annotation by the annotators to prevent biases based on tweet authors
### Other Known Limitations
- The data is included into [huggingface/datasets](https://www.github.com/huggingface/datasets) in Dec 2020. By this time, 506 of the tweets are not available publicly anymore. We denote these by `TWEET_NOT_FOUND` in `tweet_text`.
## Additional Information
### Dataset Curators
[Sirihattasak et al (2019)](https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf)
### Licensing Information
CC-BY-NC 3.0
### Citation Information
Please cite the following if you make use of the dataset:
```
@article{sirihattasak2019annotation,
title={Annotation and Classification of Toxicity for Thai Twitter},
author={Sirihattasak, Sugan and Komachi, Mamoru and Ishikawa, Hiroshi},
year={2019}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | [
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|
SocialGrep/one-million-reddit-questions | SocialGrep | 2022-07-25T18:57:10Z | 292 | 3 | [
"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-million-reddit-questions
## 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=onemillionquestions)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=dataset&utm_term=onemillionquestions)
### Dataset Summary
This corpus contains a million posts on /r/AskReddit, annotated with their score.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a Reddit post.
### 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': the domain of the data point's link.
- 'url': the destination of the data point's link, if any.
- 'selftext': the self-text of the data point, if any.
- 'title': the title of the post data point.
## 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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|
daekeun-ml/naver-news-summarization-ko | daekeun-ml | 2023-01-10T11:12:44Z | 292 | 14 | [
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:ko",
"license:apache-2.0",
"region:us"
]
| [
"summarization"
]
| 2022-08-01T14:54:17Z | ---
license: apache-2.0
task_categories:
- summarization
language:
- ko
size_categories:
- 10K<n<100K
---
This dataset is a custom dataset created by the author by crawling Naver News (https://news.naver.com) for the Korean NLP model hands-on.
- Period: July 1, 2022 - July 10, 2022
- Subject: IT, economics
```
DatasetDict({
train: Dataset({
features: ['date', 'category', 'press', 'title', 'document', 'link', 'summary'],
num_rows: 22194
})
test: Dataset({
features: ['date', 'category', 'press', 'title', 'document', 'link', 'summary'],
num_rows: 2740
})
validation: Dataset({
features: ['date', 'category', 'press', 'title', 'document', 'link', 'summary'],
num_rows: 2466
})
})
```
---
license: apache-2.0
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|
open-llm-leaderboard/details_beomi__llama-2-ko-7b | open-llm-leaderboard | 2023-09-17T14:18:09Z | 292 | 0 | [
"region:us"
]
| null | 2023-08-18T00:07:15Z | ---
pretty_name: Evaluation run of beomi/llama-2-ko-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-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_beomi__llama-2-ko-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T14:17:57.880003](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__llama-2-ko-7b/blob/main/results_2023-09-17T14-17-57.880003.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\": 0.001153523489932886,\n\
\ \"em_stderr\": 0.00034761798968571043,\n \"f1\": 0.04101300335570507,\n\
\ \"f1_stderr\": 0.0009468163407656627,\n \"acc\": 0.37055050554311253,\n\
\ \"acc_stderr\": 0.008214439814114797\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001153523489932886,\n \"em_stderr\": 0.00034761798968571043,\n\
\ \"f1\": 0.04101300335570507,\n \"f1_stderr\": 0.0009468163407656627\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.019711902956785442,\n \
\ \"acc_stderr\": 0.0038289829787357134\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7213891081294396,\n \"acc_stderr\": 0.01259989664949388\n\
\ }\n}\n```"
repo_url: https://huggingface.co/beomi/llama-2-ko-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_24T11_07_33.480523
path:
- '**/details_harness|arc:challenge|25_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T14_17_57.880003
path:
- '**/details_harness|drop|3_2023-09-17T14-17-57.880003.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T14-17-57.880003.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T14_17_57.880003
path:
- '**/details_harness|gsm8k|5_2023-09-17T14-17-57.880003.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T14-17-57.880003.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hellaswag|10_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:07:33.480523.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T11:07:33.480523.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T11:07:33.480523.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T14_17_57.880003
path:
- '**/details_harness|winogrande|5_2023-09-17T14-17-57.880003.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T14-17-57.880003.parquet'
- config_name: results
data_files:
- split: 2023_07_24T11_07_33.480523
path:
- results_2023-07-24T11:07:33.480523.parquet
- split: 2023_09_17T14_17_57.880003
path:
- results_2023-09-17T14-17-57.880003.parquet
- split: latest
path:
- results_2023-09-17T14-17-57.880003.parquet
---
# Dataset Card for Evaluation run of beomi/llama-2-ko-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/beomi/llama-2-ko-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 [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-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_beomi__llama-2-ko-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T14:17:57.880003](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__llama-2-ko-7b/blob/main/results_2023-09-17T14-17-57.880003.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": 0.001153523489932886,
"em_stderr": 0.00034761798968571043,
"f1": 0.04101300335570507,
"f1_stderr": 0.0009468163407656627,
"acc": 0.37055050554311253,
"acc_stderr": 0.008214439814114797
},
"harness|drop|3": {
"em": 0.001153523489932886,
"em_stderr": 0.00034761798968571043,
"f1": 0.04101300335570507,
"f1_stderr": 0.0009468163407656627
},
"harness|gsm8k|5": {
"acc": 0.019711902956785442,
"acc_stderr": 0.0038289829787357134
},
"harness|winogrande|5": {
"acc": 0.7213891081294396,
"acc_stderr": 0.01259989664949388
}
}
```
### 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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|
datastax/philosopher-quotes | datastax | 2023-10-11T07:55:38Z | 292 | 0 | [
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"license:cc",
"code",
"region:us"
]
| [
"conversational"
]
| 2023-10-10T12:02:19Z | ---
license: cc
task_categories:
- conversational
language:
- en
tags:
- code
pretty_name: Philosophers Quotes
size_categories:
- n<1K
---
450 quotes by 9 philosophers (50 quotes each), labeled with the author and with a variable number of topic tags.
The quotes originally come from https://www.kaggle.com/datasets/mertbozkurt5/quotes-by-philosophers (CC BY-NC-SA 4.0).
The text of each quote has been cleaned of soft-hyphens (`\xad`) and other weird characters.
The topic labeling has been done with a default HuggingFace zero-shot classifier pipeline with multi_labels. | [
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|
taylorbobaylor/tay-stack-v1 | taylorbobaylor | 2023-11-20T16:07:59Z | 292 | 0 | [
"region:us"
]
| null | 2023-11-20T16:07:58Z | ---
dataset_info:
features:
- name: repo_id
dtype: string
- name: file_path
dtype: string
- name: content
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 628428
num_examples: 66
download_size: 231587
dataset_size: 628428
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
counter | null | 2023-01-25T14:28:41Z | 291 | 0 | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:ur",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ur
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
- topic-classification
paperswithcode_id: counter
pretty_name: COUNTER
dataset_info:
features:
- name: source
struct:
- name: filename
dtype: string
- name: headline
dtype: string
- name: body
dtype: string
- name: total_number_of_words
dtype: int64
- name: total_number_of_sentences
dtype: int64
- name: number_of_words_with_swr
dtype: int64
- name: newspaper
dtype: string
- name: newsdate
dtype: string
- name: domain
dtype:
class_label:
names:
'0': business
'1': sports
'2': national
'3': foreign
'4': showbiz
- name: classification
dtype:
class_label:
names:
'0': wholly_derived
'1': partially_derived
'2': not_derived
- name: derived
struct:
- name: filename
dtype: string
- name: headline
dtype: string
- name: body
dtype: string
- name: total_number_of_words
dtype: int64
- name: total_number_of_sentences
dtype: int64
- name: number_of_words_with_swr
dtype: int64
- name: newspaper
dtype: string
- name: newsdate
dtype: string
- name: domain
dtype:
class_label:
names:
'0': business
'1': sports
'2': national
'3': foreign
'4': showbiz
- name: classification
dtype:
class_label:
names:
'0': wholly_derived
'1': partially_derived
'2': not_derived
splits:
- name: train
num_bytes: 2598872
num_examples: 600
download_size: 1356306
dataset_size: 2598872
---
# Dataset Card for COUNTER
## 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://ucrel.lancs.ac.uk/textreuse/counter.php
- **Repository:** [More Information Needed]
- **Paper:** https://link.springer.com/article/10.1007%2Fs10579-016-9367-2
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [UCREL]([email protected])
### Dataset Summary
The COrpus ofUrdu News TExt Reuse (COUNTER) corpus contains 1200 documents with realexamples of text reuse from the field of journalism. It has been manually annotatedat document level with three levels of reuse: wholly derived, partially derived andnon derived
### Supported Tasks and Leaderboards
other:text-reuse
### Languages
ur
## Dataset Structure
### Data Instances
Here is one example from the dataset:
```
{"derived": {
"body" :"میر پور(وقت نیوز) بنگلہ دیش نے 5 میچوں کی سیریز کےآ خری میچ میں بھی فتح حاصل کر کے سیریز میں وائٹ واش کر دیا،زمبابوے ایک میچ بھی نہ جیت سکا۔آخری میچ میں زمبابوے کے 129 رنز کا ہدف بنگال ٹائیگرز نے 24.3 اوورز میں 5 وکٹوں کے نقصان پر حاصل کر لیا۔بنگلہ دیش کے شیر بنگلہ سٹیڈیم میر پور میں کھیلے گئے آخری ایک روزہ میچ میں زمبابوے کے کپتان چکمبورا نے ٹاس جیت کے بینٹگ کا فیصلہ کیا جو ان کی ٹیم کیلئے ڈراؤنا خواب ثابت ہوا اور پوری ٹیم 30 اوورز میں 128 رنز بنا کر پویلین لوٹ گئی زمبابوے کی پہلی وکٹ 16 رنز پر گری جب سکندر رضا صرف 9 رنز بنا کر مشرقی مرتضی کی بال پر آؤٹ ہوئے اس کے بعد مساکد ازااور سباندا کی پارٹنرشپنے ٹیم کا سکور95 رنز تک پہنچا دیا ۔مساکدازا 52 رنز بنا کر جبیر الحسن کا شکار بنے جبکہ سباندا نے 37 رنز کی اننگز کھیلی اس کے بعد کئی بھی زمبابوے کا کھلاڑی جم کر نہ کھیل سکا۔بنگال ٹائیگرز کی جانب سے عمدہ باؤلنگ کے نتیجے میں کپتان چکمبورا سمیت 8 کھلاڑی ڈبل فیگر کراس نہ کر سکے ۔بنگلہ دیش کی جانب سے ایک روزہ میچوں میں ڈیبیو کرنے والے تیج السلام نے اپنے پہلے ہی میچ میں ہیٹرک کی اسلام نے 7 اوورز میں صرف 14 رنز دئے اور چار کھلاڑیوں کع آؤٹ کیا جبکہ شکیب الحسن نے 30 رنز دیکر 3 اور جبیر الحسن نے41 رنز دیکر2 کھلاڑیوں کو پویلین کی راہ دکھائی ۔ 128 رنز کے جواب میں بنگال ٹائیگرز نے بیٹنگ شروع کی مشکلات کا سامنا رہا ان کے بھی ابتدائی 3 کھلاڑی 47 رنز پر پویلین لوٹ گئے۔ تمیم اقبال 10، انعام الحق8 رنز بنا کر آؤٹ ہوئے،آل راؤنڈر شکیب الحسن بغیر کوئی رنز بنائیپویلین لوٹ گئے وکٹ کیپر مشفق الرحیم صرف 11 رنز بنا کر چتارہ کا شکار بن گئے۔محمد اللہ نے51 رنز کی میچ وننگ اننگز کھیلی جبکہ صابر رحمٰن13 رنز بنا کر ناٹ آؤٹ رہے۔ زمبابوے کی جانب سے چتارہ نے 3 اور پنیا نگارا نے 2 کھلاڑیوں کو آؤٹ کیا ۔فتح کے ساتھ بنگلہ دیش نے سیریز میں وائٹ واش کر دیا۔زمبابوے کی ٹیم کوئی میچ نہ جیت سکی،تیج السلام کو میچ کا بہترین ایوارڈ دیا گیا جبکہ سیریز کا بہترین کھلاڑی مشفق الرحیم کو قرار دیا گیا۔",
"classification": 1, # partially_derived
"domain": 1, # sports
"filename": "0001p.xml",
"headline": "بنگلہ دیش کا زمبابوے کا ون ڈے سیریز میں 5-0 سے وائٹ واش",
"newsdate": "02.12.14",
"newspaper": "daily_waqt",
"number_of_words_with_swr": 265,
"total_number_of_sentences": 13,
"total_number_of_words": 393},
"source": {
"body": "ڈھاکہ ۔ یکم دسمبر (اے پی پی) بنگلہ دیش نے زمبابوے کو ٹیسٹ کے بعد ون ڈے سیریز میں بھی وائٹ واش کر دیا۔ سیریز کے پانچویں اور آخری ون ڈے میچ میں بنگال ٹائیگرز نے زمبابوے کو 5 وکٹوں سے شکست دے دی، مہمان ٹیم پہلے بیٹنگ کرتے ہوئے 128 رنز پر ڈھیر ہوگئی۔ تیج الاسلام نے کیریئر کے پہلے ون ڈے میچ میں ہیٹ ٹرک کرکے نئی تاریخ رقم کر دی، انہوں نے 4 کھلاڑیوں کو آؤٹ کیا۔ جواب میں بنگلہ دیش نے ہدف 24.3 اوورز میں 5 وکٹوں کے نقصان پر حاصل کر لیا۔ محمد اللہ نے 51 رنز کی ناقابل شکست اننگز کھیلی۔ تفصیلات کے مطابق پیر کو شیر بنگلہ نیشنل سٹیڈیم، میرپور میں پانچویں اور آخری ون ڈے میچ میں زمبابوے کے کپتان ایلٹن چگمبورا نے ٹاس جیت کر پہلے بیٹنگ کا فیصلہ کیا جو غلط ثابت ہوا۔ زمبابوے کی پوری ٹیم ڈیبیو ون ڈے کھیلنے والے نوجوان لیفٹ آرم سپنر تیج الاسلام اور شکیب الحسن کی تباہ کن باؤلنگ کے باعث 30 اوورز میں 128 رنز پر ڈھیر ہوگئی۔ ہیملٹن ماساکڈزا 52 اور ووسی سبانڈا 37 رنز کے ساتھ نمایاں رہے، ان کے علاوہ کوئی بھی بلے باز دوہرا ہندسہ عبور نہ کر سکا۔ اپنا پہلا ون ڈے کھیلنے والے تیج الاسلام نے 11 رنز کے عوض 4 وکٹیں حاصل کیں جس میں شاندار ہیٹ ٹرک بھی شامل ہے، اس طرح وہ ڈیبیو میں ہیٹ ٹرک کرنے والے دنیا کے پہلے باؤلر بن گئے ہیں۔ شکیب الحسن نے تین اور زبیر حسین نے دو وکٹیں حاصل کیں۔ جواب میں بنگلہ دیش نے ہدف 24.3 اوورز میں 5 وکٹوں کے نقصان پر حاصل کر لیا۔ محمد اللہ نے 51 رنز کی ناقابل شکست اننگز کھیل کر ٹیم کی فتح میں اہم کردار ادا کیا۔ زمبابوے کی جانب سے ٹینڈائی چتارا نے تین اور تناشے پینگارا نے دو وکٹیں حاصل کیں۔",
"classification": 1, # partially_derived
"domain": 1, # sports
"filename": "0001.xml",
"headline": "بنگال ٹائیگرز نے کمزور زمبابوے کو ٹیسٹ کے بعد ون ڈے سیریز میں بھی وائٹ واش کر دیا، پانچویں اور آخری ون ڈے میچ میں بنگلہ دیش 5 وکٹوں سے فتح یاب، تیج الاسلام نے ڈیبیو ون ڈے میں ہیٹ ٹرک کرکے نئی تاریخ رقم کر دی"
"newsdate": "01.12.14",
"newspaper": "APP",
"number_of_words_with_swr": 245,
"total_number_of_sentences": 15,
"total_number_of_words": 352}}
```
### Data Fields
```source```: The source document
```derived```: The derived document
For each pair of source and derived documents. we have the following fields:
```filename (str)```: Name of the file in dataset
```headline(str)```: Headline of the news item
```body(str)```: Main text of the news item
```total_number_of_words(int)```: Number of words in document
```total_number_of_sentences(int)```: Number of sentences in document
```number_of_words_with_swr(int)```: Number of words after stop word removal
```newspaper(str)```: The newspaper in which the news item was published
```newsdate(str)```: The date on which the news item is published DD.MM.YY
```domain(int)```: The category of news item from this list: "business", "sports", "national", "foreign", "showbiz".
```classification (int)```: Three classes of reuse from this list: Wholly Derived (WD), Partially Derived (PD) and Non Derived (ND)
### Data Splits
One split train with 600 pairs of documents.
The corpus is composed of two main document types: (1) source documents and (2) derived documents. There are total 1200 documents in the corpus: 600 are newsagency articles (source documents) and 600 are newspapers stories (derived documents). The corpus contains in total 275,387 words (tokens8), 21,426 unique words and 10,841 sentences. The average length of a source document is 227 words while for derived documents it is 254 words.
## Dataset Creation
### Curation Rationale
Our main intention was to develop a standard benchmark resource for the evaluation of existing systems available for text reuse detection in general and specifically for Urdu language. To generate a corpus with realistic examples, we opted for the field of journalism. In journalism, the same news story is published in different newspapers in different forms. It is a standard practice followed by all the newspapers (reporters and editors) to reuse (verbatim or modified) a news story released by the news agency.
### Source Data
#### Initial Data Collection and Normalization
The COUNTER corpus consists of news articles (source documents) released by five news agencies in Pakistan i.e. Associated Press of Pakistan (APP), InternationalNews Network (INN), Independent News Pakistan (INP), News Network International (NNI) and South Asian News Agency (SANA). The corresponding news stories (derived documents) were extracted from nine daily published and large circulation national news papers of the All Pakistan Newspapers Society (APNS), who are subscribed to these news agencies.
These include Nawa-e-Waqt, Daily Dunya, Express, Jang, Daily Waqt, Daily Insaf, Daily Aaj, Daily Islam and DailyPakistan. All of them are part of the mainstream national press, long established dailies with total circulation figures of over four million.7News agency texts (source documents) were provided (in electronic form) by the news agencies on a daily basis when they released the news. Newspaper stories (derived documents) were collected by three volunteers over a period of six months (from July to December 2014).National, Foreign, Business, Sports and Showbiz were the domains targeted for data collection.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The corpus has been annotated at the document level with three classes of reuse i.e.Wholly Derived (WD), Partially Derived (PD) and Non Derived (ND).
The derived collection contains documents with various degrees of text reuse. Some of the newspaper stories (derived documents)are rewritten (either verbatim or paraphrased) from the new agencys text (source document) while others have been written by the journalists independently on their own. For the former case, source-derived document pairs are either tagged as Wholly Derived (WD) or Partially Derived (PD) depending on the volume of text reused from the news agencys text for creating the newspaper article while for the latter case, they are tagged as Non Derived (ND) as the journalists have not reused anything from the news agencys text but based on their own observations and findings, developed and documented the story.
The annotations were carried out in three phases: (1) training phase, (2) annotations, (3)conflict resolving. During the training phase, annotators A and B manually annotated 60 document pairs, following a preliminary version of the annotation guidelines. A detailed meeting was carried out afterwards, discussing the problems and disagreements. It was observed that the highest number of disagreements were between PD and ND cases, as both found it difficult to distinguish between these two classes. The reason being that adjusting the threshold where a text is heavily paraphrased or new information added to it that it becomes independently written(ND). Following the discussion, the annotation guidelines were slightly revised, and the first 60 annotations results were saved. In the annotation phase, the remaining540 document pairs were manually examined by the two annotators (A and B). Both were asked to judge, and classify (at document level) whether a document(newspaper story) depending on the volume of text rewritten from the source (news agency article) falls into one of the following categories:remaining540 document pairs were manually examined by the two annotators (A and B). Both were asked to judge, and classify (at document level) whether a document(newspaper story) depending on the volume of text rewritten from the source (news agency article) falls into one of the following categories:
Wholly Derived (WD)The News agency text is the only source for the reused newspaper text, which means it is a verbatim copy of the source. In this case, most of the reused text is word-to-word copy of the source text.Partially Derived (PD)The Newspaper text has been either derived from more than one news agency or most of the text is paraphrased by the editor when rewriting from news agency text source. In this case, most parts of the derived document contain paraphrased text or new facts and figures added by the journalists own findings. Non Derived (ND)The News agency text has not been used in the production of the newspaper text (though words may still co-occur in both documents), it has completely different facts and figures or is heavily paraphrased from the newsagencys copy. In this case, the derived document is independently written and has a lot more new text.
#### Who are the annotators?
The annotations were performed by three annotators (A, B and C), who were native Urdu language speakers and experts of paraphrasing mechanisms. All three were graduates, experienced in text annotations and having an advanced Urdu level.
### 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 is licensed under the Creative Common Attribution-NonCommercial-ShareAlike 4.0 International License.
[(CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
```
@Article{Sharjeel2016,
author="Sharjeel, Muhammad
and Nawab, Rao Muhammad Adeel
and Rayson, Paul",
title="COUNTER: corpus of Urdu news text reuse",
journal="Language Resources and Evaluation",
year="2016",
pages="1--27",
issn="1574-0218",
doi="10.1007/s10579-016-9367-2",
url="http://dx.doi.org/10.1007/s10579-016-9367-2"
}
```
### Contributions
Thanks to [@arkhalid](https://github.com/arkhalid) for adding this dataset. | [
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|
factckbr | null | 2023-01-25T14:30:15Z | 291 | 3 | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pt",
"license:mit",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: FACTCK BR
dataset_info:
features:
- name: url
dtype: string
- name: author
dtype: string
- name: date
dtype: string
- name: claim
dtype: string
- name: review
dtype: string
- name: title
dtype: string
- name: rating
dtype: float32
- name: best_rating
dtype: float32
- name: label
dtype:
class_label:
names:
'0': falso
'1': distorcido
'2': impreciso
'3': exagerado
'4': insustentável
'5': verdadeiro
'6': outros
'7': subestimado
'8': impossível provar
'9': discutível
'10': sem contexto
'11': de olho
'12': verdadeiro, mas
'13': ainda é cedo para dizer
splits:
- name: train
num_bytes: 750646
num_examples: 1313
download_size: 721314
dataset_size: 750646
---
# Dataset Card for FACTCK BR
## 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/jghm-f/FACTCK.BR
- **Repository:** https://github.com/jghm-f/FACTCK.BR
- **Paper:** https://dl.acm.org/doi/10.1145/3323503.3361698
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A dataset to study Fake News in Portuguese, presenting a supposedly false News along with their respective fact check and classification.
The data is collected from the ClaimReview, a structured data schema used by fact check agencies to share their results in search engines, enabling data collect in real time.
The FACTCK.BR dataset contains 1309 claims with its corresponding label.
### 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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|
ro_sent | null | 2023-01-25T14:43:14Z | 291 | 0 | [
"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:ro",
"license:unknown",
"arxiv:2009.08712",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- ro
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: RoSent
dataset_info:
features:
- name: original_id
dtype: string
- name: id
dtype: string
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: train
num_bytes: 8367687
num_examples: 17941
- name: test
num_bytes: 6837430
num_examples: 11005
download_size: 14700057
dataset_size: 15205117
---
# Dataset Card for RoSent
## 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://github.com/dumitrescustefan/Romanian-Transformers/tree/examples/examples/sentiment_analysis)
- **Repository:** [GitHub](https://github.com/dumitrescustefan/Romanian-Transformers/tree/examples/examples/sentiment_analysis)
- **Paper:** [arXiv preprint](https://arxiv.org/pdf/2009.08712.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is a Romanian Sentiment Analysis dataset. It is present in a processed form, as used by the authors of [`Romanian Transformers`](https://github.com/dumitrescustefan/Romanian-Transformers) in their examples and based on the original data present in at [this GitHub repository](https://github.com/katakonst/sentiment-analysis-tensorflow). The original data contains product and movie reviews in Romanian.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
This dataset is present in Romanian language.
## Dataset Structure
### Data Instances
An instance from the `train` split:
```
{'id': '0', 'label': 1, 'original_id': '0', 'sentence': 'acest document mi-a deschis cu adevarat ochii la ceea ce oamenii din afara statelor unite s-au gandit la atacurile din 11 septembrie. acest film a fost construit in mod expert si prezinta acest dezastru ca fiind mai mult decat un atac asupra pamantului american. urmarile acestui dezastru sunt previzionate din multe tari si perspective diferite. cred ca acest film ar trebui sa fie mai bine distribuit pentru acest punct. de asemenea, el ajuta in procesul de vindecare sa vada in cele din urma altceva decat stirile despre atacurile teroriste. si unele dintre piese sunt de fapt amuzante, dar nu abuziv asa. acest film a fost extrem de recomandat pentru mine, si am trecut pe acelasi sentiment.'}
```
### Data Fields
- `original_id`: a `string` feature containing the original id from the file.
- `id`: a `string` feature .
- `sentence`: a `string` feature.
- `label`: a classification label, with possible values including `negative` (0), `positive` (1).
### Data Splits
This dataset has two splits: `train` with 17941 examples, and `test` with 11005 examples.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The source dataset is present at the [this GitHub repository](https://github.com/katakonst/sentiment-analysis-tensorflow) and is based on product and movie reviews. The original source is unknown.
#### 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
Stefan Daniel Dumitrescu, Andrei-Marious Avram, Sampo Pyysalo, [@katakonst](https://github.com/katakonst)
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{dumitrescu2020birth,
title={The birth of Romanian BERT},
author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius and Pyysalo, Sampo},
journal={arXiv preprint arXiv:2009.08712},
year={2020}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchhablani) and [@iliemihai](https://github.com/iliemihai) for adding this dataset. | [
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|
open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-instruction-lora | open-llm-leaderboard | 2023-09-22T19:01:31Z | 291 | 0 | [
"region:us"
]
| null | 2023-08-17T23:45:20Z | ---
pretty_name: Evaluation run of lvkaokao/llama2-7b-hf-instruction-lora
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [lvkaokao/llama2-7b-hf-instruction-lora](https://huggingface.co/lvkaokao/llama2-7b-hf-instruction-lora)\
\ 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_lvkaokao__llama2-7b-hf-instruction-lora\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T19:01:17.427442](https://huggingface.co/datasets/open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-instruction-lora/blob/main/results_2023-09-22T19-01-17.427442.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\": 0.32466442953020136,\n\
\ \"em_stderr\": 0.004795311441664345,\n \"f1\": 0.372114093959732,\n\
\ \"f1_stderr\": 0.00471907408435466,\n \"acc\": 0.4202347692309533,\n\
\ \"acc_stderr\": 0.010254299592459359\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.32466442953020136,\n \"em_stderr\": 0.004795311441664345,\n\
\ \"f1\": 0.372114093959732,\n \"f1_stderr\": 0.00471907408435466\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09855951478392722,\n \
\ \"acc_stderr\": 0.008210320350946328\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7419100236779794,\n \"acc_stderr\": 0.01229827883397239\n\
\ }\n}\n```"
repo_url: https://huggingface.co/lvkaokao/llama2-7b-hf-instruction-lora
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_09T14_42_44.392764
path:
- '**/details_harness|arc:challenge|25_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T19_01_17.427442
path:
- '**/details_harness|drop|3_2023-09-22T19-01-17.427442.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T19-01-17.427442.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T19_01_17.427442
path:
- '**/details_harness|gsm8k|5_2023-09-22T19-01-17.427442.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T19-01-17.427442.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hellaswag|10_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T14:42:44.392764.parquet'
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T14:42:44.392764.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T14:42:44.392764.parquet'
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- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T14:42:44.392764.parquet'
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- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T14:42:44.392764.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T14:42:44.392764.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_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
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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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- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_machine_learning_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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- 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:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
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path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T14:42:44.392764.parquet'
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path:
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- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T14:42:44.392764.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T14:42:44.392764.parquet'
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path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T14_42_44.392764
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T14:42:44.392764.parquet'
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path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T14:42:44.392764.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
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- 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:
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path:
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- config_name: harness_hendrycksTest_world_religions_5
data_files:
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path:
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path:
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- config_name: harness_truthfulqa_mc_0
data_files:
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path:
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path:
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- config_name: harness_winogrande_5
data_files:
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path:
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path:
- '**/details_harness|winogrande|5_2023-09-22T19-01-17.427442.parquet'
- config_name: results
data_files:
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path:
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- split: 2023_09_22T19_01_17.427442
path:
- results_2023-09-22T19-01-17.427442.parquet
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path:
- results_2023-09-22T19-01-17.427442.parquet
---
# Dataset Card for Evaluation run of lvkaokao/llama2-7b-hf-instruction-lora
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lvkaokao/llama2-7b-hf-instruction-lora
- **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 [lvkaokao/llama2-7b-hf-instruction-lora](https://huggingface.co/lvkaokao/llama2-7b-hf-instruction-lora) 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_lvkaokao__llama2-7b-hf-instruction-lora",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T19:01:17.427442](https://huggingface.co/datasets/open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-instruction-lora/blob/main/results_2023-09-22T19-01-17.427442.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": 0.32466442953020136,
"em_stderr": 0.004795311441664345,
"f1": 0.372114093959732,
"f1_stderr": 0.00471907408435466,
"acc": 0.4202347692309533,
"acc_stderr": 0.010254299592459359
},
"harness|drop|3": {
"em": 0.32466442953020136,
"em_stderr": 0.004795311441664345,
"f1": 0.372114093959732,
"f1_stderr": 0.00471907408435466
},
"harness|gsm8k|5": {
"acc": 0.09855951478392722,
"acc_stderr": 0.008210320350946328
},
"harness|winogrande|5": {
"acc": 0.7419100236779794,
"acc_stderr": 0.01229827883397239
}
}
```
### 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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|
vilm/OpenOrca-Viet | vilm | 2023-11-06T17:48:37Z | 291 | 6 | [
"license:apache-2.0",
"arxiv:2306.02707",
"region:us"
]
| null | 2023-11-06T06:29:15Z | ---
license: apache-2.0
---
## 🇻🇳 Vietnamese OpenOrca is here 🐋
<img src="https://i.ibb.co/kgmJG96/orca-viet.png" alt="drawing" width="512"/>
Dive into the Vietnamese linguistic landscape with OpenOrca, a cutting-edge dataset crafted through a pioneering partnership between **Virtual Interactive** and **Alignment Lab AI**. Drawing inspiration and methodology from the renowned [Orca paper](https://arxiv.org/abs/2306.02707), we've expanded our horizons to distill knowledge from a more eclectic mix of leading LLMs including GPT-4, PaLM-2, and Claude. Our vision with this dataset is to fuel research and development that will catapult the performance of Vietnamese Language Models into uncharted territories. Join us on this exhilarating journey to redefine AI's linguistic prowess.
The main original source of tasks/questions is a translated version of *FLAN*, **vi-FLAN**. We further augmented **vi-FLAN** on better state-of-the-art LLMs.
## Citation
```
@misc{OpenOrcaViet,
title = {OpenOrca-Viet: GPT Augmented FLAN Reasoning for Vietnamese},
author = {Virtual Interactive and Alignment Lab AI},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/vilm/OpenOrca-Viet}},
}
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|
ilist | null | 2023-01-25T14:32:46Z | 290 | 1 | [
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:awa",
"language:bho",
"language:bra",
"language:hi",
"language:mag",
"license:cc-by-4.0",
"language-identification",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- awa
- bho
- bra
- hi
- mag
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: ilist
tags:
- language-identification
dataset_info:
features:
- name: language_id
dtype:
class_label:
names:
'0': AWA
'1': BRA
'2': MAG
'3': BHO
'4': HIN
- name: text
dtype: string
splits:
- name: train
num_bytes: 14362998
num_examples: 70351
- name: test
num_bytes: 2146857
num_examples: 9692
- name: validation
num_bytes: 2407643
num_examples: 10329
download_size: 18284850
dataset_size: 18917498
---
# Dataset Card for ilist
## 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:** https://github.com/kmi-linguistics/vardial2018
- **Paper:** [Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign](https://aclanthology.org/W18-3901/)
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
This dataset is introduced in a task which aimed at identifying 5 closely-related languages of Indo-Aryan language family: Hindi (also known as Khari Boli), Braj Bhasha, Awadhi, Bhojpuri and Magahi. These languages form part of a continuum starting from Western Uttar Pradesh (Hindi and Braj Bhasha) to Eastern Uttar Pradesh (Awadhi and Bhojpuri) and the neighbouring Eastern state of Bihar (Bhojpuri and Magahi).
For this task, participants were provided with a dataset of approximately 15,000 sentences in each language, mainly from the domain of literature, published over the web as well as in print.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Hindi, Braj Bhasha, Awadhi, Bhojpuri and Magahi
## Dataset Structure
### Data Instances
```
{
"language_id": 4,
"text": 'तभी बारिश हुई थी जिसका गीलापन इन मूर्तियों को इन तस्वीरों में एक अलग रूप देता है .'
}
```
### Data Fields
- `text`: text which you want to classify
- `language_id`: label for the text as an integer from 0 to 4
The language ids correspond to the following languages: "AWA", "BRA", "MAG", "BHO", "HIN".
### Data Splits
| | train | valid | test |
|----------------------|-------|-------|-------|
| # of input sentences | 70351 | 9692 | 10329 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The data for this task was collected from both hard printed and digital sources. Printed materials were
obtained from different institutions that promote these languages. We also gathered data from libraries,
as well as from local literary and cultural groups. We collected printed stories, novels and essays in
books, magazines, and newspapers.
#### Initial Data Collection and Normalization
We scanned the printed materials, then we performed OCR, and
finally we asked native speakers of the respective languages to correct the OCR output. Since there are
no specific OCR models available for these languages, we used the Google OCR for Hindi, part of the
Drive API. Since all the languages used the Devanagari script, we expected the OCR to work reasonably
well, and overall it did. We further managed to get some blogs in Magahi and Bhojpuri.
#### 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
This work is licensed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0/
### Citation Information
```
@inproceedings{zampieri-etal-2018-language,
title = "Language Identification and Morphosyntactic Tagging: The Second {V}ar{D}ial Evaluation Campaign",
author = {Zampieri, Marcos and
Malmasi, Shervin and
Nakov, Preslav and
Ali, Ahmed and
Shon, Suwon and
Glass, James and
Scherrer, Yves and
Samard{\v{z}}i{\'c}, Tanja and
Ljube{\v{s}}i{\'c}, Nikola and
Tiedemann, J{\"o}rg and
van der Lee, Chris and
Grondelaers, Stefan and
Oostdijk, Nelleke and
Speelman, Dirk and
van den Bosch, Antal and
Kumar, Ritesh and
Lahiri, Bornini and
Jain, Mayank},
booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-3901",
pages = "1--17",
}
```
### Contributions
Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset. | [
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|
ofis_publik | null | 2022-11-03T16:15:15Z | 290 | 0 | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:br",
"language:fr",
"license:unknown",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- br
- fr
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OfisPublik
dataset_info:
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- br
- fr
config_name: br-fr
splits:
- name: train
num_bytes: 12256825
num_examples: 63422
download_size: 3856983
dataset_size: 12256825
---
# Dataset Card for OfisPublik
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/OfisPublik.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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|
NbAiLab/norec_agg | NbAiLab | 2022-07-01T19:53:24Z | 290 | 0 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2011.02686",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-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:** [A Fine-grained Sentiment Dataset for Norwegian](https://www.aclweb.org/anthology/2020.lrec-1.618/)
- **Leaderboard:** N/A
- **Point of Contact:** -
### Dataset Summary
Aggregated NoRec_fine: A Fine-grained Sentiment Dataset for Norwegian.
This dataset was created by the Nordic Language Processing Laboratory by aggregating the fine-grained annotations in NoReC_fine and removing sentences with conflicting or no sentiment.
### 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 sentence
- `label`: The sentiment label. Here
- 0 = negative
- 1 = positive
### Data Splits
The dataset is split into a `train`, `validation`, and `test` split with the following sizes:
| | Tain | Valid | Test |
| ----- | ------ | ----- | ----- |
| Number of examples | 2675 | 516 | 417 |
## Dataset Creation
This dataset is based largely on the original data described in the paper _A Fine-Grained Sentiment Dataset for Norwegian_ by L. Øvrelid, P. Mæhlum, J. Barnes, and E. Velldal, accepted at LREC 2020, [paper available](https://www.aclweb.org/anthology/2020.lrec-1.618). However, we have since added annotations for another 3476 sentences, increasing the overall size and scope of the dataset.
## Additional Information
### Licensing Information
This work is licensed under a Creative Commons Attribution 4.0 International License
### Citation Information
```latex
@misc{sheng2020investigating,
title={Investigating Societal Biases in a Poetry Composition System},
author={Emily Sheng and David Uthus},
year={2020},
eprint={2011.02686},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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|
lampent/IRFL | lampent | 2023-11-30T12:40:28Z | 290 | 1 | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"figurative-language",
"multimodal-figurative-language",
" commonsense-reasoning",
"visual-reasoning",
"arxiv:2303.15445",
"region:us"
]
| null | 2023-06-01T09:34:13Z | ---
configs:
- config_name: idiom-detection-task
data_files:
- split: test
path: "idiom_detection_task.csv"
- config_name: metaphor-detection-task
data_files:
- split: test
path: "metaphor_detection_task.csv"
- config_name: simile-detection-task
data_files:
- split: test
path: "simile_detection_task.csv"
- config_name: open-simile-detection-task
data_files:
- split: test
path: "open_simile_detection_task.csv"
- config_name: idiom-retrieval-task
data_files:
- split: test
path: "idiom_retrieval_task.csv"
- config_name: metaphor-retrieval-task
data_files:
- split: test
path: "metaphor_retrieval_task.csv"
- config_name: simile-retrieval-task
data_files:
- split: test
path: "simile_retrieval_task.csv"
- config_name: open-simile-retrieval-task
data_files:
- split: test
path: "open_simile_retrieval_task.csv"
- config_name: idioms-dataset
data_files:
- split: dataset
path: "idioms_dataset.csv"
- config_name: similes-dataset
data_files:
- split: dataset
path: "similes_dataset.csv"
- config_name: metaphors-dataset
data_files:
- split: dataset
path: "metaphors_dataset.csv"
license: cc-by-4.0
language:
- en
tags:
- figurative-language
- multimodal-figurative-language
- ' commonsense-reasoning'
- visual-reasoning
size_categories:
- 1K<n<10K
---
# Dataset Card for IRFL
- [Dataset Description](#dataset-description)
- [Leaderboards](#leaderboards)
- [Colab notebook code for IRFL evaluation](#colab-notebook-code-for-irfl-evaluation)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
The IRFL dataset consists of idioms, similes, metaphors with matching figurative and literal images, and two novel tasks of multimodal figurative detection and retrieval.
Using human annotation and an automatic pipeline we created, we collected figurative and literal images for textual idioms, metaphors, and similes.
We annotated the relations between these images and the figurative phrase they originated from. We created two novel tasks of figurative detection and retrieval using these images.
The figurative detection task evaluates Vision and Language Pre-Trained Models’ (VL-PTMs) ability to choose the image that best visualizes the meaning of a figurative expression. The task is to choose the image that best visualizes the figurative phrase out of X candidates. The retrieval task examines VL-PTMs' preference for figurative images. In this task, Given a set of figurative and partially literal images, the task is to rank the images using the model-matching score such that the figurative images are ranked higher, and calculate the precision at k, where k is the number of figurative images in the input.
We evaluated state-of-the-art VL models and found that the best models achieved 22%, 30%, and 66% accuracy vs. humans 97%, 99.7%, and 100% on our detection task for idioms, metaphors, and similes respectively. The best model achieved an F1 score of 61 on the retrieval task.
- **Homepage:**
https://irfl-dataset.github.io/
- **Repository:**
https://github.com/irfl-dataset/IRFL
- **Paper:**
https://arxiv.org/abs/2303.15445
- **Leaderboard:**
https://irfl-dataset.github.io/leaderboard
- **Point of Contact:**
[email protected]; [email protected]
### Leaderboards
https://irfl-dataset.github.io/leaderboard
### Colab notebook for IRFL evaluation
https://colab.research.google.com/drive/1RfcUhBTHvREx5X7TMY5UAgMYX8NMKy7u?usp=sharing
### Languages
English.
## Dataset Structure
### Data Fields
★ - refers to idiom-only fields
⁺₊ - refers to metaphor-only fields
Multimodal Figurative Language Detection task
- query (★): the idiom definition the answer image originated from.
- distractors: the distractor images
- answer: the correct image
- figurative_type: idiom | metaphor | simile
- type: the correct image type (Figurative or Figurative+Literal).
- definition (★): list of all the definitions of the idiom
- phrase: the figurative phrase.
Multimodal Figurative Language Retrieval task
- type: the rival categories FvsPL (Figurative images vs. Partial Literal) or FLvsPL (Figurative+Literal images vs. Partial Literal)
- figurative_type: idiom | metaphor | simile
- images_metadata: the metadata of the distractors and answer images.
- first_category: the first category images (Figurative images if FvsPL, Figurative Literal images if FLvsPL)
- second_category: the second category images (Partial Literal)
- definition (★): list of all the definitions of the idiom
- theme (⁺₊): the theme of the partial literal distractor, for example, for the metaphor heart of gold, an image of a "gold bar" and an image of a "human heart" will have different theme value
- phrase: the figurative phrase.
The idioms, metaphor, and similes datasets contain all the figurative phrases, annotated images, and corresponding metadata. <br/>
## Dataset Collection
Using an automatic pipeline we created, we collected figurative and literal images for textual idioms, metaphors, and similes. We annotated the relations between these images and the figurative phrase they originated from.
#### Annotation process
We paid Amazon Mechanical Turk Workers to annotate the relation between each image and phrase (Figurative vs. Literal).
## Considerations for Using the Data
- Idioms: Annotated by five crowdworkers with rigorous qualifications and training.
- Metaphors and Similes: Annotated by three expert team members.
- Detection and Ranking Tasks: Annotated by three crowdworkers not involved in prior IRFL annotations.
### Licensing Information
CC-By 4.0
### Citation Information
@misc{yosef2023irfl,
title={IRFL: Image Recognition of Figurative Language},
author={Ron Yosef and Yonatan Bitton and Dafna Shahaf},
year={2023},
eprint={2303.15445},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | [
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|
open-llm-leaderboard/details_circulus__Llama-2-13b-orca-v1 | open-llm-leaderboard | 2023-09-17T12:51:10Z | 290 | 0 | [
"region:us"
]
| null | 2023-08-18T18:46:51Z | ---
pretty_name: Evaluation run of circulus/Llama-2-13b-orca-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [circulus/Llama-2-13b-orca-v1](https://huggingface.co/circulus/Llama-2-13b-orca-v1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 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_circulus__Llama-2-13b-orca-v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T12:50:57.881579](https://huggingface.co/datasets/open-llm-leaderboard/details_circulus__Llama-2-13b-orca-v1/blob/main/results_2023-09-17T12-50-57.881579.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\": 0.1529991610738255,\n\
\ \"em_stderr\": 0.0036866006582882706,\n \"f1\": 0.2246581375838923,\n\
\ \"f1_stderr\": 0.003770616290655452,\n \"acc\": 0.44842066021890015,\n\
\ \"acc_stderr\": 0.010546865226614108\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.1529991610738255,\n \"em_stderr\": 0.0036866006582882706,\n\
\ \"f1\": 0.2246581375838923,\n \"f1_stderr\": 0.003770616290655452\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1288855193328279,\n \
\ \"acc_stderr\": 0.009229580761400274\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827943\n\
\ }\n}\n```"
repo_url: https://huggingface.co/circulus/Llama-2-13b-orca-v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|arc:challenge|25_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T07_53_18.900339
path:
- '**/details_harness|drop|3_2023-09-17T07-53-18.900339.parquet'
- split: 2023_09_17T12_50_57.881579
path:
- '**/details_harness|drop|3_2023-09-17T12-50-57.881579.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T12-50-57.881579.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T07_53_18.900339
path:
- '**/details_harness|gsm8k|5_2023-09-17T07-53-18.900339.parquet'
- split: 2023_09_17T12_50_57.881579
path:
- '**/details_harness|gsm8k|5_2023-09-17T12-50-57.881579.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T12-50-57.881579.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hellaswag|10_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T08:46:04.009114.parquet'
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- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T08:46:04.009114.parquet'
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- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T08:46:04.009114.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T08:46:04.009114.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T08:46:04.009114.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T07_53_18.900339
path:
- '**/details_harness|winogrande|5_2023-09-17T07-53-18.900339.parquet'
- split: 2023_09_17T12_50_57.881579
path:
- '**/details_harness|winogrande|5_2023-09-17T12-50-57.881579.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T12-50-57.881579.parquet'
- config_name: results
data_files:
- split: 2023_08_18T08_46_04.009114
path:
- results_2023-08-18T08:46:04.009114.parquet
- split: 2023_09_17T07_53_18.900339
path:
- results_2023-09-17T07-53-18.900339.parquet
- split: 2023_09_17T12_50_57.881579
path:
- results_2023-09-17T12-50-57.881579.parquet
- split: latest
path:
- results_2023-09-17T12-50-57.881579.parquet
---
# Dataset Card for Evaluation run of circulus/Llama-2-13b-orca-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/circulus/Llama-2-13b-orca-v1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [circulus/Llama-2-13b-orca-v1](https://huggingface.co/circulus/Llama-2-13b-orca-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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_circulus__Llama-2-13b-orca-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T12:50:57.881579](https://huggingface.co/datasets/open-llm-leaderboard/details_circulus__Llama-2-13b-orca-v1/blob/main/results_2023-09-17T12-50-57.881579.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": 0.1529991610738255,
"em_stderr": 0.0036866006582882706,
"f1": 0.2246581375838923,
"f1_stderr": 0.003770616290655452,
"acc": 0.44842066021890015,
"acc_stderr": 0.010546865226614108
},
"harness|drop|3": {
"em": 0.1529991610738255,
"em_stderr": 0.0036866006582882706,
"f1": 0.2246581375838923,
"f1_stderr": 0.003770616290655452
},
"harness|gsm8k|5": {
"acc": 0.1288855193328279,
"acc_stderr": 0.009229580761400274
},
"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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|
usvsnsp/memories-semantic-memorization-filter-results | usvsnsp | 2023-09-20T20:16:41Z | 290 | 1 | [
"region:us"
]
| null | 2023-09-20T20:08:28Z | ---
dataset_info:
features:
- name: sequence_id
dtype: int64
- name: text
dtype: string
- name: sequence_duplicates
dtype: int64
- name: max_frequency
dtype: int64
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- name: p75_frequency
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- name: frequencies
sequence: int64
- name: is_incrementing
dtype: bool
- name: tokens
sequence: int64
- name: repeating_offset
dtype: int32
- name: num_repeating
dtype: int32
- name: smallest_repeating_chunk
sequence: int64
- name: memorization_score
dtype: float64
- name: templating_frequency_0.9
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- name: templating_frequency_0.8
dtype: int64
- name: prompt_perplexity
dtype: float32
- name: generation_perplexity
dtype: float32
- name: sequence_perplexity
dtype: float32
splits:
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- name: memories.deduped.12b
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num_examples: 1871216
download_size: 9223426756
dataset_size: 27159884469
---
# Dataset Card for "memories-semantic-memorization-filter-results"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
crawl_domain | null | 2022-11-18T19:47:14Z | 289 | 0 | [
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-Common-Crawl",
"source_datasets:original",
"language:en",
"license:mit",
"web-search",
"text-to-speech",
"arxiv:2011.03138",
"region:us"
]
| [
"other"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-Common-Crawl
- original
task_categories:
- other
task_ids: []
paperswithcode_id: common-crawl-domain-names
pretty_name: Common Crawl Domain Names
tags:
- web-search
- text-to-speech
dataset_info:
features:
- name: example
dtype: string
splits:
- name: train
num_bytes: 321134
num_examples: 17572
- name: test
num_bytes: 39712
num_examples: 2170
- name: validation
num_bytes: 36018
num_examples: 1953
download_size: 331763
dataset_size: 396864
---
# Dataset Card for Common Crawl Domain Names
## 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/google-research-datasets/common-crawl-domain-names
- **Repository:** https://github.com/google-research-datasets/common-crawl-domain-names
- **Paper:** https://arxiv.org/pdf/2011.03138
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries (e.g. "commoncrawl" to "common crawl").
Breaking [domain names](https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_URL) such as "openresearch" into component words "open" and "research" is important for applications such as Text-to-Speech synthesis and web search. [Common Crawl](https://commoncrawl.org/) is an open repository of web crawl data that can be accessed and analyzed by anyone. Specifically, we scraped the plaintext (WET) extracts for domain names from URLs that contained diverse letter casing (e.g. "OpenBSD"). Although in the previous example, segmentation is trivial using letter casing, this was not always the case (e.g. "NASA"), so we had to manually annotate the data.
### Supported Tasks and Leaderboards
- Text-to-Speech synthesis
- Web search
### Languages
en: English
## Dataset Structure
### Data Instances
Each sample is an example of space separated segments of a domain name. The examples are stored in their original letter casing, but harder and more interesting examples can be generated by lowercasing the input first.
For example:
```
Open B S D
NASA
ASAP Workouts
```
### Data Fields
- `example`: a `string` feature: space separated segments of a domain name.
### Data Splits
| split | size | trivial | avg_input_length | avg_segments |
|-------|-------|---------|------------------|--------------|
| train | 17572 | 13718 | 12.63 | 2.65 |
| eval | 1953 | 1536 | 12.77 | 2.67 |
| test | 2170 | 1714 | 12.63 | 2.66 |
## Dataset Creation
### Curation Rationale
The dataset was curated by scraping the plaintext (WET) extracts for domain names from URLs that contained diverse letter casing (e.g. "OpenBSD"). Although in the previous example, segmentation is trivial using letter casing, this was not always the case (e.g. "NASA"), so the curators of the dataset had to manually annotate the data.
### Source Data
#### Initial Data Collection and Normalization
Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The annotators are the curators of this dataset
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The curators of this dataset are [Jae Hun Ro](https://github.com/JaeHunRo) and [mwurts4google](https://github.com/mwurts4google), who are the contributors of the official Github repository for this dataset. Since the account handles of other curators are unknown currently, the authors of the paper linked to this dataset is mentioned here as curators, [Hao Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang%2C+H), [Jae Ro](https://arxiv.org/search/cs?searchtype=author&query=Ro%2C+J), and [Richard Sproat](https://arxiv.org/search/cs?searchtype=author&query=Sproat%2C+R).
### Licensing Information
[MIT License](https://github.com/google-research-datasets/common-crawl-domain-names/blob/master/LICENSE)
### Citation Information
```
@inproceedings{zrs2020urlsegmentation,
title={Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities},
author={Hao Zhang and Jae Ro and Richard William Sproat},
booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},
year={2020}
}
```
### Contributions
Thanks to [@Karthik-Bhaskar](https://github.com/Karthik-Bhaskar) for adding this dataset. | [
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|
dialog_re | null | 2022-11-18T19:58:15Z | 289 | 8 | [
"task_categories:other",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:other",
"relation-extraction",
"arxiv:2004.08056",
"region:us"
]
| [
"other",
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: dialogre
pretty_name: DialogRE
tags:
- relation-extraction
dataset_info:
features:
- name: dialog
sequence: string
- name: relation_data
sequence:
- name: x
dtype: string
- name: y
dtype: string
- name: x_type
dtype: string
- name: y_type
dtype: string
- name: r
sequence: string
- name: rid
sequence: int32
- name: t
sequence: string
config_name: dialog_re
splits:
- name: train
num_bytes: 1520940
num_examples: 1073
- name: test
num_bytes: 472306
num_examples: 357
- name: validation
num_bytes: 490580
num_examples: 358
download_size: 3816234
dataset_size: 2483826
---
# Dataset Card for [DialogRE]
## 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:** [DialogRE Homepage](https://dataset.org/dialogre/)
- **Repository:** [DialogRE Repository](https://github.com/nlpdata/dialogre)
- **Paper:** [Arxiv](https://arxiv.org/abs/2004.08056v1)
- **Point of Contact:** [[email protected]](mailto:[email protected])
### Dataset Summary
The DialogRE dataset is the first human-annotated dialogue-based relation extraction (RE) dataset, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. DialogRE can also act as a platform for studying cross-sentence RE as most facts span multiple sentences. Specifically, the dataset annotate all occurrences of 36 possible relation types that exist between pairs of arguments in the 1,788 dialogues originating from the complete transcripts of Friends (in English).
### Supported Tasks and Leaderboards
* `other-other-relation-extraction`: The dataset can be used to train a model for Relation Extraction, which consists of the prediction of relation between two arguments that appear in a dialogue. Success on this task is typically measured by achieving a *high* [F1 Score](https://huggingface.co/metrics/f1).
### Languages
The dialogues in the dataset is in English originating from the transcripts of Friends. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A typical data point consists of a dialogue between speakers as a list of sentences. This is followed by the annotations of the relations between the entities in the dialog.
An example from the DialogRE train set looks as follows:
```
{'dialog': ["Speaker 1: It's been an hour and not one of my classmates has shown up! I tell you, when I actually die some people are gonna get seriously haunted!",
'Speaker 2: There you go! Someone came!',
"Speaker 1: Ok, ok! I'm gonna go hide! Oh, this is so exciting, my first mourner!",
'Speaker 3: Hi, glad you could come.',
'Speaker 2: Please, come in.',
"Speaker 4: Hi, you're Chandler Bing, right? I'm Tom Gordon, I was in your class.",
'Speaker 2: Oh yes, yes... let me... take your coat.',
"Speaker 4: Thanks... uh... I'm so sorry about Ross, it's...",
'Speaker 2: At least he died doing what he loved... watching blimps.',
'Speaker 1: Who is he?',
'Speaker 2: Some guy, Tom Gordon.',
"Speaker 1: I don't remember him, but then again I touched so many lives.",
'Speaker 3: So, did you know Ross well?',
"Speaker 4: Oh, actually I barely knew him. Yeah, I came because I heard Chandler's news. D'you know if he's seeing anyone?",
'Speaker 3: Yes, he is. Me.',
'Speaker 4: What? You... You... Oh! Can I ask you a personal question? Ho-how do you shave your beard so close?',
"Speaker 2: Ok Tommy, that's enough mourning for you! Here we go, bye bye!!",
'Speaker 4: Hey, listen. Call me.',
'Speaker 2: Ok!'],
'relation_data': {'r': [['per:alternate_names'],
['per:alumni'],
['per:alternate_names'],
['per:alumni', 'per:positive_impression'],
['per:alternate_names'],
['unanswerable']],
'rid': [[30], [4], [30], [4, 1], [30], [37]],
't': [[''], [''], [''], ['', 'call me'], [''], ['']],
'x': ['Speaker 2',
'Speaker 2',
'Speaker 4',
'Speaker 4',
'Speaker 4',
'Speaker 1'],
'x_type': ['PER', 'PER', 'PER', 'PER', 'PER', 'PER'],
'y': ['Chandler Bing',
'Speaker 4',
'Tom Gordon',
'Speaker 2',
'Tommy',
'Tommy'],
'y_type': ['PER', 'PER', 'PER', 'PER', 'PER', 'PER']}}
```
### Data Fields
* `dialog`
* List of dialog spoken between the speakers
* List of annotations per dialog per argument
* `x` : First entity
* `y` : Second entity
* `x_type` : Type of the first entity
* `y_type`: Type of the second entity
* `r` : List of relations
* `rid`: List of relation IDs
* `t`: List of relation Trigger words
### Data Splits
The data is split into a training, validation and test set as per the original dataset split.
| | train | validation | test |
| --------------------- |-------:|------------:|------:|
| Input dialog examples | 1073 | 358 | 357 |
## 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
DialogRE dataset is intended for non-commercial research purpose only
### Citation Information
```
@inproceedings{yu2020dialogue,
title={Dialogue-Based Relation Extraction},
author={Yu, Dian and Sun, Kai and Cardie, Claire and Yu, Dong},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020},
url={https://arxiv.org/abs/2004.08056v1}
}
```
### Contributions
Thanks to [@vineeths96](https://github.com/vineeths96) for adding this dataset. | [
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|
eitb_parcc | null | 2022-11-03T16:15:31Z | 289 | 1 | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:es",
"language:eu",
"license:unknown",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- es
- eu
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: eitb-parcc
pretty_name: EiTB-ParCC
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- es
- eu
config_name: es-eu
splits:
- name: train
num_bytes: 139039398
num_examples: 637183
download_size: 57244346
dataset_size: 139039398
---
# 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:**[EiTB-ParCC: Parallel Corpus of Comparable New](http://opus.nlpl.eu/EiTB-ParCC.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
EiTB-ParCC: Parallel Corpus of Comparable News. A Basque-Spanish parallel corpus provided by \
Vicomtech (https://www.vicomtech.org), extracted from comparable news produced by the \
Basque public broadcasting group Euskal Irrati Telebista.
### Supported Tasks and Leaderboards
The underlying task is machine translation.
### 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
```
@InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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|
ro_sts | null | 2022-11-18T21:42:20Z | 289 | 0 | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-sts-b",
"language:ro",
"license:cc-by-4.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ro
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-sts-b
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
paperswithcode_id: null
pretty_name: RO-STS
dataset_info:
features:
- name: score
dtype: float32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
config_name: ro_sts
splits:
- name: train
num_bytes: 879073
num_examples: 5749
- name: test
num_bytes: 194330
num_examples: 1379
- name: validation
num_bytes: 245926
num_examples: 1500
download_size: 1267607
dataset_size: 1319329
---
# Dataset Card for RO-STS
## 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://github.com/dumitrescustefan/RO-STS)
- **Repository:** [GitHub](https://github.com/dumitrescustefan/RO-STS)
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [email]([email protected])
### Dataset Summary
We present RO-STS - the Semantic Textual Similarity dataset for the Romanian language. It is a high-quality translation of the [STS English dataset](https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). RO-STS contains 8,628 sentence pairs with their similarity scores. The original English sentences were collected from news headlines, captions of images and user forums, and are categorized accordingly. The Romanian release follows this categorization and provides the same train/validation/test split with 5,749/1,500/1,379 sentence pairs in each subset.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The text dataset is in Romanian (`ro`)
## Dataset Structure
### Data Instances
An example looks like this:
```
{'score': 1.5,
'sentence1': 'Un bărbat cântă la harpă.',
'sentence2': 'Un bărbat cântă la claviatură.',
}
```
### Data Fields
- `score`: a float representing the semantic similarity score where 0.0 is the lowest score and 5.0 is the highest
- `sentence1`: a string representing a text
- `sentence2`: another string to compare the previous text with
### Data Splits
The train/validation/test split contain 5,749/1,500/1,379 sentence pairs.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
[Needs More Information]
#### Initial Data Collection and Normalization
*To construct the dataset, we first obtained automatic translations using Google's translation engine. These were then manually checked, corrected, and cross-validated by human volunteers. *
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
#### Who are the annotators?
### 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-SA 4.0 License
### Citation Information
```
@inproceedings{dumitrescu2021liro,
title={Liro: Benchmark and leaderboard for romanian language tasks},
author={Dumitrescu, Stefan Daniel and Rebeja, Petru and Lorincz, Beata and Gaman, Mihaela and Avram, Andrei and Ilie, Mihai and Pruteanu, Andrei and Stan, Adriana and Rosia, Lorena and Iacobescu, Cristina and others},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},
year={2021}
}
```
### Contributions
Thanks to [@lorinczb](https://github.com/lorinczb) for adding this dataset. | [
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|
RohanAiLab/persian_daily_news | RohanAiLab | 2022-10-21T16:13:09Z | 289 | 0 | [
"source_datasets:original",
"language:fa",
"region:us"
]
| [
"Summarization",
"sequence-modeling"
]
| 2022-03-02T23:29:22Z | ---
pretty_name: Persian Daily News
language:
- fa
source_datasets:
- original
task_categories:
- Summarization
- sequence-modeling
---
# Persian Daily News
# Dataset Summary
persian_daily_news is a collection of 2 million of unique news articles with the headline for each article. dataset can be used in abstractive summarization and paraphrasing tasks.
This effort is part of a bigger perspective to have several datasets in Persian language(and other low resources languages) for different tasks that have two important factors: `free` and `easy-to-use`. Here is a quick HOW-TO for using this dataset in datasets library:[Demo-datasets](https://saied71.github.io/RohanAiLab/2021/09/03/Demo-datasets.html)
# Description
As discussed before, this dataset contains 2M news articles. Each article has these two attributes: text and summary. Here is a sample of dataset:
```
text: به گزارش گروه بین الملل ، خبرگزاری رسمی قطر اعلام کرد، بعد از امضای موافقتنامه همکاری نظامی بین قطر و روسیه این امکان فراهم شده است تا نظامیان قطری برای تکمیل آموزشهای نظامی خود عازم روسیه شده و در آنجا تعلیم ببینند.در چارچوب این قرارداد که امروز یک شنبه توسط سرتیپ ستاد عبدالعزیز صالح السلیطی رییس هییت همکاریهای بین المللی نظامی قطر و سرلشکر ویکتور جوریمیکین رییس اداره عمومی نیروی انسانی وزارت دفاع روسیه به امضا رسید، روابط نظامی بین دوحه و مسکو در زمینه موسسات آموزشهای نظامی شاهد توسه قابل توجهی خواهد شد.به نوشته این خبرگزاری روابط قطر و روسیه در حال گسترش بوده و به سوی شکلگیری مشارکت راهبردی در تمامی زمینهها پیش میرود.
summary: از این پس نظامیان قطری برای آموزش عازم روسیه شده و در موسسات آموزش نظامی این کشور تعلیم خواهند دید.
```
# Citation
```
[email protected]
title={persian_daily_news},
author={Saied Alimoradi},
year={2021}
}
``` | [
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|
RohanAiLab/persian_news_dataset | RohanAiLab | 2022-10-21T16:13:59Z | 289 | 1 | [
"task_categories:text-classification",
"task_ids:language-modeling",
"task_ids:multi-class-classification",
"source_datasets:original",
"language:fa",
"region:us"
]
| [
"text-classification",
"sequence-modeling"
]
| 2022-03-02T23:29:22Z | ---
pretty_name: persian_news_datset
language:
- fa
source_datasets:
- original
task_categories:
- text-classification
- sequence-modeling
task_ids:
- language-modeling
- multi-class-classification
---
# Persian_News_Dataset
# Dataset Summary
persian_news_dataset is a collection of 5 million news articles. News articles have been gathered from more than 10 news agencies for the last 12 years. This dataset can be used in different NLP tasks like language modeling, classification, supervised topic modeling,...
This effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: `free` and `easy-to-use`. Here is a quick HOW-TO for using this dataset in datasets library:[Demo-datasets](https://saied71.github.io/RohanAiLab/2021/09/03/Demo-datasets.html)
# Description
As discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:
```
text :سهشنبه شب از دور برگشت مرحله نیمهنهایی لیگ قهرمانان اروپا، منچسترسیتی در ورزشگاه «اتحاد» میزبان پاریسنژرمن بود و با ارائه نمایشی حساب شده و تحسین برانگیز به پیروزی دو بر صفر دست یافت.بازی رفت در پاریس با برتری دو بر یک سیتی به اتمام رسیده بود و با این اوصاف تیم تحت هدایت «پپ گواردیولا» در مجموع با پیروزی چهار بر یک، راهی فینال شد.بارش برف موجب سفیدپوش شدن زمین شده بود و همین امر بر عملکرد تیمها تاثیر گذاشت. دیدار در حالی آغاز به کار کرد که «امباپه» ستاره پاریسیها که به تازگی از مصدومیت رهایی پیدا کرده است، نیمکتنشین بود.بازی با حملات میهمان آغاز شد و در دقیقه هفتم داور هلندی با تصمیمی عجیب اعتقاد داشت توپ به دست «زینچنکو» مدافع سیتی برخورد کرده و نقطه پنالتی را نشان داد، اما با استفاده از سیستم کمک داور ویدئویی، پنالتی پس گرفته شد. سیتی خیلی زود به هدفش رسید و در دقیقه ۱۰ حرکت عالی او و پاس به «دیبروین» موجب شد تا توپ در یک رفت و برگشت به «ریاض محرز» رسیده و این بازیکن الجزایری گل نخست بازی را برای میزبان به ارمغان آورد.در دقیقه ۱۶ ضربه سر «مارکینیوش» مدافع پیشتاخته پاریسنژرمن با بدشانسی به تیرک دروازه سیتی برخورد کرد.در ادامه برای دقایقی، بازیکنان در میانه میدان خطاهای متعددی انجام دادند و این امر موجب ایجاد چند درگیری شد.هرچند نماینده فرانسه درپی جبران مافات بود اما برنامهای برای رسیدن به این مهم نداشت تا نیمه نخست با همین یک گل همراه شود.در نیمه دوم هم حملات پاریسیها سودی نداشت و در طرف مقابل منچسترسیتی، بازی بسیار هوشمندانهای ارائه کرد.در دقیقه ۶۲ و در ضد حملهای برق آسا، «فیل فودن» با پاسی عالی توپ را به «ریاض محرز» رساند تا این بازیکن گل دوم خود و تیمش را ثبت کرده و سند صعود سیتی به فینال را امضا کند.در دقیقه ۶۸ «آنخل دیماریا» وینگر آرژانتینی تیم پاریسنژرمن پس از درگیری با «فرناندینو» با کارت قرمز داور از زمین اخراج شد تا کار تیمش تمام شود.در این بازی پاریسنژرمن با تفکرات «پوچتینو»، طراحی حملات خود را به «نیمار» سپرده بود اما این بازیکن مطرح برزیلی با حرکات انفرادی بیش از از اندازه، عملکرد خوبی نداشت و حملات تیمش را خراب کرد.در نهایت بازی با پیروزی سیتی همراه شد و مالکان ثروتمند منچسترسیتی به آرزوی خود رسیده و پس از سالها سرمایهگذاری به دیدار نهایی رسیدند. این اولین حضور سیتی در فینال لیگ قهرمانان اروپا است.چهارشنبه شب در دیگر دیدار دور برگشت نیمهنهایی، چلسی انگلیس در ورزشگاه «استمفورد بریج» شهر لندن پذیرای رئالمادرید اسپانیا است. بازی رفت با تساوی یک بر یک به اتمام رسید
title:آرزوی سیتی برآورده شد؛ صعود شاگردان «گواردیولا» به فینال
category:ورزش
```
# Citation
```
[email protected]
title={persian_news_dataset},
author={Saied Alimoradi},
year={2021}
}
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|
clane9/NSD-Flat | clane9 | 2023-09-28T01:26:21Z | 289 | 5 | [
"task_categories:image-to-image",
"task_categories:object-detection",
"size_categories:100K<n<1M",
"license:other",
"biology",
"neuroscience",
"fmri",
"region:us"
]
| [
"image-to-image",
"object-detection"
]
| 2023-07-20T21:40:43Z | ---
license: other
dataset_info:
features:
- name: subject_id
dtype: int64
- name: trial_id
dtype: int64
- name: session_id
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- name: nsd_id
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splits:
- name: train
num_bytes: 26695182666.0
num_examples: 195000
- name: test
num_bytes: 2461280671.0
num_examples: 18000
download_size: 22565691383
dataset_size: 29156463337.0
task_categories:
- image-to-image
- object-detection
tags:
- biology
- neuroscience
- fmri
size_categories:
- 100K<n<1M
---
# NSD-Flat
[[`GitHub`]](https://github.com/clane9/NSD-Flat) [[🤗 `Hugging Face Hub`]](https://huggingface.co/datasets/clane9/NSD-Flat)
A Hugging Face dataset of pre-processed brain activity flat maps from the [Natural Scenes Dataset](https://naturalscenesdataset.org/), constrained to a visual cortex region of interest and rendered as PNG images.
## Load the dataset
Load the dataset from [Hugging Face Hub](https://huggingface.co/datasets/clane9/NSD-Flat)
```python
from datasets import load_dataset
dataset = load_dataset("clane9/NSD-Flat", split="train")
```
## Building the dataset
### 1. Download source data
Run [`download_data.sh`](download_data.sh) to download the required source data:
- NSD stimuli images and presentation info
- COCO annotations
- NSD beta activity maps in fsaverge surface space
```bash
bash download_data.sh
```
### 2. Convert the COCO annotations
Run [`convert_nsd_annotations.py`](convert_nsd_annotations.py) to crop and reorganize the COCO annotations for NSD.
```bash
python convert_nsd_annotations.py
```
### 3. Generate the dataset
Run [`generate_dataset.py`](generate_dataset.py) to generate the huggingface dataset in Arrow format.
```bash
python generate_dataset.py --img_size 256 --workers 8
```
## Citation
If you find this dataset useful, please consider citing:
```
@article{allen2022massive,
title = {A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence},
author = {Allen, Emily J and St-Yves, Ghislain and Wu, Yihan and Breedlove, Jesse L and Prince, Jacob S and Dowdle, Logan T and Nau, Matthias and Caron, Brad and Pestilli, Franco and Charest, Ian and others},
journal = {Nature neuroscience},
volume = {25},
number = {1},
pages = {116--126},
year = {2022},
publisher = {Nature Publishing Group US New York}
}
```
```
@misc{lane2023nsdflat,
author = {Connor Lane},
title = {NSD-Flat: Pre-processed brain activity flat maps from the Natural Scenes Dataset},
howpublished = {\url{https://huggingface.co/datasets/clane9/NSD-Flat}},
year = {2023},
}
```
## License
Usage of this dataset constitutes agreement to the [NSD Terms and Conditions](https://cvnlab.slite.page/p/IB6BSeW_7o/Terms-and-Conditions). | [
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|
open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla | open-llm-leaderboard | 2023-09-17T08:49:15Z | 289 | 0 | [
"region:us"
]
| null | 2023-08-18T18:47:31Z | ---
pretty_name: Evaluation run of l3utterfly/open-llama-3b-v2-layla
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [l3utterfly/open-llama-3b-v2-layla](https://huggingface.co/l3utterfly/open-llama-3b-v2-layla)\
\ 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_l3utterfly__open-llama-3b-v2-layla\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T08:49:03.131155](https://huggingface.co/datasets/open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla/blob/main/results_2023-09-17T08-49-03.131155.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\": 0.011954697986577181,\n\
\ \"em_stderr\": 0.0011130056898859086,\n \"f1\": 0.07875629194630916,\n\
\ \"f1_stderr\": 0.0018920865515620476,\n \"acc\": 0.3194349118852447,\n\
\ \"acc_stderr\": 0.008202509803690292\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.011954697986577181,\n \"em_stderr\": 0.0011130056898859086,\n\
\ \"f1\": 0.07875629194630916,\n \"f1_stderr\": 0.0018920865515620476\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01061410159211524,\n \
\ \"acc_stderr\": 0.0028227133223877035\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6282557221783741,\n \"acc_stderr\": 0.013582306284992879\n\
\ }\n}\n```"
repo_url: https://huggingface.co/l3utterfly/open-llama-3b-v2-layla
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_18T14_37_31.844402
path:
- '**/details_harness|arc:challenge|25_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T08_49_03.131155
path:
- '**/details_harness|drop|3_2023-09-17T08-49-03.131155.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T08-49-03.131155.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T08_49_03.131155
path:
- '**/details_harness|gsm8k|5_2023-09-17T08-49-03.131155.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T08-49-03.131155.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hellaswag|10_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T14:37:31.844402.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T14:37:31.844402.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T14:37:31.844402.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T08_49_03.131155
path:
- '**/details_harness|winogrande|5_2023-09-17T08-49-03.131155.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T08-49-03.131155.parquet'
- config_name: results
data_files:
- split: 2023_08_18T14_37_31.844402
path:
- results_2023-08-18T14:37:31.844402.parquet
- split: 2023_09_17T08_49_03.131155
path:
- results_2023-09-17T08-49-03.131155.parquet
- split: latest
path:
- results_2023-09-17T08-49-03.131155.parquet
---
# Dataset Card for Evaluation run of l3utterfly/open-llama-3b-v2-layla
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/l3utterfly/open-llama-3b-v2-layla
- **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 [l3utterfly/open-llama-3b-v2-layla](https://huggingface.co/l3utterfly/open-llama-3b-v2-layla) 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_l3utterfly__open-llama-3b-v2-layla",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T08:49:03.131155](https://huggingface.co/datasets/open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla/blob/main/results_2023-09-17T08-49-03.131155.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": 0.011954697986577181,
"em_stderr": 0.0011130056898859086,
"f1": 0.07875629194630916,
"f1_stderr": 0.0018920865515620476,
"acc": 0.3194349118852447,
"acc_stderr": 0.008202509803690292
},
"harness|drop|3": {
"em": 0.011954697986577181,
"em_stderr": 0.0011130056898859086,
"f1": 0.07875629194630916,
"f1_stderr": 0.0018920865515620476
},
"harness|gsm8k|5": {
"acc": 0.01061410159211524,
"acc_stderr": 0.0028227133223877035
},
"harness|winogrande|5": {
"acc": 0.6282557221783741,
"acc_stderr": 0.013582306284992879
}
}
```
### 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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|
toughdata/quora-question-answer-dataset | toughdata | 2023-08-28T13:36:21Z | 289 | 0 | [
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:text2text-generation",
"language:en",
"license:gpl-3.0",
"question",
"answer",
"quora",
"region:us"
]
| [
"question-answering",
"conversational",
"text2text-generation"
]
| 2023-08-23T22:53:09Z | ---
license: gpl-3.0
task_categories:
- question-answering
- conversational
- text2text-generation
language:
- en
tags:
- question
- answer
- quora
pretty_name: Quora Question/Answer Pairs
---
Quora Question Answer Dataset (Quora-QuAD) contains 56,402 question-answer pairs scraped from Quora.
# Usage:
For instructions on fine-tuning a model (Flan-T5) with this dataset, please check out the article: https://www.toughdata.net/blog/post/finetune-flan-t5-question-answer-quora-dataset | [
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|
consumer-finance-complaints | null | 2023-01-25T14:28:37Z | 288 | 11 | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: consumer-finance-complaints
dataset_info:
features:
- name: Date Received
dtype: timestamp[s]
- name: Product
dtype:
class_label:
names:
'0': Credit reporting, credit repair services, or other personal consumer
reports
'1': Debt collection
'2': Mortgage
'3': Credit card or prepaid card
'4': Checking or savings account
'5': Credit reporting
'6': Student loan
'7': Money transfer, virtual currency, or money service
'8': Credit card
'9': Vehicle loan or lease
'10': Bank account or service
'11': Payday loan, title loan, or personal loan
'12': Consumer Loan
'13': Payday loan
'14': Money transfers
'15': Prepaid card
'16': Other financial service
'17': Virtual currency
- name: Sub Product
dtype:
class_label:
names:
'0': Credit reporting
'1': General-purpose credit card or charge card
'2': Checking account
'3': Other debt
'4': Second mortgage
'5': Conventional home mortgage
'6': I do not know
'7': Credit card debt
'8': Medical debt
'9': Federal student loan servicing
'10': FHA mortgage
'11': Conventional fixed mortgage
'12': Loan
'13': Other (i.e. phone, health club, etc.)
'14': Store credit card
'15': Installment loan
'16': Credit card
'17': Medical
'18': Mobile or digital wallet
'19': Private student loan
'20': Non-federal student loan
'21': Domestic (US) money transfer
'22': VA mortgage
'23': Vehicle loan
'24': Auto debt
'25': Payday loan
'26': Conventional adjustable mortgage (ARM)
'27': Other personal consumer report
'28': Payday loan debt
'29': Savings account
'30': Virtual currency
'31': Other bank product/service
'32': Other type of mortgage
'33': Other banking product or service
'34': Other mortgage
'35': International money transfer
'36': Lease
'37': General-purpose prepaid card
'38': Home equity loan or line of credit (HELOC)
'39': Government benefit card
'40': Mortgage debt
'41': Personal line of credit
'42': Home equity loan or line of credit
'43': Federal student loan debt
'44': Private student loan debt
'45': Credit repair services
'46': Title loan
'47': Auto
'48': Vehicle lease
'49': Mortgage
'50': Reverse mortgage
'51': General purpose card
'52': CD (Certificate of Deposit)
'53': Federal student loan
'54': Payroll card
'55': Debt settlement
'56': Check cashing service
'57': Traveler's check or cashier's check
'58': Gift card
'59': (CD) Certificate of deposit
'60': Money order
'61': Foreign currency exchange
'62': Refund anticipation check
'63': Gift or merchant card
'64': Cashing a check without an account
'65': ID prepaid card
'66': Mobile wallet
'67': Government benefit payment card
'68': Pawn loan
'69': Other special purpose card
'70': Check cashing
'71': Credit repair
'72': Traveler’s/Cashier’s checks
'73': Transit card
'74': Student prepaid card
'75': Electronic Benefit Transfer / EBT card
'76': ''
- name: Issue
dtype: string
- name: Sub Issue
dtype: string
- name: Complaint Text
dtype: string
- name: Company Public Response
dtype: string
- name: Company
dtype: string
- name: State
dtype: string
- name: Zip Code
dtype: string
- name: Tags
dtype:
class_label:
names:
'0': Servicemember
'1': Older American
'2': Older American, Servicemember
'3': ''
- name: Consumer Consent Provided
dtype: string
- name: Submitted via
dtype: string
- name: Date Sent To Company
dtype: string
- name: Company Response To Consumer
dtype: string
- name: Timely Response
dtype: string
- name: Consumer Disputed
dtype: string
- name: Complaint ID
dtype: string
splits:
- name: train
num_bytes: 1605177353
num_examples: 2455765
download_size: 404187716
dataset_size: 1605177353
---
# Dataset Card for Consumer Finance Complaints
## 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://www.consumerfinance.gov/data-research/consumer-complaints/
- **Repository:**
https://github.com/cfpb/consumerfinance.gov
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This database is a collection of complaints about consumer financial products and services that we sent to companies for response.
The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets, are not published in the Consumer Complaint Database. The database generally updates daily.
Complaints can give us insights into problems people are experiencing in the marketplace and help us regulate consumer financial products and services under existing federal consumer financial laws, enforce those laws judiciously, and educate and empower consumers to make informed financial decisions. We also report on complaint trends annually in Consumer Response’s Annual Report to Congress.
### Supported Tasks and Leaderboards
Text Classification Tasks
| Task | Label Name | Description | SOTA |
| ----------- | ----------- |----------- | ----------- |
| Text Classification | Product| Predict the related product of a complaint | N/A |
| Task | Label Name | Description | SOTA |
| ----------- | ----------- |----------- | ----------- |
| Text Classification | Sub-Product| Predict the related sub product of a complaint | N/A |
| Task | Label Name | Description | SOTA |
| ----------- | ----------- |----------- | ----------- |
| Text Classification | Tags | Predict whether a complaint has been made by someone elderly or a service person| N/A |
### Languages
English
## Dataset Structure
### Data Instances
This dataset is a point in time extract of the database, the database increases in size every day
An example of 'train' looks as follows.
```
{
"Complaint ID": "4511031",
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub Issue": "Credit inquiries on your report that you don't recognize",
"Consumer Disputed": "N/A",
"Sub Product": "Credit reporting",
"State": "TX",
"Tags": "Older American, Servicemember",
"Company Public Response": "",
"Zip Code": "75202",
"Issue": "Improper use of your report",
"Submitted via": "Web",
"Company Response To Consumer": "Closed with explanation",
"Complaint Text": "I am XXXX XXXX and I am submitting this complaint myself and there is no third party involved. Despite the multiple previous written requests, the unverified inquiries listed below still remain on my credit report in violation of Federal Law. The Equifax Credit Bureau failed to comply with Fair Credit Reporting Act, XXXX XXXX sections XXXX within the time set forth by law and continued reporting of erroneous information which now, given all my attempts to address it directly with the creditor, as willful negligence and non-compliance with federal statutes. PLEASE REMOVE THE FOLLOWING INQUIRIES COMPLETELY FROM MY CREDIT REPORT : XXXX CARD-Date of inquiry XX/XX/XXXX XXXX CARD-Date of inquiry XX/XX/XXXX",
"Date Received": "07-02-2021",
"Company": "EQUIFAX, INC.",
"Consumer Consent Provided": "Consent not provided",
"Timely Response": "Yes",
"Date Sent To Company": "2021-07-02"
}
```
### Data Fields
| Field | name | Description | Data Type |
| ----------- | ----------- |----------- | ----------- |
| Date received | The date the CFPB received the complaint | date & time | |
| Product | The type of product the consumer identified in the complaint | plain text | This field is a categorical variable. |
| Sub-product | The type of sub-product the consumer identified in the complaint | plain text | This field is a categorical variable. Not all Products have Sub-products. |
| Issue | The issue the consumer identified in the complaint | plain text | This field is a categorical variable. Possible values are dependent on Product. |
| Sub-issue | The sub-issue the consumer identified in the complaint | plain text | This field is a categorical variable. Possible values are dependent on product and issue. Not all Issues have corresponding Sub-issues. |
| Consumer complaint narrative | Consumer complaint narrative is the consumer-submitted description of "what happened" from the complaint. Consumers must opt-in to share their narrative. We will not publish the narrative unless the consumer consents, and consumers can opt-out at any time. The CFPB takes reasonable steps to scrub personal information from each complaint that could be used to identify the consumer. | plain text | Consumers' descriptions of what happened are included if consumers consent to publishing the description and after we take steps to remove personal information. |
| Company public response | The company's optional, public-facing response to a consumer's complaint. Companies can choose to select a response from a pre-set list of options that will be posted on the public database. For example, "Company believes complaint is the result of an isolated error." | plain text | Companies' public-facing responses to complaints are included if companies choose to publish one. Companies may select a public response from a set list of options as soon as they respond to the complaint, but no later than 180 days after the complaint was sent to the company for response. |
| Company | The complaint is about this company | plain text | This field is a categorical variable. |
| State | The state of the mailing address provided by the consumer | plain text | This field is a categorical variable. |
| ZIP code | The mailing ZIP code provided by the consumer | plain text | Mailing ZIP code provided by the consumer. This field may: i) include the first five digits of a ZIP code; ii) include the first three digits of a ZIP code (if the consumer consented to publication of their complaint narrative); or iii) be blank (if ZIP codes have been submitted with non-numeric values, if there are less than 20,000 people in a given ZIP code, or if the complaint has an address outside of the United States). For example, complaints where the submitter reports the age of the consumer as 62 years or older are tagged, ‘Older American.’ Complaints submitted by or on behalf of a servicemember or the spouse or dependent of a servicemember are tagged, ‘Servicemember.’ Servicemember includes anyone who is active duty, National Guard, or Reservist, as well as anyone who previously served and is a Veteran or retiree. |
| Tags | Data that supports easier searching and sorting of complaints submitted by or on behalf of consumers. | plain text | |
| Consumer consent provided? | Identifies whether the consumer opted in to publish their complaint narrative. We do not publish the narrative unless the consumer consents and consumers can opt-out at any time. | plain text | This field shows whether a consumer provided consent to publish their complaint narrative |
| Submitted via | How the complaint was submitted to the CFPB | plain text | This field is a categorical variable. |
| Date sent to company | The date the CFPB sent the complaint to the company | date & time | |
| Company response to consumer | This is how the company responded. For example, "Closed with explanation." | plain text | This field is a categorical variable. |
| Timely response? | Whether the company gave a timely response | plain text | yes/no |
| Consumer disputed? | Whether the consumer disputed the company’s response | plain text | YES/ NO/ N/A: The Bureau discontinued the consumer dispute option on April 24, 2017. |
| Complaint ID | The unique identification number for a complaint | number | |
### Data Splits
This dataset only contains a TRAIN set - this can be further split into TRAIN, TEST and VALIDATE subsets with the datasets library
## Dataset Creation
### Curation Rationale
Open sourcing customer complaints
### Source Data
https://cfpb.github.io/api/ccdb/
#### Initial Data Collection and Normalization
This database is maintained by the Consumer Financial Protection Bureau
#### Who are the source language producers?
English
### Annotations
#### Annotation process
User submitted to the CFPB
#### Who are the annotators?
N/A
### Personal and Sensitive Information
All PII data has been anonymised
## Considerations for Using the Data
### Social Impact of Dataset
N/A
### Discussion of Biases
This database is not a statistical sample of consumers’ experiences in the marketplace. Complaints are not necessarily representative of all consumers’ experiences and complaints do not constitute “information” for purposes of the Information Quality Act .
Complaint volume should be considered in the context of company size and/or market share. For example, companies with more customers may have more complaints than companies with fewer customers. We encourage you to pair complaint data with public and private data sets for additional context.
The Bureau publishes the consumer’s narrative description of his or her experience if the consumer opts to share it publicly and after the Bureau takes steps to remove personal information. We don’t verify all the allegations in complaint narratives. Unproven allegations in consumer narratives should be regarded as opinion, not fact. We do not adopt the views expressed and make no representation that consumers’ allegations are accurate, clear, complete, or unbiased in substance or presentation. Users should consider what conclusions may be fairly drawn from complaints alone.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
https://cfpb.github.io/api/ccdb/
### Licensing Information
Creative Commons Zero v1.0 Universal
### Citation Information
N/A
### Contributions
Thanks to [@kayvane1](https://github.com/kayvane1) for adding this dataset and to the [Consumer Financial Protection Bureau](https://cfpb.github.io/) for publishing it. | [
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|
ehealth_kd | null | 2023-01-25T14:29:46Z | 288 | 1 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:es",
"license:cc-by-nc-sa-4.0",
"relation-prediction",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- es
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: eHealth-KD
tags:
- relation-prediction
dataset_info:
features:
- name: sentence
dtype: string
- name: entities
list:
- name: ent_id
dtype: string
- name: ent_text
dtype: string
- name: ent_label
dtype:
class_label:
names:
'0': Concept
'1': Action
'2': Predicate
'3': Reference
- name: start_character
dtype: int32
- name: end_character
dtype: int32
- name: relations
list:
- name: rel_id
dtype: string
- name: rel_label
dtype:
class_label:
names:
'0': is-a
'1': same-as
'2': has-property
'3': part-of
'4': causes
'5': entails
'6': in-time
'7': in-place
'8': in-context
'9': subject
'10': target
'11': domain
'12': arg
- name: arg1
dtype: string
- name: arg2
dtype: string
config_name: ehealth_kd
splits:
- name: train
num_bytes: 425713
num_examples: 800
- name: validation
num_bytes: 108154
num_examples: 199
- name: test
num_bytes: 47314
num_examples: 100
download_size: 565900
dataset_size: 581181
---
# Dataset Card for eHealth-KD
## 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:** [eHealth-KD homepage](https://knowledge-learning.github.io/ehealthkd-2020/)
- **Repository:** [eHealth-KD repository](https://github.com/knowledge-learning/ehealthkd-2020)
- **Paper:** [eHealth-KD overview paper](http://ceur-ws.org/Vol-2664/eHealth-KD_overview.pdf)
- **Leaderboard:** [eHealth-KD Challenge 2020 official results](https://knowledge-learning.github.io/ehealthkd-2020/results)
- **Point of Contact:** [Yoan Gutiérrez Vázquez](mailto:[email protected]) (Organization Committee), [María Grandury](mailto:[email protected]) (Dataset Submitter)
### Dataset Summary
Dataset of the eHealth-KD Challenge at IberLEF 2020. It is designed for the identification of semantic
entities and relations in Spanish health documents.
### Supported Tasks and Leaderboards
The eHealth-KD challenge proposes two computational subtasks:
- `named-entity-recognition`: Given a sentence of an eHealth document written in Spanish, the goal of this subtask is to
identify all the entities and their types.
- `relation-prediction`: The purpose of this subtask is to recognise all relevant semantic relationships between the entities recognised.
For an analysis of the most successful approaches of this challenge, read the [eHealth-KD overview paper](http://ceur-ws.org/Vol-2664/eHealth-KD_overview.pdf).
### Languages
The text in the dataset is in Spanish (BCP-47 code: `es`).
## Dataset Structure
### Data Instances
The first example of the eHeatlh-KD Corpus train set looks as follows:
```
{
'sentence': 'En la leucemia linfocítica crónica, hay demasiados linfocitos, un tipo de glóbulos blancos.',
'entities': {
[
'ent_id: 'T1',
'ent_text': 'leucemia linfocítica crónica',
'ent_label': 0,
'start_character': 6,
'end_character': 34
],
[
'ent_id: 'T2',
'ent_text': 'linfocitos',
'ent_label': 0,
'start_character': 51,
'end_character': 61
],
[
'ent_id: 'T3',
'ent_text': 'glóbulos blancos',
'ent_label': 0,
'start_character': 74,
'end_character': 90
]
},
relations: {
[
'rel_id: 'R0'
'rel_label': 0,
'arg1': T2
'arg2': T3
],
[
'rel_id': 'R1'
'rel_label': 5,
'arg1': T1,
'arg2': T2
]
}
}
```
### Data Fields
- `sentence`: sentence of an eHealth document written in Spanish
- `entities`: list of entities identified in the sentence
- `ent_id`: entity identifier (`T`+ a number)
- `ent_text`: entity, can consist of one or more complete words (i.e., not a prefix or a suffix of a word), and will
never include any surrounding punctuation symbols, parenthesis, etc.
- `ent_label`: type of entity (`Concept`, `Action`, `Predicate` or `Reference`)
- `start_character`: position of the first character of the entity
- `end_character`: position of the last character of the entity
- `relations`: list of semantic relationships between the entities recognised
- `rel_id`: relation identifier (`R` + a number)
- `rel_label`: type of relation, can be a general relation (`is-a`, `same-as`, `has-property`, `part-of`, `causes`, `entails`),
a contextual relation (`in-time`, `in-place`, `in-context`) an action role (`subject`, `target`) or a predicate role (`domain`, `arg`).
- `arg1`: ID of the first entity of the relation
- `arg2`: ID of the second entity of the relation
For more information about the types of entities and relations, click [here](https://knowledge-learning.github.io/ehealthkd-2020/tasks).
### Data Splits
The data is split into a training, validation and test set. The split sizes are as follow:
| | Train | Val | Test |
| ----- | ------ | ----- | ---- |
| eHealth-KD 2020 | 800 | 199 | 100 |
In the challenge there are 4 different scenarios for testing. The test data of this dataset corresponds to the third scenario.
More information about the testing data [here](https://github.com/knowledge-learning/ehealthkd-2020/tree/master/data/testing).
## Dataset Creation
### Curation Rationale
The vast amount of clinical text available online has motivated the development of automatic
knowledge discovery systems that can analyse this data and discover relevant facts.
The eHealth Knowledge Discovery (eHealth-KD) challenge, in its third edition, leverages
a semantic model of human language that encodes the most common expressions of factual
knowledge, via a set of four general-purpose entity types and thirteen semantic relations among
them. The challenge proposes the design of systems that can automatically annotate entities and
relations in clinical text in the Spanish language.
### Source Data
#### Initial Data Collection and Normalization
As in the previous edition, the corpus for eHealth-KD 2020 has been extracted from MedlinePlus sources. This platform
freely provides large health textual data from which we have made a selection for constituting the eHealth-KD corpus.
The selection has been made by sampling specific XML files from the collection available in the [Medline website](https://medlineplus.gov/xml.html).
```
“MedlinePlus is the National Institutes of Health’s Website for patients and their families and
friends. Produced by the National Library of Medicine, the world’s largest medical library, it
brings you information about diseases, conditions, and wellness issues in language you can
understand. MedlinePlus offers reliable, up-to-date health information, anytime, anywhere, for free.”
```
These files contain several entries related to health and medicine topics and have been processed to remove all
XML markup to extract the textual content. Only Spanish language items were considered. Once cleaned, each individual
item was converted to a plain text document, and some further post-processing is applied to remove unwanted sentences,
such as headers, footers and similar elements, and to flatten HTML lists into plain sentences.
#### Who are the source language producers?
As in the previous edition, the corpus for eHealth-KD 2020 was extracted from [MedlinePlus](https://medlineplus.gov/xml.html) sources.
### Annotations
#### Annotation process
Once the MedlinePlus files were cleaned, they were manually tagged using [BRAT](http://brat.nlplab.org/) by a group of
annotators. After tagging, a post-processing was applied to BRAT’s output files (ANN format) to obtain the output files
in the formats needed for the challenge.
#### Who are the annotators?
The data was manually tagged.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
"The eHealth-KD 2020 proposes –as the previous editions– modeling the human language in a scenario in which Spanish
electronic health documents could be machine-readable from a semantic point of view.
With this task, we expect to encourage the development of software technologies to automatically extract a large variety
of knowledge from eHealth documents written in the Spanish Language."
### 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
#### Organization Committee
| Name | Email | Institution |
|:---------------------------------------:|:---------------------:|:-----------------------------:|
| Yoan Gutiérrez Vázquez (contact person) | [email protected] | University of Alicante, Spain |
| Suilan Estévez Velarde | [email protected] | University of Havana, Cuba |
| Alejandro Piad Morffis | [email protected] | University of Havana, Cuba |
| Yudivián Almeida Cruz | [email protected] | University of Havana, Cuba |
| Andrés Montoyo Guijarro | [email protected] | University of Alicante, Spain |
| Rafael Muñoz Guillena | [email protected] | University of Alicante, Spain |
#### Funding
This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University
of Havana. Moreover, it has also been partially funded by both aforementioned universities, IUII, Generalitat Valenciana,
Spanish Government, Ministerio de Educación, Cultura y Deporte through the projects SIIA (PROMETEU/2018/089) and
LIVINGLANG (RTI2018-094653-B-C22).
### Licensing Information
This dataset is under the Attribution-NonCommercial-ShareAlike 4.0 International
[(CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
To accept the distribution terms, please fill in the following [form](https://forms.gle/pUJutSDq2FYLwNWQA).
### Citation Information
In the following link you can find the
[preliminar bibtexts of the systems’ working-notes](https://knowledge-learning.github.io/ehealthkd-2020/shared/eHealth-KD_2020_bibtexts.zip).
In addition, to cite the eHealth-KD challenge you can use the following preliminar bibtext:
```
@inproceedings{overview_ehealthkd2020,
author = {Piad{-}Morffis, Alejandro and
Guti{\'{e}}rrez, Yoan and
Ca{\~{n}}izares-Diaz, Hian and
Estevez{-}Velarde, Suilan and
Almeida{-}Cruz, Yudivi{\'{a}}n and
Mu{\~{n}}oz, Rafael and
Montoyo, Andr{\'{e}}s},
title = {Overview of the eHealth Knowledge Discovery Challenge at IberLEF 2020},
booktitle = ,
year = {2020},
}
```
### Contributions
Thanks to [@mariagrandury](https://github.com/mariagrandury) for adding this dataset. | [
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|
ARTeLab/ilpost | ARTeLab | 2022-11-17T02:50:32Z | 288 | 2 | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"language:it",
"region:us"
]
| [
"summarization"
]
| 2022-03-02T23:29:22Z | ---
language:
- it
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
task_categories:
- summarization
---
# Dataset Card for ilpost
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
IlPost dataset, containing news articles taken from IlPost.
There are two features:
- source: Input news article.
- target: Summary of the article.
### Supported Tasks and Leaderboards
- `abstractive-summarization`, `summarization`
### Languages
The text in the dataset is in Italian
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## 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
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
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|
sled-umich/Action-Effect | sled-umich | 2022-10-14T19:12:20Z | 288 | 1 | [
"task_categories:image-classification",
"task_categories:image-to-text",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:eng",
"region:us"
]
| [
"image-classification",
"image-to-text"
]
| 2022-10-12T20:08:03Z | ---
annotations_creators:
- crowdsourced
language:
- eng
language_creators:
- crowdsourced
license: []
multilinguality:
- monolingual
pretty_name: Action-Effect-Prediction
size_categories:
- 1K<n<10K
source_datasets:
- original
tags: []
task_categories:
- image-classification
- image-to-text
task_ids: []
---
# Physical-Action-Effect-Prediction
Official dataset for ["What Action Causes This? Towards Naive Physical Action-Effect Prediction"](https://aclanthology.org/P18-1086/), ACL 2018.
## Overview
Despite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apart into smaller pieces. If artificial agents (e.g., robots) ever become our partners in joint tasks, it is critical to empower them with such action-effect understanding so that they can reason about the state of the world and plan for actions. Towards this goal, this paper introduces a new task on naive physical action-effect prediction, which addresses the relations between concrete actions (expressed in the form of verb-noun pairs) and their effects on the state of the physical world as depicted by images. We collected a dataset for this task and developed an approach that harnesses web image data through distant supervision to facilitate learning for action-effect prediction. Our empirical results have shown that web data can be used to complement a small number of seed examples (e.g., three examples for each action) for model learning. This opens up possibilities for agents to learn physical action-effect relations for tasks at hand through communication with humans with a few examples.
### Datasets
- This dataset contains action-effect information for 140 verb-noun pairs. It has two parts: effects described by natural language, and effects depicted in images.
- The language data contains verb-noun pairs and their effects described in natural language. For each verb-noun pair, its possible effects are described by 10 different annotators. The format for each line is `verb noun, effect_sentence, [effect_phrase_1, effect_phrase_2, effect_phrase_3, ...]`. Effect_phrases were automatically extracted from their corresponding effect_sentences.
- The image data contains images depicting action effects. For each verb-noun pair, an average of 15 positive images and 15 negative images were collected. Positive images are those deemed to capture the resulting world state of the action. And negative images are those deemed to capture some state of the related object (*i.e.*, the nouns in the verb-noun pairs), but are not the resulting state of the corresponding action.
### Download
```python
from datasets import load_dataset
dataset = load_dataset("sled-umich/Action-Effect")
```
* [HuggingFace](https://huggingface.co/datasets/sled-umich/Action-Effect)
* [Google Drive](https://drive.google.com/drive/folders/1P1_xWdCUoA9bHGlyfiimYAWy605tdXlN?usp=sharing)
* Dropbox:
* [Language Data](https://www.dropbox.com/s/pi1ckzjipbqxyrw/action_effect_sentence_phrase.txt?dl=0)
* [Image Data](https://www.dropbox.com/s/ilmfrqzqcbdf22k/action_effect_image_rs.tar.gz?dl=0)
### Cite
[What Action Causes This? Towards Naïve Physical Action-Effect Prediction](https://sled.eecs.umich.edu/publication/dblp-confacl-vanderwende-cyg-18/). *Qiaozi Gao, Shaohua Yang, Joyce Chai, Lucy Vanderwende*. ACL, 2018. [[Paper]](https://aclanthology.org/P18-1086/) [[Slides]](https://aclanthology.org/attachments/P18-1086.Presentation.pdf)
```tex
@inproceedings{gao-etal-2018-action,
title = "What Action Causes This? Towards Naive Physical Action-Effect Prediction",
author = "Gao, Qiaozi and
Yang, Shaohua and
Chai, Joyce and
Vanderwende, Lucy",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1086",
doi = "10.18653/v1/P18-1086",
pages = "934--945",
abstract = "Despite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apart into smaller pieces. If artificial agents (e.g., robots) ever become our partners in joint tasks, it is critical to empower them with such action-effect understanding so that they can reason about the state of the world and plan for actions. Towards this goal, this paper introduces a new task on naive physical action-effect prediction, which addresses the relations between concrete actions (expressed in the form of verb-noun pairs) and their effects on the state of the physical world as depicted by images. We collected a dataset for this task and developed an approach that harnesses web image data through distant supervision to facilitate learning for action-effect prediction. Our empirical results have shown that web data can be used to complement a small number of seed examples (e.g., three examples for each action) for model learning. This opens up possibilities for agents to learn physical action-effect relations for tasks at hand through communication with humans with a few examples.",
}
```
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|
Snit/french-conversation | Snit | 2023-04-29T06:41:29Z | 288 | 5 | [
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:fr",
"license:cc-by-4.0",
"french",
"region:us"
]
| [
"conversational"
]
| 2023-04-29T05:51:26Z | ---
license: cc-by-4.0
language:
- fr
tags:
- french
task_categories:
- conversational
size_categories:
- 1K<n<10K
---
+15 hours of speech data from TTS and text file recording.
+9k utterances from various sources, novels, parliamentary debates, professional language. | [
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|
finer | null | 2023-01-25T14:30:30Z | 287 | 1 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fi",
"license:mit",
"arxiv:1908.04212",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- fi
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: finer
pretty_name: Finnish News Corpus for Named Entity Recognition
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-DATE
'2': B-EVENT
'3': B-LOC
'4': B-ORG
'5': B-PER
'6': B-PRO
'7': I-DATE
'8': I-EVENT
'9': I-LOC
'10': I-ORG
'11': I-PER
'12': I-PRO
- name: nested_ner_tags
sequence:
class_label:
names:
'0': O
'1': B-DATE
'2': B-EVENT
'3': B-LOC
'4': B-ORG
'5': B-PER
'6': B-PRO
'7': I-DATE
'8': I-EVENT
'9': I-LOC
'10': I-ORG
'11': I-PER
'12': I-PRO
config_name: finer
splits:
- name: train
num_bytes: 5159550
num_examples: 13497
- name: validation
num_bytes: 387494
num_examples: 986
- name: test
num_bytes: 1327354
num_examples: 3512
- name: test_wikipedia
num_bytes: 1404397
num_examples: 3360
download_size: 3733127
dataset_size: 8278795
---
# 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:** [Github](https://github.com/mpsilfve/finer-data)
- **Repository:** [Github](https://github.com/mpsilfve/finer-data)
- **Paper:** [Arxiv](https://arxiv.org/abs/1908.04212)
- **Leaderboard:**
- **Point of Contact:**
### 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
Each row consists of the following fields:
* `id`: The sentence id
* `tokens`: An ordered list of tokens from the full text
* `ner_tags`: Named entity recognition tags for each token
* `nested_ner_tags`: Nested named entity recognition tags for each token
Note that by design, the length of `tokens`, `ner_tags`, and `nested_ner_tags` will always be identical.
`ner_tags` and `nested_ner_tags` correspond to the list below:
```
[ "O", "B-DATE", "B-EVENT", "B-LOC", "B-ORG", "B-PER", "B-PRO", "I-DATE", "I-EVENT", "I-LOC", "I-ORG", "I-PER", "I-PRO" ]
```
IOB2 labeling scheme is used.
### 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 [@stefan-it](https://github.com/stefan-it) for adding this dataset. | [
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|
giga_fren | null | 2022-11-03T16:15:21Z | 287 | 0 | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"language:fr",
"license:unknown",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- fr
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: GigaFren
dataset_info:
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- fr
config_name: en-fr
splits:
- name: train
num_bytes: 8690296821
num_examples: 22519904
download_size: 2701536198
dataset_size: 8690296821
---
# Dataset Card for GigaFren
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/giga-fren.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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|
tsac | null | 2023-01-25T14:54:29Z | 287 | 0 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:aeb",
"license:lgpl-3.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- aeb
license:
- lgpl-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: tsac
pretty_name: Tunisian Sentiment Analysis Corpus
dataset_info:
features:
- name: id
dtype: string
- name: sentence
dtype: string
- name: target
dtype:
class_label:
names:
'0': '1'
'1': '-1'
splits:
- name: train
num_bytes: 1020146
num_examples: 13669
- name: test
num_bytes: 268504
num_examples: 3400
download_size: 963015
dataset_size: 1288650
---
# Dataset Card for Tunisian Sentiment Analysis Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** None
- **Repository:** https://github.com/fbougares/TSAC
- **Paper:** https://www.aclweb.org/anthology/W17-1307
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** Salima Mdhaffar ([email protected])
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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|
wrbsc | null | 2023-01-25T15:02:59Z | 287 | 0 | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:cc-by-sa-3.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pl
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
pretty_name: wrbsc
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: relationship
dtype:
class_label:
names:
'0': Krzyżowanie_się
'1': Tło_historyczne
'2': Źródło
'3': Dalsze_informacje
'4': Zawieranie
'5': Opis
'6': Uszczegółowienie
'7': Parafraza
'8': Spełnienie
'9': Mowa_zależna
'10': Zmiana_poglądu
'11': Streszczenie
'12': Tożsamość
'13': Sprzeczność
'14': Modalność
'15': Cytowanie
splits:
- name: train
num_bytes: 779881
num_examples: 2827
download_size: 1273815
dataset_size: 779881
---
# Dataset Card for wrbsc
## 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://clarin-pl.eu/dspace/handle/11321/305
- **Repository:** https://clarin-pl.eu/dspace/handle/11321/305
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
WUT Relations Between Sentences Corpus contains 2827 pairs of related sentences. Relationships are derived from Cross-document Structure Theory (CST), which enables multi-document summarization through identification of cross-document rhetorical relationships within a cluster of related documents. Every relation was marked by at least 3 annotators.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Polish
## Dataset Structure
### Data Instances
An example contains two related sentences and a class representing the type of relationship between those sentences.
```
{'relationship': 0,
'sentence1': 'Znajdujące się w Biurze Bezpieczeństwa Narodowego akta Komisji Weryfikacyjnej WSI zostały przewiezione do siedziby Służby Kontrwywiadu Wojskowego.',
'sentence2': '2008-07-03: Wywiezienie akt dotyczących WSI – sprawa dla prokuratury?'}
```
### Data Fields
- `sentence1`: the first sentence being compared (`string`)
- `sentence2`: the second sentence being compared (`string`)
- `relationship`: the type of relationship between those sentences. Can be one of 16 classes listed below:
- `Krzyżowanie_się`: crossing
- `Tło_historyczne`: historical background
- `Źródło`: source
- `Dalsze_informacje`: additional information
- `Zawieranie`: inclusion
- `Opis`: description
- `Uszczegółowienie`: further detail
- `Parafraza`: paraphrase
- `Spełnienie`: fulfillment
- `Mowa_zależna`: passive voice
- `Zmiana_poglądu`: change of opinion
- `Streszczenie`: summarization
- `Tożsamość`: identity
- `Sprzeczność`: conflict
- `Modalność`: modality
- `Cytowanie`: quotation
### Data Splits
Single train split
## 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
Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
### Citation Information
```
@misc{11321/305,
title = {{WUT} Relations Between Sentences Corpus},
author = {Oleksy, Marcin and Fikus, Dominika and Wolski, Micha{\l} and Podbielska, Ma{\l}gorzata and Turek, Agnieszka and Kędzia, Pawe{\l}},
url = {http://hdl.handle.net/11321/305},
note = {{CLARIN}-{PL} digital repository},
copyright = {Attribution-{ShareAlike} 3.0 Unported ({CC} {BY}-{SA} 3.0)},
year = {2016}
}
```
### Contributions
Thanks to [@kldarek](https://github.com/kldarek) for adding this dataset. | [
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|
yoruba_wordsim353 | null | 2022-11-03T16:07:49Z | 287 | 0 | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"language:yo",
"license:unknown",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
- yo
license:
- unknown
multilinguality:
- multilingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
paperswithcode_id: null
pretty_name: Wordsim-353 In Yorùbá (YorubaWordsim353)
dataset_info:
features:
- name: english1
dtype: string
- name: english2
dtype: string
- name: yoruba1
dtype: string
- name: yoruba2
dtype: string
- name: similarity
dtype: float32
splits:
- name: test
num_bytes: 19299
num_examples: 353
download_size: 17039
dataset_size: 19299
---
# Dataset Card for wordsim-353 in Yorùbá (yoruba_wordsim353)
## 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:** https://github.com/ajesujoba/YorubaTwi-Embedding
- **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335/
- **Leaderboard:** -
- **Point of Contact:** Jesujoba Alabi ( jesujobaoluwadara.alabi (at) dfki.de ) and David Adelani ( didelani (at) lsv.uni-saarland.de )
### Dataset Summary
A translation of the word pair similarity dataset wordsim-353 to Yorùbá.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Yorùbá (ISO 639-1: yo)
## Dataset Structure
### Data Instances
An instance consists of a pair of words as well as their similarity. The dataset contains both the original English words (from wordsim-353) as well as their translation to Yorùbá.
### Data Fields
- `english1`: the first word of the pair; the original English word
- `english2`: the second word of the pair; the original English word
- `yoruba1`: the first word of the pair; translation to Yorùbá
- `yoruba2`: the second word of the pair; translation to Yorùbá
- `similarity`: similarity rating according to the English dataset
### 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 [@michael-aloys](https://github.com/michael-aloys) for adding this dataset. | [
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|
GEM/sportsett_basketball | GEM | 2022-10-24T15:30:28Z | 287 | 6 | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:mit",
"data-to-text",
"region:us"
]
| [
"table-to-text"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- mit
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: sportsett_basketball
tags:
- data-to-text
---
# Dataset Card for GEM/sportsett_basketball
## Dataset Description
- **Homepage:** https://github.com/nlgcat/sport_sett_basketball
- **Repository:** https://github.com/nlgcat/sport_sett_basketball
- **Paper:** https://aclanthology.org/2020.intellang-1.4/
- **Leaderboard:** N/A
- **Point of Contact:** Craig Thomson
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/sportsett_basketball).
### Dataset Summary
The sportsett dataset is an English data-to-text dataset in the basketball domain. The inputs are statistics summarizing an NBA game and the outputs are high-quality descriptions of the game in natural language.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/sportsett_basketball')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/sportsett_basketball).
#### website
[Github](https://github.com/nlgcat/sport_sett_basketball)
#### paper
[ACL Anthology](https://aclanthology.org/2020.intellang-1.4/)
#### authors
Craig Thomson, Ashish Upadhyay
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/nlgcat/sport_sett_basketball)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/nlgcat/sport_sett_basketball)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2020.intellang-1.4/)
#### 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{thomson-etal-2020-sportsett,
title = "{S}port{S}ett:Basketball - A robust and maintainable data-set for Natural Language Generation",
author = "Thomson, Craig and
Reiter, Ehud and
Sripada, Somayajulu",
booktitle = "Proceedings of the Workshop on Intelligent Information Processing and Natural Language Generation",
month = sep,
year = "2020",
address = "Santiago de Compostela, Spain",
publisher = "Association for Computational Lingustics",
url = "https://aclanthology.org/2020.intellang-1.4",
pages = "32--40",
}
```
#### 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 -->
Craig Thomson
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
[email protected]
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
American English
One dialect, one language.
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
American sports writers
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
mit: MIT License
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Maintain a robust and scalable Data-to-Text generation resource with structured data and textual summaries
#### 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 -->
A model trained on this dataset should summarise the statistical and other information from a basketball game. This will be focused on a single game, although facts from prior games, or aggregate statistics over many games can and should be used for comparison where appropriate. There no single common narrative, although summaries usually start with who player, when, where, and the score. They then provide high level commentary on what the difference in the game was (why the winner won). breakdowns of statistics for prominent players follow, winning team first. Finally, the upcoming schedule for both teams is usually included. There are, however, other types of fact that can be included, and other narrative structures.
### 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 Aberdeen, Robert Gordon University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Craig Thomson, Ashish Upadhyay
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
EPSRC
#### 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 -->
Craig Thomson, Ashish Upadhyay
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
Each instance in the dataset has five fields.
1. "sportsett_id": This is a unique id as used in the original SportSett database. It starts with '1' with the first instance in the train-set and ends with '6150' with the last instance in test-set.
2. "gem_id": This is a unique id created as per GEM's requirement which follows the `GEM-${DATASET_NAME}-${SPLIT-NAME}-${id}` pattern.
3. "game": This field contains a dictionary with information about current game. It has information such as date on which the game was played alongwith the stadium, city, state where it was played.
4. "teams": This filed is a dictionary of multiple nested dictionaries. On the highest level, it has two keys: 'home' and 'vis', which provide the stats for home team and visiting team of the game. Both are dictionaries with same structure. Each dictionary will contain team's information such as name of the team, their total wins/losses in current season, their conference standing, the SportSett ids for their current and previous games. Apart from these general information, they also have the box- and line- scores for the team in the game. Box score is the stats of players from the team at the end of the game, while line score along with the whole game stats is divided into quarters and halves as well as the extra-time (if happened in the game). After these scores, there is another field of next-game, which gives general information about team's next game such as the place and opponent's name of the next game.
5. "summaries": This is a list of summaries for each game. Some games will have more than one summary, in that case, the list will have more than one entries. Each summary in the list is a string which can be tokenised by a space, following the practices in RotoWire-FG dataset ([Wang, 2019](https://www.aclweb.org/anthology/W19-8639)).
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The structure mostly follows the original structure defined in RotoWire dataset ([Wiseman et. al. 2017](https://aclanthology.org/D17-1239/)) with some modifications (such as game and next-game keys) address the problem of information gap between input and output data ([Thomson et. al. 2020](https://aclanthology.org/2020.inlg-1.6/)).
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
Similar to RotoWire dataset ([Wiseman et. al. 2017](https://aclanthology.org/D17-1239/))
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
"sportsett_id": "1",
"gem_id": "GEM-sportsett_basketball-train-0",
"game": {
"day": "1",
"month": "November",
"year": "2014",
"dayname": "Saturday",
"season": "2014",
"stadium": "Wells Fargo Center",
"city": "Philadelphia",
"state": "Pennsylvania",
"attendance": "19753",
"capacity": "20478",
"game_id": "1"
},
"teams": {
"home": {
"name": "76ers",
"place": "Philadelphia",
"conference": "Eastern Conference",
"division": "Atlantic",
"wins": "0",
"losses": "3",
"conference_standing": 15,
"game_number": "3",
"previous_game_id": "42",
"next_game_id": "2",
"line_score": {
"game": {
"FG3A": "23",
"FG3M": "7",
"FG3_PCT": "30",
"FGA": "67",
"FGM": "35",
"FG_PCT": "52",
"FTA": "26",
"FTM": "19",
"FT_PCT": "73",
"DREB": "33",
"OREB": "4",
"TREB": "37",
"BLK": "10",
"AST": "28",
"STL": "9",
"TOV": "24",
"PF": "21",
"PTS": "96",
"MIN": "4"
},
"H1": {
"FG3A": "82",
"FG3M": "30",
"FG3_PCT": "37",
"FGA": "2115",
"FGM": "138",
"FG_PCT": "7",
"FTA": "212",
"FTM": "18",
"FT_PCT": "8",
"DREB": "810",
"OREB": "21",
"TREB": "831",
"BLK": "51",
"AST": "107",
"STL": "21",
"TOV": "64",
"PTS": "3024",
"MIN": "6060"
},
"H2": {
"FG3A": "85",
"FG3M": "40",
"FG3_PCT": "47",
"FGA": "1615",
"FGM": "104",
"FG_PCT": "6",
"FTA": "66",
"FTM": "55",
"FT_PCT": "83",
"DREB": "96",
"OREB": "10",
"TREB": "106",
"BLK": "22",
"AST": "92",
"STL": "24",
"TOV": "68",
"PTS": "2913",
"MIN": "6060"
},
"Q1": {
"FG3A": "8",
"FG3M": "3",
"FG3_PCT": "38",
"FGA": "21",
"FGM": "13",
"FG_PCT": "62",
"FTA": "2",
"FTM": "1",
"FT_PCT": "50",
"DREB": "8",
"OREB": "2",
"TREB": "10",
"BLK": "5",
"AST": "10",
"STL": "2",
"TOV": "6",
"PTS": "30",
"MIN": "60"
},
"Q2": {
"FG3A": "2",
"FG3M": "0",
"FG3_PCT": "0",
"FGA": "15",
"FGM": "8",
"FG_PCT": "53",
"FTA": "12",
"FTM": "8",
"FT_PCT": "67",
"DREB": "10",
"OREB": "1",
"TREB": "11",
"BLK": "1",
"AST": "7",
"STL": "1",
"TOV": "4",
"PTS": "24",
"MIN": "60"
},
"Q3": {
"FG3A": "8",
"FG3M": "4",
"FG3_PCT": "50",
"FGA": "16",
"FGM": "10",
"FG_PCT": "62",
"FTA": "6",
"FTM": "5",
"FT_PCT": "83",
"DREB": "9",
"OREB": "1",
"TREB": "10",
"BLK": "2",
"AST": "9",
"STL": "2",
"TOV": "6",
"PTS": "29",
"MIN": "60"
},
"Q4": {
"FG3A": "5",
"FG3M": "0",
"FG3_PCT": "0",
"FGA": "15",
"FGM": "4",
"FG_PCT": "27",
"FTA": "6",
"FTM": "5",
"FT_PCT": "83",
"DREB": "6",
"OREB": "0",
"TREB": "6",
"BLK": "2",
"AST": "2",
"STL": "4",
"TOV": "8",
"PTS": "13",
"MIN": "60"
},
"OT": {
"FG3A": "0",
"FG3M": "0",
"FG3_PCT": "0",
"FGA": "0",
"FGM": "0",
"FG_PCT": "0",
"FTA": "0",
"FTM": "0",
"FT_PCT": "0",
"DREB": "0",
"OREB": "0",
"TREB": "0",
"BLK": "0",
"AST": "0",
"STL": "0",
"TOV": "0",
"PTS": "0",
"MIN": "0"
}
},
"box_score": [
{
"first_name": "Tony",
"last_name": "Wroten",
"name": "Tony Wroten",
"starter": "True",
"MIN": "33",
"FGM": "6",
"FGA": "11",
"FG_PCT": "55",
"FG3M": "1",
"FG3A": "4",
"FG3_PCT": "25",
"FTM": "8",
"FTA": "11",
"FT_PCT": "73",
"OREB": "0",
"DREB": "3",
"TREB": "3",
"AST": "10",
"STL": "1",
"BLK": "1",
"TOV": "4",
"PF": "1",
"PTS": "21",
"+/-": "-11",
"DOUBLE": "double"
},
{
"first_name": "Hollis",
"last_name": "Thompson",
"name": "Hollis Thompson",
"starter": "True",
"MIN": "32",
"FGM": "4",
"FGA": "8",
"FG_PCT": "50",
"FG3M": "2",
"FG3A": "5",
"FG3_PCT": "40",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "1",
"TREB": "1",
"AST": "2",
"STL": "0",
"BLK": "3",
"TOV": "2",
"PF": "2",
"PTS": "10",
"+/-": "-17",
"DOUBLE": "none"
},
{
"first_name": "Henry",
"last_name": "Sims",
"name": "Henry Sims",
"starter": "True",
"MIN": "27",
"FGM": "4",
"FGA": "9",
"FG_PCT": "44",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "1",
"FTA": "2",
"FT_PCT": "50",
"OREB": "1",
"DREB": "3",
"TREB": "4",
"AST": "2",
"STL": "0",
"BLK": "1",
"TOV": "0",
"PF": "1",
"PTS": "9",
"+/-": "-10",
"DOUBLE": "none"
},
{
"first_name": "Nerlens",
"last_name": "Noel",
"name": "Nerlens Noel",
"starter": "True",
"MIN": "25",
"FGM": "1",
"FGA": "4",
"FG_PCT": "25",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "5",
"TREB": "5",
"AST": "3",
"STL": "1",
"BLK": "1",
"TOV": "3",
"PF": "1",
"PTS": "2",
"+/-": "-19",
"DOUBLE": "none"
},
{
"first_name": "Luc",
"last_name": "Mbah a Moute",
"name": "Luc Mbah a Moute",
"starter": "True",
"MIN": "19",
"FGM": "4",
"FGA": "10",
"FG_PCT": "40",
"FG3M": "0",
"FG3A": "2",
"FG3_PCT": "0",
"FTM": "1",
"FTA": "2",
"FT_PCT": "50",
"OREB": "3",
"DREB": "4",
"TREB": "7",
"AST": "3",
"STL": "1",
"BLK": "0",
"TOV": "6",
"PF": "3",
"PTS": "9",
"+/-": "-12",
"DOUBLE": "none"
},
{
"first_name": "Brandon",
"last_name": "Davies",
"name": "Brandon Davies",
"starter": "False",
"MIN": "23",
"FGM": "7",
"FGA": "9",
"FG_PCT": "78",
"FG3M": "1",
"FG3A": "2",
"FG3_PCT": "50",
"FTM": "3",
"FTA": "4",
"FT_PCT": "75",
"OREB": "0",
"DREB": "3",
"TREB": "3",
"AST": "0",
"STL": "3",
"BLK": "0",
"TOV": "3",
"PF": "3",
"PTS": "18",
"+/-": "-1",
"DOUBLE": "none"
},
{
"first_name": "Chris",
"last_name": "Johnson",
"name": "Chris Johnson",
"starter": "False",
"MIN": "21",
"FGM": "2",
"FGA": "4",
"FG_PCT": "50",
"FG3M": "1",
"FG3A": "3",
"FG3_PCT": "33",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "2",
"TREB": "2",
"AST": "0",
"STL": "3",
"BLK": "0",
"TOV": "2",
"PF": "5",
"PTS": "5",
"+/-": "3",
"DOUBLE": "none"
},
{
"first_name": "K.J.",
"last_name": "McDaniels",
"name": "K.J. McDaniels",
"starter": "False",
"MIN": "20",
"FGM": "2",
"FGA": "4",
"FG_PCT": "50",
"FG3M": "1",
"FG3A": "3",
"FG3_PCT": "33",
"FTM": "3",
"FTA": "4",
"FT_PCT": "75",
"OREB": "0",
"DREB": "1",
"TREB": "1",
"AST": "2",
"STL": "0",
"BLK": "3",
"TOV": "2",
"PF": "3",
"PTS": "8",
"+/-": "-10",
"DOUBLE": "none"
},
{
"first_name": "Malcolm",
"last_name": "Thomas",
"name": "Malcolm Thomas",
"starter": "False",
"MIN": "19",
"FGM": "4",
"FGA": "4",
"FG_PCT": "100",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "9",
"TREB": "9",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "0",
"PF": "2",
"PTS": "8",
"+/-": "-6",
"DOUBLE": "none"
},
{
"first_name": "Alexey",
"last_name": "Shved",
"name": "Alexey Shved",
"starter": "False",
"MIN": "14",
"FGM": "1",
"FGA": "4",
"FG_PCT": "25",
"FG3M": "1",
"FG3A": "4",
"FG3_PCT": "25",
"FTM": "3",
"FTA": "3",
"FT_PCT": "100",
"OREB": "0",
"DREB": "1",
"TREB": "1",
"AST": "6",
"STL": "0",
"BLK": "0",
"TOV": "2",
"PF": "0",
"PTS": "6",
"+/-": "-7",
"DOUBLE": "none"
},
{
"first_name": "JaKarr",
"last_name": "Sampson",
"name": "JaKarr Sampson",
"starter": "False",
"MIN": "2",
"FGM": "0",
"FGA": "0",
"FG_PCT": "0",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "1",
"TREB": "1",
"AST": "0",
"STL": "0",
"BLK": "1",
"TOV": "0",
"PF": "0",
"PTS": "0",
"+/-": "0",
"DOUBLE": "none"
},
{
"first_name": "Michael",
"last_name": "Carter-Williams",
"name": "Michael Carter-Williams",
"starter": "False",
"MIN": "0",
"FGM": "0",
"FGA": "0",
"FG_PCT": "0",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "0",
"TREB": "0",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "0",
"PF": "0",
"PTS": "0",
"+/-": "0",
"DOUBLE": "none"
}
],
"next_game": {
"day": "3",
"month": "November",
"year": "2014",
"dayname": "Monday",
"stadium": "Wells Fargo Center",
"city": "Philadelphia",
"opponent_name": "Rockets",
"opponent_place": "Houston",
"is_home": "True"
}
},
"vis": {
"name": "Heat",
"place": "Miami",
"conference": "Eastern Conference",
"division": "Southeast",
"wins": "2",
"losses": "0",
"conference_standing": 1,
"game_number": "2",
"previous_game_id": "329",
"next_game_id": "330",
"line_score": {
"game": {
"FG3A": "24",
"FG3M": "12",
"FG3_PCT": "50",
"FGA": "83",
"FGM": "41",
"FG_PCT": "49",
"FTA": "29",
"FTM": "20",
"FT_PCT": "69",
"DREB": "26",
"OREB": "9",
"TREB": "35",
"BLK": "0",
"AST": "33",
"STL": "16",
"TOV": "16",
"PF": "20",
"PTS": "114",
"MIN": "4"
},
"H1": {
"FG3A": "69",
"FG3M": "44",
"FG3_PCT": "64",
"FGA": "2321",
"FGM": "1110",
"FG_PCT": "48",
"FTA": "106",
"FTM": "64",
"FT_PCT": "60",
"DREB": "35",
"OREB": "23",
"TREB": "58",
"BLK": "00",
"AST": "88",
"STL": "53",
"TOV": "34",
"PTS": "3228",
"MIN": "6060"
},
"H2": {
"FG3A": "45",
"FG3M": "22",
"FG3_PCT": "49",
"FGA": "1920",
"FGM": "1010",
"FG_PCT": "53",
"FTA": "85",
"FTM": "55",
"FT_PCT": "65",
"DREB": "612",
"OREB": "22",
"TREB": "634",
"BLK": "00",
"AST": "98",
"STL": "35",
"TOV": "36",
"PTS": "2727",
"MIN": "6060"
},
"Q1": {
"FG3A": "6",
"FG3M": "4",
"FG3_PCT": "67",
"FGA": "23",
"FGM": "11",
"FG_PCT": "48",
"FTA": "10",
"FTM": "6",
"FT_PCT": "60",
"DREB": "3",
"OREB": "2",
"TREB": "5",
"BLK": "0",
"AST": "8",
"STL": "5",
"TOV": "3",
"PTS": "32",
"MIN": "60"
},
"Q2": {
"FG3A": "9",
"FG3M": "4",
"FG3_PCT": "44",
"FGA": "21",
"FGM": "10",
"FG_PCT": "48",
"FTA": "6",
"FTM": "4",
"FT_PCT": "67",
"DREB": "5",
"OREB": "3",
"TREB": "8",
"BLK": "0",
"AST": "8",
"STL": "3",
"TOV": "4",
"PTS": "28",
"MIN": "60"
},
"Q3": {
"FG3A": "4",
"FG3M": "2",
"FG3_PCT": "50",
"FGA": "19",
"FGM": "10",
"FG_PCT": "53",
"FTA": "8",
"FTM": "5",
"FT_PCT": "62",
"DREB": "6",
"OREB": "2",
"TREB": "8",
"BLK": "0",
"AST": "9",
"STL": "3",
"TOV": "3",
"PTS": "27",
"MIN": "60"
},
"Q4": {
"FG3A": "5",
"FG3M": "2",
"FG3_PCT": "40",
"FGA": "20",
"FGM": "10",
"FG_PCT": "50",
"FTA": "5",
"FTM": "5",
"FT_PCT": "100",
"DREB": "12",
"OREB": "2",
"TREB": "14",
"BLK": "0",
"AST": "8",
"STL": "5",
"TOV": "6",
"PTS": "27",
"MIN": "60"
},
"OT": {
"FG3A": "0",
"FG3M": "0",
"FG3_PCT": "0",
"FGA": "0",
"FGM": "0",
"FG_PCT": "0",
"FTA": "0",
"FTM": "0",
"FT_PCT": "0",
"DREB": "0",
"OREB": "0",
"TREB": "0",
"BLK": "0",
"AST": "0",
"STL": "0",
"TOV": "0",
"PTS": "0",
"MIN": "0"
}
},
"box_score": [
{
"first_name": "Chris",
"last_name": "Bosh",
"name": "Chris Bosh",
"starter": "True",
"MIN": "33",
"FGM": "9",
"FGA": "17",
"FG_PCT": "53",
"FG3M": "2",
"FG3A": "5",
"FG3_PCT": "40",
"FTM": "10",
"FTA": "11",
"FT_PCT": "91",
"OREB": "3",
"DREB": "5",
"TREB": "8",
"AST": "4",
"STL": "2",
"BLK": "0",
"TOV": "3",
"PF": "2",
"PTS": "30",
"+/-": "10",
"DOUBLE": "none"
},
{
"first_name": "Dwyane",
"last_name": "Wade",
"name": "Dwyane Wade",
"starter": "True",
"MIN": "32",
"FGM": "4",
"FGA": "18",
"FG_PCT": "22",
"FG3M": "0",
"FG3A": "1",
"FG3_PCT": "0",
"FTM": "1",
"FTA": "3",
"FT_PCT": "33",
"OREB": "1",
"DREB": "2",
"TREB": "3",
"AST": "10",
"STL": "3",
"BLK": "0",
"TOV": "6",
"PF": "1",
"PTS": "9",
"+/-": "13",
"DOUBLE": "none"
},
{
"first_name": "Luol",
"last_name": "Deng",
"name": "Luol Deng",
"starter": "True",
"MIN": "29",
"FGM": "7",
"FGA": "11",
"FG_PCT": "64",
"FG3M": "1",
"FG3A": "3",
"FG3_PCT": "33",
"FTM": "0",
"FTA": "1",
"FT_PCT": "0",
"OREB": "2",
"DREB": "2",
"TREB": "4",
"AST": "2",
"STL": "2",
"BLK": "0",
"TOV": "1",
"PF": "0",
"PTS": "15",
"+/-": "4",
"DOUBLE": "none"
},
{
"first_name": "Shawne",
"last_name": "Williams",
"name": "Shawne Williams",
"starter": "True",
"MIN": "29",
"FGM": "5",
"FGA": "9",
"FG_PCT": "56",
"FG3M": "3",
"FG3A": "5",
"FG3_PCT": "60",
"FTM": "2",
"FTA": "2",
"FT_PCT": "100",
"OREB": "0",
"DREB": "4",
"TREB": "4",
"AST": "4",
"STL": "1",
"BLK": "0",
"TOV": "1",
"PF": "4",
"PTS": "15",
"+/-": "16",
"DOUBLE": "none"
},
{
"first_name": "Norris",
"last_name": "Cole",
"name": "Norris Cole",
"starter": "True",
"MIN": "27",
"FGM": "4",
"FGA": "7",
"FG_PCT": "57",
"FG3M": "2",
"FG3A": "4",
"FG3_PCT": "50",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "1",
"TREB": "1",
"AST": "4",
"STL": "2",
"BLK": "0",
"TOV": "0",
"PF": "1",
"PTS": "10",
"+/-": "6",
"DOUBLE": "none"
},
{
"first_name": "Mario",
"last_name": "Chalmers",
"name": "Mario Chalmers",
"starter": "False",
"MIN": "25",
"FGM": "6",
"FGA": "9",
"FG_PCT": "67",
"FG3M": "2",
"FG3A": "2",
"FG3_PCT": "100",
"FTM": "6",
"FTA": "10",
"FT_PCT": "60",
"OREB": "0",
"DREB": "2",
"TREB": "2",
"AST": "4",
"STL": "4",
"BLK": "0",
"TOV": "0",
"PF": "1",
"PTS": "20",
"+/-": "18",
"DOUBLE": "none"
},
{
"first_name": "Shabazz",
"last_name": "Napier",
"name": "Shabazz Napier",
"starter": "False",
"MIN": "20",
"FGM": "2",
"FGA": "3",
"FG_PCT": "67",
"FG3M": "1",
"FG3A": "2",
"FG3_PCT": "50",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "3",
"TREB": "3",
"AST": "4",
"STL": "2",
"BLK": "0",
"TOV": "1",
"PF": "4",
"PTS": "5",
"+/-": "11",
"DOUBLE": "none"
},
{
"first_name": "Chris",
"last_name": "Andersen",
"name": "Chris Andersen",
"starter": "False",
"MIN": "17",
"FGM": "0",
"FGA": "2",
"FG_PCT": "0",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "1",
"DREB": "2",
"TREB": "3",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "0",
"PF": "2",
"PTS": "0",
"+/-": "6",
"DOUBLE": "none"
},
{
"first_name": "Josh",
"last_name": "McRoberts",
"name": "Josh McRoberts",
"starter": "False",
"MIN": "11",
"FGM": "1",
"FGA": "3",
"FG_PCT": "33",
"FG3M": "0",
"FG3A": "1",
"FG3_PCT": "0",
"FTM": "1",
"FTA": "2",
"FT_PCT": "50",
"OREB": "0",
"DREB": "3",
"TREB": "3",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "2",
"PF": "3",
"PTS": "3",
"+/-": "1",
"DOUBLE": "none"
},
{
"first_name": "James",
"last_name": "Ennis",
"name": "James Ennis",
"starter": "False",
"MIN": "7",
"FGM": "2",
"FGA": "3",
"FG_PCT": "67",
"FG3M": "1",
"FG3A": "1",
"FG3_PCT": "100",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "1",
"DREB": "1",
"TREB": "2",
"AST": "1",
"STL": "0",
"BLK": "0",
"TOV": "0",
"PF": "1",
"PTS": "5",
"+/-": "2",
"DOUBLE": "none"
},
{
"first_name": "Justin",
"last_name": "Hamilton",
"name": "Justin Hamilton",
"starter": "False",
"MIN": "5",
"FGM": "1",
"FGA": "1",
"FG_PCT": "100",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "1",
"DREB": "1",
"TREB": "2",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "1",
"PF": "0",
"PTS": "2",
"+/-": "3",
"DOUBLE": "none"
},
{
"first_name": "Andre",
"last_name": "Dawkins",
"name": "Andre Dawkins",
"starter": "False",
"MIN": "1",
"FGM": "0",
"FGA": "0",
"FG_PCT": "0",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "0",
"TREB": "0",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "1",
"PF": "1",
"PTS": "0",
"+/-": "0",
"DOUBLE": "none"
},
{
"first_name": "Shannon",
"last_name": "Brown",
"name": "Shannon Brown",
"starter": "False",
"MIN": "0",
"FGM": "0",
"FGA": "0",
"FG_PCT": "0",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "0",
"TREB": "0",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "0",
"PF": "0",
"PTS": "0",
"+/-": "0",
"DOUBLE": "none"
}
],
"next_game": {
"day": "2",
"month": "November",
"year": "2014",
"dayname": "Sunday",
"stadium": "American Airlines Arena",
"city": "Miami",
"opponent_name": "Raptors",
"opponent_place": "Toronto",
"is_home": "True"
}
}
},
"summaries": [
"The Miami Heat ( 20 ) defeated the Philadelphia 76ers ( 0 - 3 ) 114 - 96 on Saturday . Chris Bosh scored a game - high 30 points to go with eight rebounds in 33 minutes . Josh McRoberts made his Heat debut after missing the entire preseason recovering from toe surgery . McRoberts came off the bench and played 11 minutes . Shawne Williams was once again the starter at power forward in McRoberts ' stead . Williams finished with 15 points and three three - pointers in 29 minutes . Mario Chalmers scored 18 points in 25 minutes off the bench . Luc Richard Mbah a Moute replaced Chris Johnson in the starting lineup for the Sixers on Saturday . Hollis Thompson shifted down to the starting shooting guard job to make room for Mbah a Moute . Mbah a Moute finished with nine points and seven rebounds in 19 minutes . K.J . McDaniels , who suffered a minor hip flexor injury in Friday 's game , was available and played 21 minutes off the bench , finishing with eight points and three blocks . Michael Carter-Williams is expected to be out until Nov. 13 , but Tony Wroten continues to put up impressive numbers in Carter-Williams ' absence . Wroten finished with a double - double of 21 points and 10 assists in 33 minutes . The Heat will complete a back - to - back set at home Sunday against the Tornoto Raptors . The Sixers ' next game is at home Monday against the Houston Rockets ."
]
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- Train: NBA seasons - 2014, 2015, & 2016; total instances - 3690
- Validation: NBA seasons - 2017; total instances - 1230
- Test: NBA seasons - 2018; total instances - 1230
#### 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 splits were created as per different NBA seasons. All the games in regular season (no play-offs) are added in the dataset
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
This dataset contains a data analytics problem in the classic sense ([Reiter, 2007](https://aclanthology.org/W07-2315)). That is, there is a large amount of data from which insights need to be selected. Further, the insights should be both from simple shallow queries (such as dirext transcriptions of the properties of a subject, i.e., a player and their statistics), as well as aggregated (how a player has done over time). There is far more on the data side than is required to be realised, and indeed, could be practically realised. This depth of data analytics problem does not exist in other datasets.
#### 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 -->
Many, if not all aspects of data-to-text systems can be measured with this dataset. It has complex data analytics, meaninful document planning (10-15 sentence documents with a narrative structure), as well as microplanning and realisation requirements. Finding models to handle this volume of data, as well as methods for meaninfully evaluate generations is a very open question.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
For dataset discussion see [Thomson et al, 2020](https://aclanthology.org/2020.intellang-1.4/)
For evaluation see:
- [Thomson & Reiter 2020, Thomson & Reiter (2021)](https://aclanthology.org/2021.inlg-1.23)
- [Kasner et al (2021)](https://aclanthology.org/2021.inlg-1.25)
For a system using the relational database form of SportSett, see:
- [Thomson et al (2020)](https://aclanthology.org/2020.inlg-1.6/)
For recent systems using the Rotowire dataset, see:
- [Puduppully & Lapata (2021)](https://github.com/ratishsp/data2text-macro-plan-py)
- [Rebuffel et all (2020)](https://github.com/KaijuML/data-to-text-hierarchical)
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Many, if not all aspects of data-to-text systems can be measured with this dataset. It has complex data analytics, meaninful document planning (10-15 sentence documents with a narrative structure), as well as microplanning and realisation requirements. Finding models to handle this volume of data, as well as methods for meaninfully evaluate generations is a very open question.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
BLEU is the only off-the-shelf metric commonly used. Works have also used custom metrics like RG ([Wiseman et al, 2017](https://aclanthology.org/D17-1239)), and a recent shared task explored other metrics and their corrolation with human evaluation ([Thomson & Reiter, 2021](https://aclanthology.org/2021.inlg-1.23)).
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
Most results from prior works use the original Rotowire dataset, which has train/validation/test contamination. For results of BLEU and RG on the relational database format of SportSett, as a guide, see [Thomson et al, 2020](https://aclanthology.org/2020.inlg-1.6).
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
The results on this dataset are largely unexplored, as is the selection of suitable metrics that correlate with human judgment. See Thomson et al, 2021 (https://aclanthology.org/2021.inlg-1.23) for an overview, and Kasner et al (2021) for the best performing metric at the time of writing (https://aclanthology.org/2021.inlg-1.25).
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The references texts were taken from the existing dataset RotoWire-FG ([Wang, 2019](https://www.aclweb.org/anthology/W19-8639)), which is in turn based on Rotowire ([Wiseman et al, 2017](https://aclanthology.org/D17-1239)). The rationale behind this dataset was to re-structure the data such that aggregate statistics over multiple games, as well as upcoming game schedules could be included, moving the dataset from snapshots of single games, to a format where almost everything that could be present in the reference texts could be found in the data.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Create a summary of a basketball, with insightful facts about the game, teams, and players, both within the game, withing periods during the game, and over the course of seasons/careers where appropriate. This is a data-to-text problem in the classic sense ([Reiter, 2007](https://aclanthology.org/W07-2315)) in that it has a difficult data analystics state, in addition to ordering and transcription of selected facts.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
RotoWire-FG (https://www.rotowire.com).
Wikipedia (https://en.wikipedia.org/wiki/Main_Page)
Basketball Reference (https://www.basketball-reference.com)
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Multiple websites`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
None
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
Summaries of basketball games (in the NBA).
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
It retains the original tokenization scheme employed by Wang 2019
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
manually
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
Games from the 2014 through 2018 seasons were selected. Within these seasons games are not filtered, all are present, but this was an arbitrary solution from the original RotoWirte-FG dataset.
### 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 -->
The dataset consits of a pre-existing dataset, as well as publically available facts.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
yes
#### Links and Summaries of Analysis Work
<!-- info: Provide links to and summaries of works analyzing these biases. -->
<!-- scope: microscope -->
Unaware of any work, but, this is a dataset considting solely of summaries of mens professional basketball games. It does not cover different levels of the sport, or different genders, and all pronouns are likely to be male unless a specific player is referred to by other pronouns in the training text. This makes it difficult to train systems where gender can be specified as an attribute, although it is an interesting, open problem that could be investigated using the dataset.
#### 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 -->
No, it is very specifically American English from the sports journalism domain.
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
All information relating to persons is of public record.
### 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 -->
`public domain`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
SportSett resolved the major overlap problems of RotoWire, although some overlap is unavoidable. For example, whilst it is not possible to find career totals and other historic information for all players (the data only goes back to 2014), it is possible to do so for some players. It is unavoidable that some data which is aggregated, exists in its base form in previous partitions. The season-based partition scheme heavily constrains this however.
#### 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 -->
Factual accuray continues to be a problem, systems may incorrectly represent the facts of the game.
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
Using the RG metric to maximise the number of true facts in a generate summary is not nececeraly
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|
GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1645559101 | GEM-submissions | 2022-02-22T19:45:04Z | 287 | 0 | [
"benchmark:gem",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
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|
GEM-submissions/lewtun__this-is-a-test__1646230987 | GEM-submissions | 2022-03-02T14:23:10Z | 287 | 0 | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
benchmark: gem
type: prediction
submission_name: This is a test
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test
| [
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|
Hello-SimpleAI/HC3-Chinese | Hello-SimpleAI | 2023-01-21T13:11:49Z | 287 | 106 | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:zero-shot-classification",
"size_categories:10K<n<100K",
"language:en",
"language:zh",
"license:cc-by-sa-4.0",
"ChatGPT",
"SimpleAI",
"Detection",
"OOD",
"arxiv:2301.07597",
"region:us"
]
| [
"text-classification",
"question-answering",
"sentence-similarity",
"zero-shot-classification"
]
| 2023-01-18T14:20:45Z | ---
task_categories:
- text-classification
- question-answering
- sentence-similarity
- zero-shot-classification
language:
- en
- zh
tags:
- ChatGPT
- SimpleAI
- Detection
- OOD
size_categories:
- 10K<n<100K
license: cc-by-sa-4.0
---
# Human ChatGPT Comparison Corpus (HC3)
We propose the first human-ChatGPT comparison corpus, named **HC3** dataset.
This dataset is introduced in our paper:
- Paper: [***How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection***](https://arxiv.org/abs/2301.07597)
Code, models and analysis are available on our GitHub:
- GitHub: [**Chatgpt-Comparison-Detection project** 🔬](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection)
# Dataset Copyright
If the source datasets used in this corpus has a specific license which is stricter than CC-BY-SA, our products follow the same. If not, they follow CC-BY-SA license.
See [dataset copyright](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection#dataset-copyright).
# Citation
Checkout this papaer [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597)
```
@article{guo-etal-2023-hc3,
title = "How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection",
author = "Guo, Biyang and
Zhang, Xin and
Wang, Ziyuan and
Jiang, Minqi and
Nie, Jinran and
Ding, Yuxuan and
Yue, Jianwei and
Wu, Yupeng",
journal={arXiv preprint arxiv:2301.07597}
year = "2023",
}
``` | [
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|
mattymchen/refinedweb-3m | mattymchen | 2023-06-12T06:01:04Z | 287 | 4 | [
"region:us"
]
| null | 2023-06-12T05:58:49Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 7834920949
num_examples: 3000000
download_size: 4904877808
dataset_size: 7834920949
---
# Dataset Card for "refinedweb-3m"
[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_elyza__ELYZA-japanese-Llama-2-7b-instruct | open-llm-leaderboard | 2023-09-22T19:09:17Z | 287 | 0 | [
"region:us"
]
| null | 2023-08-31T10:23:18Z | ---
pretty_name: Evaluation run of elyza/ELYZA-japanese-Llama-2-7b-instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [elyza/ELYZA-japanese-Llama-2-7b-instruct](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b-instruct)\
\ 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_elyza__ELYZA-japanese-Llama-2-7b-instruct\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T19:09:05.444752](https://huggingface.co/datasets/open-llm-leaderboard/details_elyza__ELYZA-japanese-Llama-2-7b-instruct/blob/main/results_2023-09-22T19-09-05.444752.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0007340604026845638,\n\
\ \"em_stderr\": 0.0002773614457335574,\n \"f1\": 0.05706166107382565,\n\
\ \"f1_stderr\": 0.0014151120658963989,\n \"acc\": 0.40564322185674373,\n\
\ \"acc_stderr\": 0.00993255448838312\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0007340604026845638,\n \"em_stderr\": 0.0002773614457335574,\n\
\ \"f1\": 0.05706166107382565,\n \"f1_stderr\": 0.0014151120658963989\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07884761182714177,\n \
\ \"acc_stderr\": 0.007423390519873231\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7324388318863457,\n \"acc_stderr\": 0.012441718456893009\n\
\ }\n}\n```"
repo_url: https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b-instruct
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_31T10_22_54.863946
path:
- '**/details_harness|arc:challenge|25_2023-08-31T10:22:54.863946.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T19_09_05.444752
path:
- '**/details_harness|drop|3_2023-09-22T19-09-05.444752.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T19-09-05.444752.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T19_09_05.444752
path:
- '**/details_harness|gsm8k|5_2023-09-22T19-09-05.444752.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T19-09-05.444752.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_31T10_22_54.863946
path:
- '**/details_harness|hellaswag|10_2023-08-31T10:22:54.863946.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_31T10_22_54.863946
path:
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- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T10:22:54.863946.parquet'
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path:
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T10:22:54.863946.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_anatomy_5
data_files:
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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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- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T10:22:54.863946.parquet'
- 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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- split: latest
path:
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- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T10:22:54.863946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T10:22:54.863946.parquet'
- 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_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T10:22:54.863946.parquet'
- 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_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_31T10_22_54.863946
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
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- split: latest
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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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_logical_fallacies_5
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:
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path:
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path:
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- config_name: harness_hendrycksTest_management_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
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- split: latest
path:
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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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- split: latest
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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- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T10:22:54.863946.parquet'
- 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_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_31T10_22_54.863946
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T10:22:54.863946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_31T10_22_54.863946
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T10:22:54.863946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_31T10_22_54.863946
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T10:22:54.863946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_31T10_22_54.863946
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T10:22:54.863946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_31T10_22_54.863946
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T10:22:54.863946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_31T10_22_54.863946
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T10:22:54.863946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_31T10_22_54.863946
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T10:22:54.863946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_31T10_22_54.863946
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T10:22:54.863946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_31T10_22_54.863946
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-31T10:22:54.863946.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-31T10:22:54.863946.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T19_09_05.444752
path:
- '**/details_harness|winogrande|5_2023-09-22T19-09-05.444752.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T19-09-05.444752.parquet'
- config_name: results
data_files:
- split: 2023_08_31T10_22_54.863946
path:
- results_2023-08-31T10:22:54.863946.parquet
- split: 2023_09_22T19_09_05.444752
path:
- results_2023-09-22T19-09-05.444752.parquet
- split: latest
path:
- results_2023-09-22T19-09-05.444752.parquet
---
# Dataset Card for Evaluation run of elyza/ELYZA-japanese-Llama-2-7b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b-instruct
- **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 [elyza/ELYZA-japanese-Llama-2-7b-instruct](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b-instruct) 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_elyza__ELYZA-japanese-Llama-2-7b-instruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T19:09:05.444752](https://huggingface.co/datasets/open-llm-leaderboard/details_elyza__ELYZA-japanese-Llama-2-7b-instruct/blob/main/results_2023-09-22T19-09-05.444752.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0007340604026845638,
"em_stderr": 0.0002773614457335574,
"f1": 0.05706166107382565,
"f1_stderr": 0.0014151120658963989,
"acc": 0.40564322185674373,
"acc_stderr": 0.00993255448838312
},
"harness|drop|3": {
"em": 0.0007340604026845638,
"em_stderr": 0.0002773614457335574,
"f1": 0.05706166107382565,
"f1_stderr": 0.0014151120658963989
},
"harness|gsm8k|5": {
"acc": 0.07884761182714177,
"acc_stderr": 0.007423390519873231
},
"harness|winogrande|5": {
"acc": 0.7324388318863457,
"acc_stderr": 0.012441718456893009
}
}
```
### 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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|
usvsnsp/pile-semantic-memorization-filter-results | usvsnsp | 2023-09-19T18:56:42Z | 287 | 1 | [
"region:us"
]
| null | 2023-09-19T18:16:50Z | ---
dataset_info:
features:
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dtype: int64
- name: text
dtype: string
- name: sequence_duplicates
dtype: int64
- name: max_frequency
dtype: int64
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- name: p75_frequency
dtype: int64
- name: frequencies
sequence: int64
- name: is_incrementing
dtype: bool
- name: tokens
sequence: int64
- name: repeating_offset
dtype: int32
- name: num_repeating
dtype: int32
- name: smallest_repeating_chunk
sequence: int64
- name: memorization_score
dtype: float64
- name: templating_frequency_0.9
dtype: int64
- name: templating_frequency_0.8
dtype: int64
- name: prompt_perplexity
dtype: float32
- name: generation_perplexity
dtype: float32
- name: sequence_perplexity
dtype: float32
splits:
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- name: pile.duped.12b
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num_bytes: 7013409756
num_examples: 5000000
- name: pile.deduped.12b
num_bytes: 7013409756
num_examples: 5000000
download_size: 48107269588
dataset_size: 112134065488
---
# Dataset Card for "pile-semantic-memorization-filter-results"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
sinandraide/hotpot_qa_spread | sinandraide | 2023-11-11T17:11:06Z | 287 | 0 | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"arxiv:1809.09600",
"region:us"
]
| [
"question-answering"
]
| 2023-10-30T18:13:11Z | ---
task_categories:
- question-answering
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
This dataset is a spread version of the HotpotQA dataset. This version allows it to be compatible with Langchain's HuggingfaceLoader.
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
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[More Information Needed] | [
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|
menyo20k_mt | null | 2022-12-30T19:38:49Z | 286 | 1 | [
"task_categories:translation",
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:yo",
"license:cc-by-nc-4.0",
"arxiv:2103.08647",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
- found
language_creators:
- found
language:
- en
- yo
license:
- cc-by-nc-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: menyo-20k
pretty_name: MENYO-20k
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- en
- yo
config_name: menyo20k_mt
splits:
- name: train
num_bytes: 2551345
num_examples: 10070
- name: validation
num_bytes: 870011
num_examples: 3397
- name: test
num_bytes: 1905432
num_examples: 6633
download_size: 5206234
dataset_size: 5326788
---
# Dataset Card for MENYO-20k
## 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:** https://github.com/uds-lsv/menyo-20k_MT/
- **Paper:** [The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation](https://arxiv.org/abs/2103.08647)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences, and 6,633 test sentences (3,419 multi-domain, 1,714 news domain, and 1,500 ted talks speech transcript domain).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Languages are English and Yoruba.
## Dataset Structure
### Data Instances
An instance example:
```
{'translation':
{'en': 'Unit 1: What is Creative Commons?',
'yo': 'Ìdá 1: Kín ni Creative Commons?'
}
}
```
### Data Fields
- `translation`:
- `en`: English sentence.
- `yo`: Yoruba sentence.
### Data Splits
Training, validation and test splits are available.
## 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
The dataset is open but for non-commercial use because some data sources like Ted talks and JW news require permission for commercial use.
The dataset is licensed under Creative Commons [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) License: https://github.com/uds-lsv/menyo-20k_MT/blob/master/LICENSE
### Citation Information
If you use this dataset, please cite this paper:
```
@inproceedings{adelani-etal-2021-effect,
title = "The Effect of Domain and Diacritics in {Y}oruba{--}{E}nglish Neural Machine Translation",
author = "Adelani, David and
Ruiter, Dana and
Alabi, Jesujoba and
Adebonojo, Damilola and
Ayeni, Adesina and
Adeyemi, Mofe and
Awokoya, Ayodele Esther and
Espa{\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track)",
month = aug,
year = "2021",
address = "Virtual",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2021.mtsummit-research.6",
pages = "61--75",
abstract = "Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs. However and these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difficulty of evaluating MT models on low-resource pairs is often due to lack of standardized evaluation datasets. In this paper and we present MENYO-20k and the first multi-domain parallel corpus with a especially curated orthography for Yoruba{--}English with standardized train-test splits for benchmarking. We provide several neural MT benchmarks and compare them to the performance of popular pre-trained (massively multilingual) MT models both for the heterogeneous test set and its subdomains. Since these pre-trained models use huge amounts of data with uncertain quality and we also analyze the effect of diacritics and a major characteristic of Yoruba and in the training data. We investigate how and when this training condition affects the final quality of a translation and its understandability.Our models outperform massively multilingual models such as Google ($+8.7$ BLEU) and Facebook M2M ($+9.1$) when translating to Yoruba and setting a high quality benchmark for future research.",
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
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|
newsph | null | 2022-11-03T16:07:51Z | 286 | 2 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:fil",
"language:tl",
"license:gpl-3.0",
"arxiv:2010.11574",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- fil
- tl
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: newsph-nli
pretty_name: NewsPH-NLI
dataset_info:
features:
- name: text
dtype: string
config_name: newsph
splits:
- name: train
num_bytes: 298833914
num_examples: 2190465
download_size: 104086466
dataset_size: 298833914
---
# Dataset Card for NewsPH
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Filipino Text Benchmarks](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Repository:**
- **Paper:** [Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation](https://arxiv.org/abs/2010.11574)
- **Leaderboard:**
- **Point of Contact:** [Jan Christian Blaise Cruz]([email protected])
### Dataset Summary
Raw collection of news articles in Filipino. Used to produce the NewsPH-NLI dataset in Cruz et al. (2020)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Tagalog/Filipino
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- `text` (`str`)
The dataset is in plaintext and only has one field ("text"). It can be used for language modeling.
### 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 [@jcblaisecruz02](https://github.com/jcblaisecruz02) for adding this dataset. | [
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|
etalab-ia/piaf | etalab-ia | 2022-11-03T16:31:15Z | 286 | 8 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:mit",
"region:us"
]
| [
"question-answering"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- fr
language_bcp47:
- fr-FR
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null
pretty_name: Piaf
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: 3332905
num_examples: 3835
download_size: 1370384
dataset_size: 3332905
---
# Dataset Card for Piaf
## 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://piaf.etalab.studio](https://piaf.etalab.studio)
- **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:** 1.31 MB
- **Size of the generated dataset:** 3.18 MB
- **Total amount of disk used:** 4.49 MB
### Dataset Summary
Piaf is a reading comprehension dataset. This version, published in February 2020, contains 3835 questions on French Wikipedia.
### 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:** 1.31 MB
- **Size of the generated dataset:** 3.18 MB
- **Total amount of disk used:** 4.49 MB
An example of 'train' looks as follows.
```
{
"answers": {
"answer_start": [0],
"text": ["Voici"]
},
"context": "Voici le contexte du premier paragraphe du deuxième article.",
"id": "p140295460356960",
"question": "Suis-je la troisième question ?",
"title": "Jakob Böhme"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `string` feature.
- `title`: 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 |
|------------|------:|
| plain_text | 3835 |
## 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{keraron-EtAl:2020:LREC,
author = {Keraron, Rachel and Lancrenon, Guillaume and Bras, Mathilde and Allary, Frédéric and Moyse, Gilles and Scialom, Thomas and Soriano-Morales, Edmundo-Pavel and Staiano, Jacopo},
title = {Project PIAF: Building a Native French Question-Answering Dataset},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
month = {May},
year = {2020},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {5483--5492},
abstract = {Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.},
url = {https://www.aclweb.org/anthology/2020.lrec-1.673}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@RachelKer](https://github.com/RachelKer) for adding this dataset. | [
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|
spanish_billion_words | null | 2022-11-03T16:16:07Z | 286 | 9 | [
"task_categories:other",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:es",
"license:cc-by-sa-4.0",
"region:us"
]
| [
"other",
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- es
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: sbwce
pretty_name: Spanish Billion Word Corpus and Embeddings
dataset_info:
features:
- name: text
dtype: string
config_name: corpus
splits:
- name: train
num_bytes: 8950895954
num_examples: 46925295
download_size: 2024166993
dataset_size: 8950895954
---
# Dataset Card for Spanish Billion Words
## 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:** [Spanish Billion Words homepage](https://crscardellino.github.io/SBWCE/)
- **Point of Contact:** [Cristian Cardellino](mailto:[email protected]) (Corpus Creator), [María Grandury](mailto:[email protected]) (Corpus Submitter)
### Dataset Summary
The Spanish Billion Words Corpus is an unannotated Spanish corpus of nearly 1.5 billion words, compiled from different resources from the web.
This resources include the spanish portions of SenSem, the Ancora Corpus, some OPUS Project Corpora and the Europarl,
the Tibidabo Treebank, the IULA Spanish LSP Treebank, and dumps from the Spanish Wikipedia, Wikisource and Wikibooks.
This corpus is a compilation of 100 text files. Each line of these files represents one of the 50 million sentences from the corpus.
### Supported Tasks and Leaderboards
This dataset can be used for language modelling and for pretraining language models.
### Languages
The text in this dataset is in Spanish, BCP-47 code: 'es'.
## Dataset Structure
### Data Instances
Each example in this dataset is a sentence in Spanish:
```
{'text': 'Yo me coloqué en un asiento próximo a una ventana cogí un libro de una mesa y empecé a leer'}
```
### Data Fields
- `text`: a sentence in Spanish
### Data Splits
The dataset is not split.
## Dataset Creation
### Curation Rationale
The Spanish Billion Words Corpus was created to train word embeddings using the word2vect algorithm provided by the gensim package.
### Source Data
#### Initial Data Collection and Normalization
The corpus was created compiling the following resources:
- The Spanish portion of [SenSem]().
- The Spanish portion of the [Ancora Corpus](http://clic.ub.edu/corpus/en).
- [Tibidabo Treebank and IULA Spanish LSP Treebank](http://lod.iula.upf.edu/resources/metadata_TRL_Tibidabo_LSP_treebank_ES).
- The Spanish portion of the following [OPUS Project](http://opus.nlpl.eu/index.php) Corpora:
- The [books](http://opus.nlpl.eu/Books.php) aligned by [Andras Farkas](https://farkastranslations.com/).
- The [JRC-Acquis](http://opus.nlpl.eu/JRC-Acquis.php) collection of legislative text of the European Union.
- The [News Commentary](http://opus.nlpl.eu/News-Commentary.php) corpus.
- The [United Nations](http://opus.nlpl.eu/UN.php) documents compiled by [Alexandre Rafalovitch](https://www.outerthoughts.com/) and [Robert Dale](http://web.science.mq.edu.au/~rdale/).
- The Spanish portion of the [Europarl](http://statmt.org/europarl/) (European Parliament), compiled by [Philipp Koehn](https://homepages.inf.ed.ac.uk/pkoehn/).
- Dumps from the Spanish [Wikipedia](https://es.wikipedia.org/wiki/Wikipedia:Portada), [Wikisource](https://es.wikisource.org/wiki/Portada) and [Wikibooks](https://es.wikibooks.org/wiki/Portada) on date 2015-09-01, parsed with the Wikipedia Extractor.
All the annotated corpora (like Ancora, SenSem and Tibidabo) were untagged and
the parallel corpora (most coming from the OPUS Project) was preprocessed to obtain only the Spanish portions of it.
Once the whole corpus was unannotated, all non-alphanumeric characters were replaced with whitespaces,
all numbers with the token “DIGITO” and all the multiple whitespaces with only one whitespace.
The capitalization of the words remained unchanged.
#### Who are the source language producers?
The data was compiled and processed by Cristian Cardellino.
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The data was collected and processed by Cristian Cardellino.
### Licensing Information
The dataset is licensed under a Creative Commons Attribution-ShareAlike 4.0 International license
[(CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@misc{cardellinoSBWCE,
author = {Cardellino, Cristian},
title = {Spanish {B}illion {W}ords {C}orpus and {E}mbeddings},
url = {https://crscardellino.github.io/SBWCE/},
month = {August},
year = {2019}
}
```
### Contributions
Thanks to [@mariagrandury](https://github.com/mariagrandury) for adding this dataset. | [
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|
Nexdata/chinese_dialect | Nexdata | 2023-11-22T09:49:16Z | 286 | 5 | [
"task_categories:automatic-speech-recognition",
"language:zh",
"region:us"
]
| [
"automatic-speech-recognition"
]
| 2022-03-02T23:29:22Z | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
task_categories:
- automatic-speech-recognition
language:
- zh
---
# Dataset Card for chinese_dialect
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://nexdata.ai/?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset contains 25,000 hours of Chinese Dialect speech data. It's collected from local dialect speakers in multiple dialect regions, covering Hokkien, Cantonese, Sichuan Dialect, Henan Dialects,Northeastern Dialect, Shanghai Dialect,Uyghur and Tibetan etc. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%.
For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Chinese Dialect
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commercial License
### Citation Information
[More Information Needed]
### Contributions | [
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|
distil-whisper/gigaspeech-l | distil-whisper | 2023-09-25T10:28:52Z | 286 | 0 | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:other",
"region:us"
]
| [
"automatic-speech-recognition"
]
| 2023-04-11T20:16:14Z | ---
license: other
task_categories:
- automatic-speech-recognition
language:
- en
extra_gated_prompt: |-
SpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for non-commercial research and/or educational purposes, we can provide access through the Hub under certain conditions and terms.
Terms of Access:
The "Researcher" has requested permission to use the GigaSpeech database (the "Database") at Tsinghua University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
5. The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
Please also fill out the Google Form https://forms.gle/UuGQAPyscGRrUMLq6 to request access to the GigaSpeech dataset.
extra_gated_fields:
Name: text
Email: text
Organization: text
Address: text
I hereby confirm that I have requested access via the Google Form provided above: checkbox
I accept the terms of access: checkbox
---
# Distil Whisper: GigaSpeech
This is a variant of the [GigaSpeech](https://huggingface.co/datasets/speechcolab/gigaspeech) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/speechcolab/gigaspeech).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/gigaspeech-l", "l")
# take the first sample of the validation set
sample = dataset["validation"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/gigaspeech-l", "l", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under custom terms. To view the custom license for this dataset, refer to the original [dataset card](https://huggingface.co/datasets/speechcolab/gigaspeech).
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|
ArtifactAI/arxiv-math-instruct-50k | ArtifactAI | 2023-06-22T03:12:01Z | 286 | 36 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"doi:10.57967/hf/0799",
"region:us"
]
| [
"text-generation"
]
| 2023-06-21T03:26:49Z | ---
annotations_creators:
- no-annotation
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: arxiv-math-instruct-50k
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: arxiv-math-instruct-50k
---
# Dataset Card for "arxiv-math-instruct-50k"
### Dataset Summary
The "ArtifactAI/arxiv-math-instruct-50k" dataset consists of question-answer pairs derived from ArXiv abstracts from the following categories:
"math.AC", "math.AG", "math.AP", "math.AT", "math.CA", "math.CO", "math.CT", "math.CV", "math.DG", "math.DS", "math.FA", "math.GM", "math.GN", "math.GR", "math.GT", "math.HO",
"math.IT", "math.KT", "math.LO", "math.MG", "math.MP", "math.NA", "math.NT", "math.OA", "math.OC", "math.PR", "math.QA", "math.RA", "math.RT", "math.SG", "math.SP", "math.ST", "math-ph".
Questions are generated using the [t5-base model](https://huggingface.co/t5-base), while the answers are generated using the [GPT-3.5-turbo model](https://openai.com/chatgpt).
### Languages
English
## Dataset Structure
### Data Instances
#### train
- **Size of downloaded dataset files:** 38.4 MB
An example of 'train' looks as follows.
{
"question": "Which math term describes the behaviour of an elliptic curve?",
"answer": "The term that describes the behavior of an elliptic curve is its "rank".
The rank of an elliptic curve is a measure of the number of rational points on the curve.
It is an important concept in number theory and cryptography, as the security of certain cryptographic algorithms based
on elliptic curves depends on the rank of the curve."
}
### Data Fields
The data fields present in the dataset are as follows:
- question: a string feature representing the question.
- answer: a string feature representing the answer.
### Data Splits
train: 50,488 question answer pairs
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
Question-answer pairs derived from [ArXiv](https://arxiv.org/) abstracts.
#### Initial Data Collection and Normalization
The "ArtifactAI/arxiv-math-instruct-50k" dataset consists of question-answer pairs derived from ArXiv abstracts. Questions are generated from ArXiv papers in the following categories:
"math.AC", "math.AG", "math.AP", "math.AT", "math.CA", "math.CO", "math.CT", "math.CV", "math.DG", "math.DS", "math.FA", "math.GM", "math.GN", "math.GR", "math.GT", "math.HO",
"math.IT", "math.KT", "math.LO", "math.MG", "math.MP", "math.NA", "math.NT", "math.OA", "math.OC", "math.PR", "math.QA", "math.RA", "math.RT", "math.SG", "math.SP", "math.ST", "math-ph"
Questions are generated using the [t5-base model](https://huggingface.co/t5-base), while the answers are generated using the [GPT-3.5-turbo model](https://openai.com/chatgpt).
### Annotations
The dataset doesn't contain annotations.
### Personal and Sensitive Information
None
#### Notice policy
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
Clearly identify the copyrighted work claimed to be infringed.
Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
And contact us at the following email address: matt at artifactai.com and datasets at huggingface.co
#### Take down policy
The original authors will comply to legitimate requests by removing the affected sources from the next release of the corpus.
Hugging Face will also update this repository accordingly.
### Citation Information
```
@misc{arxiv-math-instruct-50k,
title={arxiv-math-instruct-50},
author={Matthew Kenney},
year={2023}
}
```
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|
open-llm-leaderboard/details_shareAI__bimoGPT-llama2-13b | open-llm-leaderboard | 2023-09-17T02:26:46Z | 286 | 0 | [
"region:us"
]
| null | 2023-08-18T00:06:00Z | ---
pretty_name: Evaluation run of shareAI/bimoGPT-llama2-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [shareAI/bimoGPT-llama2-13b](https://huggingface.co/shareAI/bimoGPT-llama2-13b)\
\ 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_shareAI__bimoGPT-llama2-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T02:26:34.744739](https://huggingface.co/datasets/open-llm-leaderboard/details_shareAI__bimoGPT-llama2-13b/blob/main/results_2023-09-17T02-26-34.744739.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\": 0.0014681208053691276,\n\
\ \"em_stderr\": 0.0003921042190298278,\n \"f1\": 0.05842386744966444,\n\
\ \"f1_stderr\": 0.0013305449660371358,\n \"acc\": 0.43888155205954144,\n\
\ \"acc_stderr\": 0.01031967359624197\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.0003921042190298278,\n\
\ \"f1\": 0.05842386744966444,\n \"f1_stderr\": 0.0013305449660371358\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11296436694465505,\n \
\ \"acc_stderr\": 0.008719339028833059\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7647987371744278,\n \"acc_stderr\": 0.011920008163650882\n\
\ }\n}\n```"
repo_url: https://huggingface.co/shareAI/bimoGPT-llama2-13b
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_08_09T18_04_32.310000
path:
- '**/details_harness|arc:challenge|25_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T02_26_34.744739
path:
- '**/details_harness|drop|3_2023-09-17T02-26-34.744739.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T02-26-34.744739.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T02_26_34.744739
path:
- '**/details_harness|gsm8k|5_2023-09-17T02-26-34.744739.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T02-26-34.744739.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hellaswag|10_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T18:04:32.310000.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T18:04:32.310000.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T18:04:32.310000.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T02_26_34.744739
path:
- '**/details_harness|winogrande|5_2023-09-17T02-26-34.744739.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T02-26-34.744739.parquet'
- config_name: results
data_files:
- split: 2023_08_09T18_04_32.310000
path:
- results_2023-08-09T18:04:32.310000.parquet
- split: 2023_09_17T02_26_34.744739
path:
- results_2023-09-17T02-26-34.744739.parquet
- split: latest
path:
- results_2023-09-17T02-26-34.744739.parquet
---
# Dataset Card for Evaluation run of shareAI/bimoGPT-llama2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/shareAI/bimoGPT-llama2-13b
- **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 [shareAI/bimoGPT-llama2-13b](https://huggingface.co/shareAI/bimoGPT-llama2-13b) 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_shareAI__bimoGPT-llama2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T02:26:34.744739](https://huggingface.co/datasets/open-llm-leaderboard/details_shareAI__bimoGPT-llama2-13b/blob/main/results_2023-09-17T02-26-34.744739.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": 0.0014681208053691276,
"em_stderr": 0.0003921042190298278,
"f1": 0.05842386744966444,
"f1_stderr": 0.0013305449660371358,
"acc": 0.43888155205954144,
"acc_stderr": 0.01031967359624197
},
"harness|drop|3": {
"em": 0.0014681208053691276,
"em_stderr": 0.0003921042190298278,
"f1": 0.05842386744966444,
"f1_stderr": 0.0013305449660371358
},
"harness|gsm8k|5": {
"acc": 0.11296436694465505,
"acc_stderr": 0.008719339028833059
},
"harness|winogrande|5": {
"acc": 0.7647987371744278,
"acc_stderr": 0.011920008163650882
}
}
```
### 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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|
distil-whisper/librispeech_asr-timestamped | distil-whisper | 2023-09-25T10:30:13Z | 286 | 0 | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| [
"automatic-speech-recognition"
]
| 2023-09-22T09:05:08Z | ---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- en
-pretty_name: LibriSpeech ASR
---
# Distil Whisper: LibriSpeech ASR With Timestamps
This is a variant of the [LibriSpeech ASR](https://huggingface.co/datasets/librispeech_asr) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling and timestamp prediction. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/librispeech_asr).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/librispeech_asr", "all")
# take the first sample of the validation set
sample = dataset["validation.clean"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/librispeech_asr", "all", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation.clean"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under cc-by-4.0.
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|
ckotait/ATRDataset | ckotait | 2023-12-04T15:01:15Z | 286 | 0 | [
"region:us"
]
| null | 2023-11-23T16:20:10Z | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 674327851.666
num_examples: 16706
- name: validation
num_bytes: 46935738.0
num_examples: 1000
- name: test
num_bytes: 16859858.0
num_examples: 200
download_size: 813600043
dataset_size: 738123447.666
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for Dataset Name
The Active Template Regression (ATR) dataset comprises 18 semantic category labels, including face, sunglasses, hat, scarf, hair, upper clothes, left arm, right arm, belt, pants, left leg, right leg, skirt, left shoe, right shoe, bag, dress, and background. A total of 17,700 images were incorporated into the ATR dataset. 16,700 images were designated for training, and 1,000 for testing.
- **Curated by:** Xiaodan Liang, Si Liu, Xiaohui Shen, Jianchao Yang, Luoqi Liu, Jian Dong, Liang Lin, Shuicheng Yan
- **Shared by:** Xiaodan Liang, Si Liu, Xiaohui Shen, Jianchao Yang, Luoqi Liu, Jian Dong, Liang Lin, Shuicheng Yan
- **License:** MIT
# Dataset Sources
- **Repository:** https://github.com/lemondan/HumanParsing-Dataset
- **Paper:** Deep Human Parsing with Active Template Regression
# Human Parsing Labels
- 0: **background**
- 1: **hat**
- 2: **hair**
- 3: **sunglasses**
- 4: **upperclothes**
- 5: **skirt**
- 6: **pants**
- 7: **dress**
- 8: **belt**
- 9: **leftshoe**
- 10: **rightshoe**
- 11: **face**
- 12: **leftleg**
- 13: **rightleg**
- 14: **leftarm**
- 15: **rightarm**
- 16: **bag**
- 17: **scarf**
# Uses
Semantic segmentation, and more specifically, human body parsing.
# Dataset Card Authors
Christian Kotait
**BibTeX:**
@article{liang2015deep,
title={Deep human parsing with active template regression},
author={Liang, Xiaodan and Liu, Si and Shen, Xiaohui and Yang, Jianchao and Liu, Luoqi and Dong, Jian and Lin, Liang and Yan, Shuicheng},
journal={IEEE transactions on pattern analysis and machine intelligence},
volume={37},
number={12},
pages={2402--2414},
year={2015},
publisher={IEEE}
}
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|
twi_wordsim353 | null | 2022-11-03T16:07:57Z | 285 | 1 | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"language:tw",
"license:unknown",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
- tw
license:
- unknown
multilinguality:
- multilingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
paperswithcode_id: null
pretty_name: Yorùbá Wordsim-353
dataset_info:
features:
- name: twi1
dtype: string
- name: twi2
dtype: string
- name: similarity
dtype: float32
splits:
- name: test
num_bytes: 7285
num_examples: 274
download_size: 6141
dataset_size: 7285
---
# Dataset Card for Yorùbá Wordsim-353
## 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.aclweb.org/anthology/2020.lrec-1.335/
- **Repository:** https://github.com/ajesujoba/YorubaTwi-Embedding
- **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335/
- **Leaderboard:** -
- **Point of Contact:** [Kwabena Amponsah-Kaakyire](mailto:[email protected])
### Dataset Summary
A translation of the word pair similarity dataset wordsim-353 to Twi. However, only 274 (out of 353) pairs of words were translated
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Twi (ISO 639-1: tw)
## Dataset Structure
### Data Instances
An instance consists of a pair of words as well as their similarity. The dataset contains both the original English words (from wordsim-353) as well as their translation to Twi.
### Data Fields
- `twi1`: the first word of the pair; translation to Twi
- `twi2`: the second word of the pair; translation to Twi
- `similarity`: similarity rating according to the English dataset
### Data Splits
Only the test data is available
## 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
```
@inproceedings{alabi-etal-2020-massive,
title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
author = "Alabi, Jesujoba and
Amponsah-Kaakyire, Kwabena and
Adelani, David and
Espa{\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
pages = "2754--2762",
abstract = "The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yor{\`u}b{\'a} and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yor{\`u}b{\'a} and Twi. We extend the analysis to contextual word embeddings and evaluate multilingual BERT on a named entity recognition task. For this, we annotate with named entities the Global Voices corpus for Yor{\`u}b{\'a}. As output of the work, we provide corpora, embeddings and the test suits for both languages.",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
### Contributions
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset. | [
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|
Abirate/french_book_reviews | Abirate | 2022-08-25T19:26:48Z | 285 | 4 | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:fr",
"doi:10.57967/hf/1052",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- crowdsourced
language:
- fr
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
---
# ****Dataset Card for French book reviews****
# **I-Dataset Summary**
The majority of review datasets are in English. There are datasets in other languages, but not many. Through this work, I would like to enrich the datasets in the French language(my mother tongue with Arabic).
The data was retrieved from two French websites: [Babelio](https://www.babelio.com/) and [Critiques Libres](http://www.critiqueslibres.com/)
Like Wikipedia, these two French sites are made possible by the contributions of volunteers who use the Internet to share their knowledge and reading experiences.
The French book reviews is a dataset of a huge number of reader reviews on French books that ill be constantly updated over time.
# **II-Supported Tasks and Leaderboards**
- Multi-label text classification : The dataset can be used to train a model for text-classification, which consists of classifying reviews by label value. Success on this task is typically measured by achieving a high or low accuracy.
# **III-Languages**
The texts in the dataset are in French (fr).
# **IV-Dataset Structure**
#### Data Instances
A JSON-formatted example of a typical instance in the dataset:
```python
{
"book_title": "La belle histoire des maths",
"author": "Michel Rousselet",
"reader_review": "C’est un livre impressionnant, qui inspire le respect
par la qualité de sa reliure et son contenu. Je le feuillette et je découvre
à chaque tour de page un thème distinct magnifiquement illustré. Très beau livre !",
"rating": 4.0,
"label": 1
}
```
#### Data Fields
- **book_title**: The title of the book that received the reader's review,
- **author** : The author of the book that received the reader's review,
- **reader_review** : The text of the reader's review,
- **rating**: A five-star rating system is used to rate the book read,
- **label** : A post-processed field indicating if the review is positive (1), neutral(0), or negative(-1) based on the rating field. For more details, see the [Notebook of the Dataset creation](https://github.com/Abirate/Dataset_Creation_Scrapy_Project_French_book_reviews/blob/master/scrapyproject_a_to_z_dataset_book_reviews.ipynb)
#### Data Splits
I kept the dataset as one block (train), so it can be shuffled and split by users later using methods of the hugging face dataset library like the (.train_test_split()) method.
# **V-Dataset Creation**
#### Curation Rationale
The majority of review datasets are in English. There are datasets in other languages, but not many. Through this work, I would like to enrich the datasets in the French language (French is my mother tongue with Arabic) and slightly contribute to advancing data science and AI, not only for English NLP tasks but for other languages around the world.
French is an international language and it is gaining ground. In addition, it is the language of love. The richness of the French language, so appreciated around the world, is largely related to the richness of its culture. The most telling example is French literature, which has many world-famous writers, such as [Gustave Flaubert](https://en.wikipedia.org/wiki/Gustave_Flaubert), [Albert Camus](https://iep.utm.edu/camus/), [Victor Hugo](https://en.wikipedia.org/wiki/Victor_Hugo), [Molière](https://en.wikipedia.org/wiki/Moli%C3%A8re), [Simone de Beauvoir](https://iep.utm.edu/beauvoir/), [Antoine de Saint-Exupéry](https://en.wikipedia.org/wiki/Antoine_de_Saint-Exup%C3%A9ry): the author of "Le Petit Prince" (The Little Prince), which is still among the most translated books in literary history. And one of the world-famous quotes from this book is: "Voici mon secret. Il est très simple: on ne voit bien qu'avec le coeur. L'essentiel est invisible pour les yeux." etc.
#### Source Data
The source of Data is: two French websites: [Babelio](https://www.babelio.com/) and [Critiques Libres](http://www.critiqueslibres.com/)
#### Initial Data Collection and Normalization
The data was collected using web scraping (with Scrapy Framework) and subjected to additional data processing. For more details, see this notebook, which details the dataset creation process. [Notebook of the Dataset creation](https://github.com/Abirate/Dataset_Creation_Scrapy_Project_French_book_reviews/blob/master/scrapyproject_a_to_z_dataset_book_reviews.ipynb)
**Note**: This dataset will be constantly updated to include the most recent reviews on French books by aggregating the old datasets with the updated ones in order to have a huge dataset over time.
#### Who are the source Data producers ?
I created the Dataset using web scraping, by building a spider and a crawler to scrape the two french web sites [Babelio](https://www.babelio.com/) and [Critiques Libres](http://www.critiqueslibres.com/)
#### Annotations
Annotations are part of the initial data collection (see the script above).
# **VI-Additional Informations**
#### Dataset Curators
Abir ELTAIEF
#### Licensing Information
This work is licensed under [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
#### Contributions
Thanks to [@Abirate](https://huggingface.co/Abirate) for creating and adding this dataset.
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|
cdleong/piglatin-mt | cdleong | 2022-10-24T19:22:09Z | 285 | 0 | [
"task_categories:translation",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
language:
- en
license:
- mit
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
language_details: eng and engyay
---
## Dataset Description
- **Homepage:** cdleong.github.io
# Dataset Summary:
Pig-latin machine and English parallel machine translation corpus.
Based on [The Project Gutenberg EBook of "De Bello Gallico" and Other Commentaries](https://www.gutenberg.org/ebooks/10657)
Converted to pig-latin with https://github.com/bpabel/piglatin
Blank lines removed.
## Dataset Structure
```
DatasetDict({
train: Dataset({
features: ['translation'],
num_rows: 14778
})
validation: Dataset({
features: ['translation'],
num_rows: 1000
})
})
```
### Data Instances
```
{
'translation':
{
'eng': 'thrown into disorder they returned with more precipitation than is usual',
'engyay': 'own-thray into-ay isorder-day ey-thay eturned-ray ith-way ore-may ecipitation-pray an-thay is-ay usual-ay'
}
}
```
### Data Fields
- `translation`: a dictionary containing two strings paired with a key indicating the corresponding language.
### Data Splits
- `train`: most of the data, 13,232 samples total.
- `dev`: 1k holdout samples, created with the datasets.train_test_split() function | [
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|
gorkemgoknar/tr_ted_talk_translated | gorkemgoknar | 2022-01-13T09:14:54Z | 285 | 1 | [
"language:tr",
"license:apache-2.0",
"dataset",
"turkish",
"ted-multi",
"cleaned",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- tr
thumbnail:
tags:
- dataset
- turkish
- ted-multi
- cleaned
license: apache-2.0
datasets:
- ted-multi
---
# Turkish Ted talk translations
# Created from ted-multi dataset
adding processing steps here if you want another language
```python
#using Turkish as target
target_lang="tr" # change to your target lang
from datasets import load_dataset
#ted-multi is a multiple language translated dataset
#fits for our case , not to big and curated but need a simple processing
dataset = load_dataset("ted_multi")
dataset.cleanup_cache_files()
#original from patrick's
#chars_to_ignore_regex = '[,?.!\-\;\:\"“%‘”�—’…–]' # change to the ignored characters of your fine-tuned model
#will use cahya/wav2vec2-base-turkish-artificial-cv
#checking inside model repository to find which chars removed (no run.sh)
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
import re
def extract_target_lang_entries(batch):
#specific mapping for ted_multi dataset
#need to find index of language in each translation as it can shift
try:
target_index_for_lang= batch["translations"]["language"].index(target_lang)
except ValueError:
#target not in list empty it for later processing
batch["text"] = None
return batch
#index_translation_pairs = zip(batch, target_index_for_batch)
text= batch["translations"]["translation"][target_index_for_lang]
batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower())
return batch
#this dataset has additional columns need to say it
cols_to_remove = ['translations', 'talk_name']
dataset = dataset.map(extract_target_lang_entries, remove_columns=cols_to_remove)
#on preocessing we tagged None for empty ones
dataset_cleaned = dataset.filter(lambda x: x['text'] is not None)
dataset_cleaned
from huggingface_hub import notebook_login
notebook_login()
dataset_cleaned.push_to_hub(f"{target_lang}_ted_talk_translated")
``` | [
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|
allenai/soda | allenai | 2023-01-04T09:24:32Z | 285 | 110 | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"source_datasets:extended|Atomic10x",
"language:en",
"license:cc-by-4.0",
"dialogue",
"narrative",
"commonsense",
"arxiv:2212.10465",
"region:us"
]
| [
"conversational"
]
| 2023-01-04T08:51:53Z | ---
language:
- en
language_creators:
- machine-generated
annotation_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: SODA
size_categories:
- 1M<n<10M
splits:
- name: train
num_examples: 1191582
- name: valid
num_examples: 146346
- name: test
num_examples: 148968
dataset_size: 1486896
source_datasets:
- original
- extended|Atomic10x
tags:
- dialogue
- narrative
- commonsense
task_categories:
- conversational
task_ids:
- dialogue-generation
---
# Dataset Card for 🥤SODA
## Dataset Description
- **Repository:** [Code](https://github.com/skywalker023/sodaverse)
- **Paper:** [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://arxiv.org/abs/2212.10465)
- **Point of Contact:** [Hyunwoo Kim](mailto:[email protected])
## Dataset Summary
🥤SODA is the first publicly available, million-scale, high-quality dialogue dataset covering a wide range of social interactions. Dialogues are distilled from a PLM (InstructGPT; Ouyang et al., 2022) by contextualizing social commonsense knowledge from a knowledge graph (Atomic10x; West et al., 2022). Human evaluation shows that dialogues in SODA are more consistent, specific, and (surprisingly) natural than prior human-authored datasets – e.g., DailyDialog (Li et al., 2017), BlendedSkillTalk (Smith et al., 2020). Also, since social commonsense knowledge encompasses emotional reactions (i.e., the xReact `relation`), SODA includes 385K conversations labeled with 1.7K unique emotions along with information about the experiencer and the cause – i.e., `PersonX` and the `head` event in the symbolic commonsense knowledge triple.
## Languages
English
## Dataset Structure
field | type | description
--- | --- | ---
`head` | str | the head event in the symbolic commonsense knowledge triple
`relation` | str | the relationship between `head` and `tail` events
`tail` | str | the tail event in the symbolic commonsense knowledge triple
`literal` | str | the symbolic commonsense knowledge in sentence-form
`narrative` | str | narrative based on the `literal`
`dialogue` | list of str | dialogue grounded in the `narrative`
`speakers` | list of str | the speakers for each turn in the `dialogue`
`PersonX` | str | the assigned name for PersonX in the commonsense knowledge triple
`PersonY` | str\|null | the assigned name for PersonY in the commonsense knowledge triple
`PersonZ` | str\|null | the assigned name for PersonZ in the commonsense knowledge triple
`original_index` | int | the original index from Atomic10x
`split` | str | the split information: {train, valid, test}
`head_answer` | str | the answer for whether the `head` is included in the `narrative`: {Yes, Unknown}
`pmi_head_answer` | str | the answer for whether the `head` is included in the `narrative` with point-wise mutual information applied: {Yes, No, Unknown}
`relation_tail_answer` | str | the answer for whether the `relation`-`tail` is included in the `dialogue`: {Yes, No, Unknown}
`pmi_relation_tail_answer` | str | the answer for whether the `relation`-`tail` is included in the `dialogue` with point-wise mutual information applied: {Yes, No, Unknown}
## Dataset Creation
To create 🥤SODA, we distill dialogues from InstructGPT by contextualizing social commonsense knowledge – i.e., adding context information in multiple steps: (1) Retrieve social commonsense from the symbolic commonsense knowledge graph, (2) convert it into sentence form, (3) generate a narrative from the sentence, (4) infer the speakers from the narrative, and finally (5) derive contentful conversation grounded in the narrative and speakers. Anchoring the PLM in commonsense knowledge for deriving conversations offers two key advantages: (1) minimizing nonsensical conversations and (2) maximizing diversity. For more details, please refer to our [paper](https://arxiv.org/abs/2212.10465).
### Further Details, Social Impacts, and Limitations
Please refer to our [paper](https://arxiv.org/abs/2212.10465).
## Trained Model
Using 🥤SODA, we train 🧑🏻🚀COSMO: a generalizable conversation agent outperforming previous best-performing agents on both in- and out-of-domain datasets. COSMO-3B is available [here](https://huggingface.co/allenai/cosmo-xl)!
## Additional Information
For a brief summary of our paper, please see this [tweet](https://twitter.com/hyunw__kim/status/1605400305126248448).
### Citation
Please cite our work if you find the resources in this repository useful:
```
@article{kim2022soda,
title={SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization},
author={Hyunwoo Kim and Jack Hessel and Liwei Jiang and Peter West and Ximing Lu and Youngjae Yu and Pei Zhou and Ronan Le Bras and Malihe Alikhani and Gunhee Kim and Maarten Sap and Yejin Choi},
journal={ArXiv},
year={2022},
volume={abs/2212.10465}
}
``` | [
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|
VinayHajare/Fruits-30 | VinayHajare | 2023-11-11T05:00:28Z | 285 | 1 | [
"task_categories:image-classification",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"multiclass-image-classification",
"vision",
"region:us"
]
| [
"image-classification"
]
| 2023-08-16T15:54:47Z | ---
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- multiclass-image-classification
- vision
size_categories:
- n<1K
---
# Fruits30 Dataset
## Description:
The Fruits30 dataset is a collection of images featuring 30 different types of fruits. Each image has been preprocessed and standardized to a size of 224x224 pixels, ensuring uniformity in the dataset.
## Dataset Composition:
- **Number of Classes:** 30
- **Image Resolution:** 224x224 pixels
- **Total Images:** 826
## Classes:
0 : acerolas
1 : apples
2 : apricots
3 : avocados
4 : bananas
5 : blackberries
6 : blueberries
7 : cantaloupes
8 : cherries
9 : coconuts
10 : figs
11 : grapefruits
12 : grapes
13 : guava
14 : kiwifruit
15 : lemons
16 : limes
17 : mangos
18 : olives
19 : oranges
20 : passionfruit
21 : peaches
22 : pears
23 : pineapples
24 : plums
25 : pomegranates
26 : raspberries
27 : strawberries
28 : tomatoes
29 : watermelons
## Preprocessing:
Images have undergone preprocessing to maintain consistency and facilitate model training. Preprocessing steps may include resizing, normalization, and other enhancements.
## Intended Use:
The Fruits30 dataset is suitable for tasks such as image classification, object recognition, and machine learning model training within the domain of fruit identification.
## Sources:
Croudsource.
## Note:
Ensure proper attribution and compliance with the dataset's licensing terms when using it for research or development purposes. | [
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|
open-llm-leaderboard/details_andreaskoepf__llama2-13b-megacode2_min100 | open-llm-leaderboard | 2023-09-22T15:48:47Z | 285 | 0 | [
"region:us"
]
| null | 2023-08-18T11:13:10Z | ---
pretty_name: Evaluation run of andreaskoepf/llama2-13b-megacode2_min100
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [andreaskoepf/llama2-13b-megacode2_min100](https://huggingface.co/andreaskoepf/llama2-13b-megacode2_min100)\
\ 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_andreaskoepf__llama2-13b-megacode2_min100\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T15:48:34.680007](https://huggingface.co/datasets/open-llm-leaderboard/details_andreaskoepf__llama2-13b-megacode2_min100/blob/main/results_2023-09-22T15-48-34.680007.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\": 0.0030411073825503355,\n\
\ \"em_stderr\": 0.0005638896908753115,\n \"f1\": 0.07890205536912773,\n\
\ \"f1_stderr\": 0.0016368809848969982,\n \"acc\": 0.4643729284759866,\n\
\ \"acc_stderr\": 0.010956919441194278\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0030411073825503355,\n \"em_stderr\": 0.0005638896908753115,\n\
\ \"f1\": 0.07890205536912773,\n \"f1_stderr\": 0.0016368809848969982\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.15921152388172857,\n \
\ \"acc_stderr\": 0.010077966717551878\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.01183587216483668\n\
\ }\n}\n```"
repo_url: https://huggingface.co/andreaskoepf/llama2-13b-megacode2_min100
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_17T10_46_30.131407
path:
- '**/details_harness|arc:challenge|25_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T15_48_34.680007
path:
- '**/details_harness|drop|3_2023-09-22T15-48-34.680007.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T15-48-34.680007.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T15_48_34.680007
path:
- '**/details_harness|gsm8k|5_2023-09-22T15-48-34.680007.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T15-48-34.680007.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hellaswag|10_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T10:46:30.131407.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T10:46:30.131407.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_machine_learning_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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- split: latest
path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T10_46_30.131407
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T10:46:30.131407.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
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- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T10:46:30.131407.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T15_48_34.680007
path:
- '**/details_harness|winogrande|5_2023-09-22T15-48-34.680007.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T15-48-34.680007.parquet'
- config_name: results
data_files:
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path:
- results_2023-08-17T10:46:30.131407.parquet
- split: 2023_09_22T15_48_34.680007
path:
- results_2023-09-22T15-48-34.680007.parquet
- split: latest
path:
- results_2023-09-22T15-48-34.680007.parquet
---
# Dataset Card for Evaluation run of andreaskoepf/llama2-13b-megacode2_min100
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/andreaskoepf/llama2-13b-megacode2_min100
- **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 [andreaskoepf/llama2-13b-megacode2_min100](https://huggingface.co/andreaskoepf/llama2-13b-megacode2_min100) 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_andreaskoepf__llama2-13b-megacode2_min100",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T15:48:34.680007](https://huggingface.co/datasets/open-llm-leaderboard/details_andreaskoepf__llama2-13b-megacode2_min100/blob/main/results_2023-09-22T15-48-34.680007.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": 0.0030411073825503355,
"em_stderr": 0.0005638896908753115,
"f1": 0.07890205536912773,
"f1_stderr": 0.0016368809848969982,
"acc": 0.4643729284759866,
"acc_stderr": 0.010956919441194278
},
"harness|drop|3": {
"em": 0.0030411073825503355,
"em_stderr": 0.0005638896908753115,
"f1": 0.07890205536912773,
"f1_stderr": 0.0016368809848969982
},
"harness|gsm8k|5": {
"acc": 0.15921152388172857,
"acc_stderr": 0.010077966717551878
},
"harness|winogrande|5": {
"acc": 0.7695343330702447,
"acc_stderr": 0.01183587216483668
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_yeontaek__Platypus2xOpenOrca-13B-IA3-v4 | open-llm-leaderboard | 2023-09-18T08:06:37Z | 285 | 0 | [
"region:us"
]
| null | 2023-08-27T12:10:57Z | ---
pretty_name: Evaluation run of yeontaek/Platypus2xOpenOrca-13B-IA3-v4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [yeontaek/Platypus2xOpenOrca-13B-IA3-v4](https://huggingface.co/yeontaek/Platypus2xOpenOrca-13B-IA3-v4)\
\ 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_yeontaek__Platypus2xOpenOrca-13B-IA3-v4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-18T08:06:24.321760](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__Platypus2xOpenOrca-13B-IA3-v4/blob/main/results_2023-09-18T08-06-24.321760.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\": 0.006396812080536913,\n\
\ \"em_stderr\": 0.0008164468837432404,\n \"f1\": 0.0888349412751677,\n\
\ \"f1_stderr\": 0.0018756142649103522,\n \"acc\": 0.4401587986402365,\n\
\ \"acc_stderr\": 0.010178434162145544\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.006396812080536913,\n \"em_stderr\": 0.0008164468837432404,\n\
\ \"f1\": 0.0888349412751677,\n \"f1_stderr\": 0.0018756142649103522\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10841546626231995,\n \
\ \"acc_stderr\": 0.008563852506627488\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7719021310181531,\n \"acc_stderr\": 0.011793015817663597\n\
\ }\n}\n```"
repo_url: https://huggingface.co/yeontaek/Platypus2xOpenOrca-13B-IA3-v4
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_23T14_45_16.156132
path:
- '**/details_harness|arc:challenge|25_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_18T08_06_24.321760
path:
- '**/details_harness|drop|3_2023-09-18T08-06-24.321760.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-18T08-06-24.321760.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_18T08_06_24.321760
path:
- '**/details_harness|gsm8k|5_2023-09-18T08-06-24.321760.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-18T08-06-24.321760.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hellaswag|10_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T14:45:16.156132.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_23T14_45_16.156132
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T14:45:16.156132.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T14:45:16.156132.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_18T08_06_24.321760
path:
- '**/details_harness|winogrande|5_2023-09-18T08-06-24.321760.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-18T08-06-24.321760.parquet'
- config_name: results
data_files:
- split: 2023_09_18T08_06_24.321760
path:
- results_2023-09-18T08-06-24.321760.parquet
- split: latest
path:
- results_2023-09-18T08-06-24.321760.parquet
---
# Dataset Card for Evaluation run of yeontaek/Platypus2xOpenOrca-13B-IA3-v4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeontaek/Platypus2xOpenOrca-13B-IA3-v4
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [yeontaek/Platypus2xOpenOrca-13B-IA3-v4](https://huggingface.co/yeontaek/Platypus2xOpenOrca-13B-IA3-v4) 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_yeontaek__Platypus2xOpenOrca-13B-IA3-v4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-18T08:06:24.321760](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__Platypus2xOpenOrca-13B-IA3-v4/blob/main/results_2023-09-18T08-06-24.321760.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": 0.006396812080536913,
"em_stderr": 0.0008164468837432404,
"f1": 0.0888349412751677,
"f1_stderr": 0.0018756142649103522,
"acc": 0.4401587986402365,
"acc_stderr": 0.010178434162145544
},
"harness|drop|3": {
"em": 0.006396812080536913,
"em_stderr": 0.0008164468837432404,
"f1": 0.0888349412751677,
"f1_stderr": 0.0018756142649103522
},
"harness|gsm8k|5": {
"acc": 0.10841546626231995,
"acc_stderr": 0.008563852506627488
},
"harness|winogrande|5": {
"acc": 0.7719021310181531,
"acc_stderr": 0.011793015817663597
}
}
```
### 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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|
deokhk/en_wiki_sentences_100000 | deokhk | 2023-11-21T07:37:59Z | 285 | 0 | [
"region:us"
]
| null | 2023-11-21T07:37:53Z | ---
dataset_info:
features:
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 12629524
num_examples: 100000
- name: dev
num_bytes: 122796
num_examples: 1000
download_size: 7913615
dataset_size: 12752320
---
# Dataset Card for "en_wiki_sentences_100000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
hrenwac_para | null | 2022-11-03T16:07:49Z | 284 | 0 | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:hr",
"license:cc-by-sa-3.0",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
- hr
license:
- cc-by-sa-3.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: HrenwacPara
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- en
- hr
config_name: hrenWaC
splits:
- name: train
num_bytes: 29602110
num_examples: 99001
download_size: 11640281
dataset_size: 29602110
---
# Dataset Card for hrenwac_para
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://nlp.ffzg.hr/resources/corpora/hrenwac/
- **Repository:** http://nlp.ffzg.hr/data/corpora/hrenwac/hrenwac.en-hr.txt.gz
- **Paper:** http://workshop2013.iwslt.org/downloads/IWSLT-2013-Cettolo.pdf
- **Leaderboard:**
- **Point of Contact:** [Nikola Ljubešič](mailto:[email protected])
### Dataset Summary
The hrenWaC corpus version 2.0 consists of parallel Croatian-English texts crawled from the .hr top-level domain for Croatia. The corpus was built with Spidextor (https://github.com/abumatran/spidextor), a tool that glues together the output of SpiderLing used for crawling and Bitextor used for bitext extraction. The accuracy of the extracted bitext on the segment level is around 80% and on the word level around 84%.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is bilingual with Croatian and English languages.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@misc{11356/1058,
title = {Croatian-English parallel corpus {hrenWaC} 2.0},
author = {Ljube{\v s}i{\'c}, Nikola and Espl{\`a}-Gomis, Miquel and Ortiz Rojas, Sergio and Klubi{\v c}ka, Filip and Toral, Antonio},
url = {http://hdl.handle.net/11356/1058},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {{CLARIN}.{SI} User Licence for Internet Corpora},
year = {2016} }
```
### Contributions
Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset. | [
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|
makhzan | null | 2022-11-03T16:07:47Z | 284 | 0 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ur",
"license:other",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ur
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: makhzan
dataset_info:
features:
- name: file_id
dtype: string
- name: metadata
dtype: string
- name: title
dtype: string
- name: num-words
dtype: int64
- name: contains-non-urdu-languages
dtype: string
- name: document_body
dtype: string
splits:
- name: train
num_bytes: 35637310
num_examples: 5522
download_size: 15187763
dataset_size: 35637310
---
# Dataset Card for makhzan
## 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://matnsaz.net/en/makhzan
- **Repository:** https://github.com/zeerakahmed/makhzan
- **Paper:** [More Information Needed]
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** Zeerak Ahmed
### Dataset Summary
An Urdu text corpus for machine learning, natural language processing and linguistic analysis.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
ur
## Dataset Structure
### Data Instances
```
{
"contains-non-urdu-languages": "No",
"document_body":
"
<body>
<section>
<p>بنگلہ دیش کی عدالتِ عالیہ نے طلاق کے ایک مقدمے کا فیصلہ کرتے ہوئے علما کے فتووں کو غیر قانونی قرار دیا ہے۔ عدالت نے پارلیمنٹ سے یہ درخواست کی ہے کہ وہ جلد ایسا قانون وضع کرے کہ جس کے بعد فتویٰ بازی قابلِ دست اندازیِ پولیس جرم بن جائے۔ بنگلہ دیش کے علما نے اس فیصلے پر بھر پور ردِ عمل ظاہرکرتے ہوئے اس کے خلاف ملک گیر تحریک چلانے کا اعلان کیا ہے۔ اس ضمن میں علما کی ایک تنظیم ”اسلامک یونٹی الائنس“ نے متعلقہ ججوں کو مرتد یعنی دین سے منحرف اور دائرۂ اسلام سے خارج قرار دیا ہے۔</p>
<p>فتوے کا لفظ دو موقعوں پر استعمال ہوتا ہے۔ ایک اس موقع پر جب کوئی صاحبِ علم شریعت کے کسی مئلے کے بارے میں اپنی رائے پیش کرتا ہے۔ دوسرے اس موقع پر جب کوئی عالمِ دین کسی خاص واقعے کے حوالے سے اپنا قانونی فیصلہ صادر کرتا ہے۔ ایک عرصے سے ہمارے علما کے ہاں اس دوسرے موقعِ استعمال کا غلبہ ہو گیا ہے۔ اس کا نتیجہ یہ نکلا ہے کہ اس لفظ کا رائے یا نقطۂ نظر کے مفہوم میں استعمال کم و بیش متروک ہو گیا ہے۔ چنانچہ اب فتوے کا مطلب ہی علما کی طرف سے کسی خاص مألے یا واقعے کے بارے میں حتمی فیصلے کا صدور سمجھا جاتا ہے۔ علما اسی حیثیت سے فتویٰ دیتے ہیں اور عوام الناس اسی اعتبار سے اسے قبول کرتے ہیں۔ اس صورتِ حال میں ہمارے نزدیک، چند مسائل پیدا ہوتے ہیں۔ اس سے پہلے کہ ہم مذکورہ فیصلے کے بارے میں اپنا تاثر بیان کریں، یہ ضروری معلوم ہوتا ہے کہ مختصر طور پر ان مسائل کا جائزہ لے لیا جائے۔</p>
<p>پہلا مألہ یہ پیدا ہوتا ہے کہ قانون سازی اور شرعی فیصلوں کا اختیار ایسے لوگوں کے ہاتھ میں آجاتا ہے جو قانون کی رو سے اس کے مجاز ہی نہیں ہوتے۔ کسی میاں بیوی کے مابین طلاق کے مألے میں کیا طلاق واقع ہوئی ہے یا نہیں ہوئی؟ ان کا نکاح قائم ہے یا باطل ہو گیا ہے؟ رمضان یا عید کا چاند نظر آیا ہے یا نہیں آیا؟کوئی مسلمان اپنے کسی قول یا اقدام کی وجہ سے کہیں دائرۂ اسلام سے خارج اورنتیجۃً مسلم شہریت کے قانونی حقوق سے محروم تو نہیں ہو گیا؟ یہ اور اس نوعیت کے بہت سے دوسرے معاملات سر تا سر قانون اور عدالت سے متعلق ہوتے ہیں۔ علما کی فتویٰ سازی کے نتیجے میںیہ امور گویا حکومت اورعدلیہ کے ہاتھ سے نکل کر غیر متعلق افراد کے ہاتھوں میں آجاتے ہیں۔</p>
<p>دوسرا مألہ یہ پیدا ہوتا ہے کہ قانون کی حاکمیت کا تصور مجروح ہوتا ہے اور لوگوں میں قانون سے روگردانی کے رجحانات کو تقویت ملتی ہے۔ اس کی وجہ یہ ہے کہ قانون اپنی روح میں نفاذ کا متقاضی ہوتا ہے۔ اگر اسے نفاذ سے محروم رکھا جائے تو اس کی حیثیت محض رائے اور نقطۂ نظر کی سی ہوتی ہے۔ غیر مجاز فرد سے صادر ہونے والا فتویٰ یا قانون حکومت کی قوتِ نافذہ سے محروم ہوتا ہے۔ اس کی خلاف ورزی پر کسی قسم کی سزا کا خوف نہیں ہوتا۔ چنانچہ فتویٰ اگر مخاطب کی پسند کے مطابق نہ ہو تو اکثر وہ اسے ماننے سے انکار کر دیتا ہے۔ اس طرح وہ فتویٰ یا قانون بے توقیر ہوتا ہے۔ ایسے ماحول میں رہنے والے شہریوں میں قانون ناپسندی کا رجحان فروغ پاتا ہے اور جیسے ہی انھیں موقع ملتا ہے وہ بے دریغ قانون کی خلاف ورزی کر ڈالتے ہیں۔</p>
<p>تیسرامسئلہ یہ پیدا ہوتا ہے کہ اگرغیر مجاز افراد سے صادر ہونے والے فیصلوں کو نافذ کرنے کی کوشش کی جائے تو ملک میں بد نظمی اور انارکی کا شدید اندیشہ پیدا ہو جاتا ہے۔ جب غیر مجازافراد سے صادر ہونے والے قانونی فیصلوں کو حکومتی سرپرستی کے بغیر نافذ کرنے کی کوشش کی جاتی ہے تو اپنے عمل سے یہ اس بات کا اعلان ہوتا ہے کہ مرجعِ قانون و اقتدارتبدیل ہو چکا ہے۔ جب کوئی عالمِ دین مثال کے طور پر، یہ فتویٰ صادر کرتا ہے کہ سینما گھروں اور ٹی وی اسٹیشنوں کو مسمار کرنامسلمانوں کی ذمہ داری ہے، یا کسی خاص قوم کے خلاف جہاد فرض ہو چکا ہے، یا فلاں کی دی گئی طلاق واقع ہو گئی ہے اور فلاں کی نہیں ہوئی، یا فلاں شخص یا گروہ اپنا اسلامی تشخص کھو بیٹھا ہے تو وہ درحقیقت قانونی فیصلہ جاری کر رہا ہوتا ہے۔ دوسرے الفاظ میں، وہ ریاست کے اندر اپنی ایک الگ ریاست بنانے کا اعلان کر رہا ہوتا ہے۔ اس کا نتیجہ سوائے انتشار اور انارکی کے اور کچھ نہیں نکلتا۔ یہی وجہ ہے کہ جن علاقوں میں حکومت کی گرفت کمزور ہوتی ہے وہاں اس طرح کے فیصلوں کا نفاذ بھی ہو جاتا ہے اور حکومت منہ دیکھتی رہتی ہے۔</p>
<p>چوتھا مسئلہ یہ پیدا ہوتا ہے کہ مختلف مذہبی مسالک کی وجہ سے ایک ہی معاملے میں مختلف اور متضاد فتوے منظرِ عام پر آتے ہیں۔ یہ تو ہمارے روز مرہ کی بات ہے کہ ایک ہی گروہ کو بعض علماے دین کافر قرار دیتے ہیں اور بعض مسلمان سمجھتے ہیں۔ کسی شخص کے منہ سے اگر ایک موقع پر طلاق کے الفاظ تین بار نکلتے ہیں تو بعض علما اس پر ایک طلاق کا حکم لگا کر رجوع کا حق باقی رکھتے ہیں اور بعض تین قرار دے کررجوع کو باطل قرار دیتے ہیں۔ یہ صورتِ حال ایک عام آدمی کے لیے نہایت دشواریاں پیدا کر دیتی ہے۔</p>
<p>پانچواں مسئلہ یہ پیدا ہوتا ہے کہ حکمران اگر دین و شریعت سے کچھ خاص دلچسپی نہ رکھتے ہوں تو وہ اس صورتِ حال میں شریعت کی روشنی میں قانون سازی کی طرف متوجہ نہیں ہوتے۔ کام چل رہا ہے کے اصول پر وہ اس طریقِ قانون سازی سے سمجھوتاکیے رہتے ہیں۔ اس کا نتیجہ یہ نکلتا ہے کہ حکومتی ادارے ضروری قانون سازی کے بارے میں بے پروائی کا رویہ اختیار کرتے ہیں اور قوانین اپنے فطری ارتقا سے محروم رہتے ہیں۔</p>
<p>چھٹا مألہ یہ پیدا ہوتا ہے کہ رائج الوقت قانون اور عدالتوں کی توہین کے امکانات پیدا ہو جاتے ہیں۔ جب کسی مسئلے میں عدالتیں اپنا فیصلہ سنائیں اور علما اسے باطل قرار دیتے ہوئے اس کے برعکس اپنا فیصلہ صادر کریں تو اس سے عدالتوں کا وقار مجروح ہوتا ہے۔ اس کا مطلب یہ ہوتا ہے کہ کوئی شہری عدلیہ کو چیلنج کرنے کے لیے کھڑا ہو گیا ہے۔</p>
<p>ان مسائل کے تناظر میں بنگلہ دیش کی عدالتِ عالیہ کا فیصلہ ہمارے نزدیک، امت کی تاریخ میں ایک عظیم فیصلہ ہے۔ جناب جاوید احمد صاحب غامدی نے اسے بجا طور پر صدی کا بہترین فیصلہ قرار دیا ہے۔ بنگلہ دیش کی عدالت اگر علما کے فتووں اور قانونی فیصلوں پر پابندی لگانے کے بجائے، ان کے اظہارِ رائے پر پابندی عائدکرتی تو ہم اسے صدی کا بدترین فیصلہ قرار دیتے اور انھی صفحات میں بے خوفِ لومۃ و لائم اس پر نقد کر رہے ہوتے۔</p>
<p>موجودہ زمانے میں امتِ مسلمہ کا ایک بڑا المیہ یہ ہے کہ اس کے علما اپنی اصل ذمہ داری کو ادا کرنے کے بجائے ان ذمہ داریوں کو ادا کرنے پر مصر ہیں جن کے نہ وہ مکلف ہیں اور نہ اہل ہیں۔ قرآن و سنت کی رو سے علما کی اصل ذمہ داری دعوت و تبلیغ، انذار و تبشیر اور تعلیم و تحقیق ہے۔ ان کا کام سیاست نہیں، بلکہ سیاست دانوں کو دین کی رہنمائی سے آگاہی ہے؛ ان کا کام حکومت نہیں، بلکہ حکمرانوں کی اصلاح کی کوشش ہے؛ ان کا کام جہاد و قتال نہیں، بلکہ جہادکی تعلیم اور جذبۂ جہاد کی بیداری ہے؛ اسی طرح ان کا کام قانون سازی اور فتویٰ بازی نہیں بلکہ تحقیق و اجتہاد ہے۔ گویا انھیں قرآنِ مجیدکامفہوم سمجھنے، سنتِ ثابتہ کا مدعا متعین کرنے اور قولِ پیغمبر کا منشامعلوم کرنے کے لیے تحقیق کرنی ہے اور جن امور میں قرآن و سنت خاموش ہیں ان میں اپنی عقل و بصیرت سے اجتہادی آراقائم کرنی ہیں۔ ان کی کسی تحقیق یا اجتہاد کو جب عدلیہ یا پارلیمنٹ قبول کرے گی تو وہ قانون قرار پائے گا۔ اس سے پہلے اس کی حیثیت محض ایک رائے کی ہوگی۔ اس لیے اسے اسی حیثیت سے پیش کیا جائے گا۔</p>
<p>اس کا مطلب یہ ہے کہ کوئی حکم نہیں لگایا جائے گا، کوئی فیصلہ نہیں سنایا جائے گا، کوئی فتویٰ نہیں دیا جائے گا، بلکہ طالبِ علمانہ لب و لہجے میں محض علم و استدلال کی بنا پر اپنا نقطۂ نظر پیش کیا جائے گا۔ یہ نہیں کہا جائے گا کہ فلاں شخص کافر ہے، بلکہ اس کی اگر ضرورت پیش آئے تو یہ کہا جائے گا کہ فلاں شخص کا فلاں عقیدہ کفر ہے۔ یہ نہیں کہا جائے گا کہ فلاں آدمی دائرۂ اسلام سے خارج ہو گیا ہے، بلکہ یہ کہا جائے گا کہ فلاں آدمی کا فلاں نقطۂ نظر اسلام کے دائرے میں نہیں آتا۔ یہ نہیں کہا جائے گا فلاں آدمی مشرک ہے، بلکہ یہ کہا جائے گا فلاں نظریہ یا فلاں طرزِ عمل شرک ہے۔ یہ نہیں کہا جائے گا کہ زید کی طرف سے دی گئی ایک وقت کی تین طلاقیں واقع ہو گئی ہیں، بلکہ یہ کہا جائے گا کہ ایک وقت کی تین طلاقیں واقع ہو نی چاہییں۔</p>
<p>حکم لگانا، فیصلہ سنانا، قانون وضع کرنا اورفتویٰ جاری کرنا درحقیقت، عدلیہ اور حکومت کا کام ہے کسی عالمِ دین یا کسی اور غیر مجاز فرد کی طرف سے اس کام کو انجام دینے کی کوشش سراسر تجاوز ہے۔ خلافتِ راشدہ کے زمانے میں اس اصول کو ہمیشہ ملحوظ رکھا گیا۔ شاہ ولی اللہ محدث دہلوی اپنی کتاب ”ازالتہ الخفا ء“ میں لکھتے ہیں:</p>
<blockquote>
<p>”اس زمانے تک وعظ اور فتویٰ خلیفہ کی رائے پر موقوف تھا۔ خلیفہ کے حکم کے بغیر نہ وعظ کہتے تھے اور نہ فتویٰ دیتے تھے۔ بعد میں خلیفہ کے حکم کے بغیر وعظ کہنے اور فتویٰ دینے لگے اور فتویٰ کے معاملے میں جماعت (مجلسِ شوریٰ) کے مشورہ کی جو صورت پہلے تھی وہ باقی نہ رہی——- (اس زمانے میں) جب کوئی اختلافی صورت نمودار ہوتی، خلیفہ کے سامنے معاملہ پیش کرتے، خلیفہ اہلِ علم و تقویٰ سے مشورہ کرنے کے بعد ایک رائے قائم کرتا اور وہی سب لوگوں کی رائے بن جاتی۔ حضرت عثمان کی شہادت کے بعد ہر عالم بطورِ خود فتویٰ دینے لگا اور اس طرح مسلمانوں میں اختلاف برپا ہوا۔“ (بحوالہ ”اسلامی ریاست میں فقہی اختلافات کا حل“، مولاناامین احسن اصلاحی، ص۳۲)</p>
</blockquote>
</section>
</body>
",
"file_id": "0001.xml",
"metadata":
"
<meta>
<title>بنگلہ دیش کی عدالت کا تاریخی فیصلہ</title>
<author>
<name>سید منظور الحسن</name>
<gender>Male</gender>
</author>
<publication>
<name>Mahnama Ishraq February 2001</name>
<year>2001</year>
<city>Lahore</city>
<link>https://www.javedahmedghamidi.org/#!/ishraq/5adb7341b7dd1138372db999?articleId=5adb7452b7dd1138372dd6fb&year=2001&decade=2000</link>
<copyright-holder>Al-Mawrid</copyright-holder>
</publication>
<num-words>1694</num-words>
<contains-non-urdu-languages>No</contains-non-urdu-languages>
</meta>
",
"num-words": 1694,
"title": "بنگلہ دیش کی عدالت کا تاریخی فیصلہ"
}
```
### Data Fields
```file_id (str)```: Document file_id corresponding to filename in repository.
```metadata(str)```: XML formatted string containing metadata on the document such as the document's title, information about the author and publication, as well as other potentially useful facts such as the number of Urdu words in the document and whether the document contains text in any other languages.
```title (str)```: Title of the document.
```num-words (int)```: Number of words in document.
```contains-non-urdu-languages (str)```: ```Yes``` if document contains words other than urdu, ```No``` otherwise.
```document_body```: XML formatted body of the document. Details below:
The document is divided into ```<section>``` elements. In general the rule is that a clear visual demarkation in the original text (such as a page break, or a horizontal rule) is used to indicate a section break. A heading does not automatically create a new section.
Each paragraph is a ```<p>``` element.
Headings are wrapped in an ```<heading>``` element.
Blockquotes are wrapped in a ```<blockquote>``` element. Blockquotes may themselves contain other elements.
Lists are wrapped in an ```<list>```. Individual items in each list are wrapped in an ```<li>``` element.
Poetic verses are wrapped in a ```<verse>``` element. Each verse is on a separate line but is not wrapped in an individual element.
Tables are wrapped in a ```<table>``` element. A table is divided into rows marked by ```<tr>``` and columns marked by ```<td>```.
Text not in the Urdu language is wrapped in an ```<annotation>``` tag (more below).
```<p>, <heading>, <li>, <td>``` and ```<annotation>``` tags are inline with the text (i.e. there is no new line character before and after the tag). Other tags have a new line after the opening and before the closing tag.
Due to the use of XML syntax, ```<```, ```>``` and ```&``` characters have been escaped as ```<```;, ```>```;, and ```&```; respectively. This includes the use of these characters in URLs inside metadata.
### Data Splits
All the data is in one split ```train```
## Dataset Creation
### Curation Rationale
All text in this repository has been selected for quality of language, upholding high editorial standards. Given the poor quality of most published Urdu text in digital form, this selection criteria allows the use of this text for natural language processing, and machine learning applications without the need to address fundamental quality issues in the text.
We have made efforts to ensure this text is as broadly representative as possible. Specifically we have attempted to select for as many authors as possible, and diversity in the gender of the author, as well as years and city of publication. This effort is imperfect, and we appreciate any attempts at pointing us to resources that can help diversify this text further.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Makhzan has been started with generous initial donations of text from two renowned journals Bunyad, from the Gurmani Center of Literature and Languages at the Lahore University of Management Sciences (LUMS), and Ishraq, from the Al-Mawrid Institute. This choice of sources allowed us to get a diversity of voices even in a small initial corpus, while ensuring the highest editorial standards available in published Urdu text. As a result your models can also maintain high linguistic standards.
### Annotations
#### Annotation process
Text is structured and annotated using XML syntax. The ontology of elements used is loosely based around HTML, with simplifications made when HTML's specificity is not needed, and additions made to express common occurences in this corpus that would be useful for linguistic analysis. The semantic tagging of text is editorial in nature, which is to say that another person semantically tagging the text may do so differently. Effort has been made however to ensure consistency, and to retain the original meaning of the text while making it easy to parse through linguistically different pieces of text for analysis.
Annotations have been made inline using an ```<annotation>``` element.
A language (lang) attribute is added to the ```<annotation>``` element to indicate text in other languages (such as quoted text or technical vocabulary presented in other languages and scripts). The attribute value a two-character ISO 639-1 code. So the resultant annotation for an Arabic quote for example, will be ```<annotation lang="ar"></annotation>```.
A type (type) attributed is added to indicate text that is not in a language per se but is not Urdu text. URLs for example are wrapped in an ```<annotation type="url">``` tag.
#### 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
A few of the files do not have valid XML and cannot be loaded. This issue is tracked [here](https://github.com/zeerakahmed/makhzan/issues/28)
## Additional Information
### Dataset Curators
Zeerak Ahmed
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{makhzan,
title={Maḵẖzan},
howpublished = "\url{https://github.com/zeerakahmed/makhzan/}",
}
```
### Contributions
Thanks to [@arkhalid](https://github.com/arkhalid) for adding this dataset. | [
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|
narrativeqa_manual | null | 2022-11-18T21:32:14Z | 284 | 0 | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1712.07040",
"region:us"
]
| [
"text2text-generation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
paperswithcode_id: narrativeqa
pretty_name: NarrativeQA
dataset_info:
features:
- name: document
struct:
- name: id
dtype: string
- name: kind
dtype: string
- name: url
dtype: string
- name: file_size
dtype: int32
- name: word_count
dtype: int32
- name: start
dtype: string
- name: end
dtype: string
- name: summary
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: question
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: answers
list:
- name: text
dtype: string
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 9115940054
num_examples: 32747
- name: test
num_bytes: 2911702563
num_examples: 10557
- name: validation
num_bytes: 968994186
num_examples: 3461
download_size: 22638273
dataset_size: 12996636803
---
# Dataset Card for Narrative QA Manual
## 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:** [NarrativeQA Homepage](https://deepmind.com/research/open-source/narrativeqa)
- **Repository:** [NarrativeQA Repo](https://github.com/deepmind/narrativeqa)
- **Paper:** [The NarrativeQA Reading Comprehension Challenge](https://arxiv.org/pdf/1712.07040.pdf)
- **Leaderboard:**
- **Point of Contact:** [Tomáš Kočiský](mailto:[email protected]) [Jonathan Schwarz](mailto:[email protected]) [Phil Blunsom]([email protected]) [Chris Dyer]([email protected]) [Karl Moritz Hermann](mailto:[email protected]) [Gábor Melis](mailto:[email protected]) [Edward Grefenstette](mailto:[email protected])
### Dataset Summary
NarrativeQA Manual is an English-language dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents. THIS DATASET REQUIRES A MANUALLY DOWNLOADED FILE! Because of a script in the original repository which downloads the stories from original URLs everytime, the links are sometimes broken or invalid. Therefore, you need to manually download the stories for this dataset using the script provided by the authors (https://github.com/deepmind/narrativeqa/blob/master/download_stories.sh). Running the shell script creates a folder named "tmp" in the root directory and downloads the stories there. This folder containing the stories can be used to load the dataset via `datasets.load_dataset("narrativeqa_manual", data_dir="<path/to/folder>")`.
### Supported Tasks and Leaderboards
The dataset is used to test reading comprehension. There are 2 tasks proposed in the paper: "summaries only" and "stories only", depending on whether the human-generated summary or the full story text is used to answer the question.
### Languages
English
## Dataset Structure
### Data Instances
A typical data point consists of a question and answer pair along with a summary/story which can be used to answer the question. Additional information such as the url, word count, wikipedia page, are also provided.
A typical example looks like this:
```
{
"document": {
"id": "23jncj2n3534563110",
"kind": "movie",
"url": "https://www.imsdb.com/Movie%20Scripts/Name%20of%20Movie.html",
"file_size": 80473,
"word_count": 41000,
"start": "MOVIE screenplay by",
"end": ". THE END",
"summary": {
"text": "Joe Bloggs begins his journey exploring...",
"tokens": ["Joe", "Bloggs", "begins", "his", "journey", "exploring",...],
"url": "http://en.wikipedia.org/wiki/Name_of_Movie",
"title": "Name of Movie (film)"
},
"text": "MOVIE screenplay by John Doe\nSCENE 1..."
},
"question": {
"text": "Where does Joe Bloggs live?",
"tokens": ["Where", "does", "Joe", "Bloggs", "live", "?"],
},
"answers": [
{"text": "At home", "tokens": ["At", "home"]},
{"text": "His house", "tokens": ["His", "house"]}
]
}
```
### Data Fields
- `document.id` - Unique ID for the story.
- `document.kind` - "movie" or "gutenberg" depending on the source of the story.
- `document.url` - The URL where the story was downloaded from.
- `document.file_size` - File size (in bytes) of the story.
- `document.word_count` - Number of tokens in the story.
- `document.start` - First 3 tokens of the story. Used for verifying the story hasn't been modified.
- `document.end` - Last 3 tokens of the story. Used for verifying the story hasn't been modified.
- `document.summary.text` - Text of the wikipedia summary of the story.
- `document.summary.tokens` - Tokenized version of `document.summary.text`.
- `document.summary.url` - Wikipedia URL of the summary.
- `document.summary.title` - Wikipedia Title of the summary.
- `question` - `{"text":"...", "tokens":[...]}` for the question about the story.
- `answers` - List of `{"text":"...", "tokens":[...]}` for valid answers for the question.
### Data Splits
The data is split into training, valiudation, and test sets based on story (i.e. the same story cannot appear in more than one split):
| Train | Valid | Test |
| ------ | ----- | ----- |
| 32747 | 3461 | 10557 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Stories and movies scripts were downloaded from [Project Gutenburg](https://www.gutenberg.org) and a range of movie script repositories (mainly [imsdb](http://www.imsdb.com)).
#### Who are the source language producers?
The language producers are authors of the stories and scripts as well as Amazon Turk workers for the questions.
### Annotations
#### Annotation process
Amazon Turk Workers were provided with human written summaries of the stories (To make the annotation tractable and to lead annotators towards asking non-localized questions). Stories were matched with plot summaries from Wikipedia using titles and verified the matching with help from human annotators. The annotators were asked to determine if both the story and the summary refer to a movie or a book (as some books are made into movies), or if they are the same part in a series produced in the same year. Annotators on Amazon Mechanical Turk were instructed to write 10 question–answer pairs each based solely on a given summary. Annotators were instructed to imagine that they are writing questions to test students who have read the full stories but not the summaries. We required questions that are specific enough, given the length and complexity of the narratives, and to provide adiverse set of questions about characters, events, why this happened, and so on. Annotators were encouraged to use their own words and we prevented them from copying. We asked for answers that are grammatical, complete sentences, and explicitly allowed short answers (one word, or a few-word phrase, or ashort sentence) as we think that answering with a full sentence is frequently perceived as artificial when asking about factual information. Annotators were asked to avoid extra, unnecessary information in the question or the answer, and to avoid yes/no questions or questions about the author or the actors.
#### Who are the annotators?
Amazon Mechanical Turk workers.
### Personal and Sensitive Information
None
## 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
The dataset is released under a [Apache-2.0 License](https://github.com/deepmind/narrativeqa/blob/master/LICENSE).
### Citation Information
```
@article{narrativeqa,
author = {Tom\'a\v s Ko\v cisk\'y and Jonathan Schwarz and Phil Blunsom and
Chris Dyer and Karl Moritz Hermann and G\'abor Melis and
Edward Grefenstette},
title = {The {NarrativeQA} Reading Comprehension Challenge},
journal = {Transactions of the Association for Computational Linguistics},
url = {https://TBD},
volume = {TBD},
year = {2018},
pages = {TBD},
}
```
### Contributions
Thanks to [@rsanjaykamath](https://github.com/rsanjaykamath) for adding this dataset. | [
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|
nkjp-ner | null | 2023-01-25T14:41:28Z | 284 | 1 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:gpl-3.0",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: NJKP NER
dataset_info:
features:
- name: sentence
dtype: string
- name: target
dtype:
class_label:
names:
'0': geogName
'1': noEntity
'2': orgName
'3': persName
'4': placeName
'5': time
splits:
- name: train
num_bytes: 1612125
num_examples: 15794
- name: test
num_bytes: 221092
num_examples: 2058
- name: validation
num_bytes: 196652
num_examples: 1941
download_size: 821629
dataset_size: 2029869
---
# Dataset Card for NJKP NER
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
http://nkjp.pl/index.php?page=0&lang=1
- **Repository:**
- **Paper:**
@book{przepiorkowski2012narodowy,
title={Narodowy korpus j{\k{e}}zyka polskiego},
author={Przepi{\'o}rkowski, Adam},
year={2012},
publisher={Naukowe PWN}
- **Leaderboard:**
- **Point of Contact:**
[email protected]
### Dataset Summary
A linguistic corpus is a collection of texts where one can find the typical use of a single word or a phrase, as well as their meaning and grammatical function. Nowadays, without access to a language corpus, it has become impossible to do linguistic research, to write dictionaries, grammars and language teaching books, to create search engines sensitive to Polish inflection, machine translation engines and software of advanced language technology. Language corpora have become an essential tool for linguists, but they are also helpful for software engineers, scholars of literature and culture, historians, librarians and other specialists of art and computer sciences.
The manually annotated 1-million word subcorpus of the NJKP, available on GNU GPL v.3
### Supported Tasks and Leaderboards
Named entity recognition
[More Information Needed]
### Languages
Polish
## Dataset Structure
### Data Instances
Two tsv files (train, dev) with two columns (sentence, target) and one (test) with just one (sentence).
### Data Fields
- sentence
- target
### Data Splits
Data is splitted in train/dev/test split.
## Dataset Creation
### Curation Rationale
This dataset is one of nine evaluation tasks to improve polish language processing.
### 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
GNU GPL v.3
### Citation Information
@book{przepiorkowski2012narodowy,
title={Narodowy korpus j{\k{e}}zyka polskiego},
author={Przepi{\'o}rkowski, Adam},
year={2012},
publisher={Naukowe PWN}
}
### Contributions
Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset. | [
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|
sede | null | 2022-11-18T21:44:41Z | 284 | 6 | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2106.05006",
"arxiv:2005.02539",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
pretty_name: SEDE (Stack Exchange Data Explorer)
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
paperswithcode_id: sede
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- parsing
dataset_info:
features:
- name: QuerySetId
dtype: uint32
- name: Title
dtype: string
- name: Description
dtype: string
- name: QueryBody
dtype: string
- name: CreationDate
dtype: string
- name: validated
dtype: bool
config_name: sede
splits:
- name: train
num_bytes: 4410584
num_examples: 10309
- name: validation
num_bytes: 380942
num_examples: 857
- name: test
num_bytes: 386599
num_examples: 857
download_size: 6318959
dataset_size: 5178125
---
# Dataset Card for SEDE
## 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
- **Repository:** https://github.com/hirupert/sede
- **Paper:** https://arxiv.org/abs/2106.05006
- **Leaderboard:** https://paperswithcode.com/sota/text-to-sql-on-sede
- **Point of Contact:** [email]([email protected])
### Dataset Summary
SEDE (Stack Exchange Data Explorer) is a dataset for Text-to-SQL tasks with more than 12,000 SQL queries and their natural language description. It's based on a real usage of users from the Stack Exchange Data Explorer platform, which brings complexities and challenges never seen before in any other semantic parsing dataset like including complex nesting, dates manipulation, numeric and text manipulation, parameters, and most importantly: under-specification and hidden-assumptions.
### Supported Tasks and Leaderboards
- `parsing`: The dataset can be used to train a model for Text-to-SQL task. A Seq2Seq model (e.g. T5) can be used to solve the task. A model with more inductive-bias (e.g. a model with a grammar-based decoder) or an interactive settings for Text-to-SQL (https://arxiv.org/abs/2005.02539) can improve the results further. The model performance is measured by how high its [PCM-F1](https://arxiv.org/abs/2106.05006) score is. A [t5-large](https://huggingface.co/t5-large) achieves a [PCM-F1 of 50.6](https://arxiv.org/abs/2106.05006).
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A typical data point comprises a question title, (optionally) a description and its underlying SQL query. In addition, each sample has a unique ID (from the Stack Exchange Data Explorer), its creation date and a boolean flag named `validated` if this sample was validated to be in gold quality by humans, see the paper for full details regarding the `validated` flag.
An instance for example:
```
{
'QuerySetId':1233,
'Title':'Top 500 Askers on the site',
'Description':'A list of the top 500 askers of questions ordered by average answer score excluding community wiki closed posts.',
'QueryBody':'SELECT * FROM (\nSELECT \n TOP 500\n OwnerUserId as [User Link],\n Count(Posts.Id) AS Questions,\n CAST(AVG(CAST(Score AS float)) as numeric(6,2)) AS [Average Question Score]\nFROM\n Posts\nWHERE \n PostTypeId = 1 and CommunityOwnedDate is null and ClosedDate is null\nGROUP BY\n OwnerUserId\nORDER BY\n Count(Posts.Id) DESC\n)ORDER BY\n [Average Question Score] DESC',
'CreationDate':'2010-05-27 20:08:16',
'validated':true
}
```
### Data Fields
- QuerySetId: a unique ID coming from the Stack Exchange Data Explorer.
- Title: utterance title.
- Description: utterance description (might be empty).
- QueryBody: the underlying SQL query.
- CreationDate: when this sample was created.
- validated: `true` if this sample was validated to be in gold quality by humans.
### Data Splits
The data is split into a training, validation and test set. The validation and test set contain only samples that were validated by humans to be in gold quality.
Train Valid Test
10309 857 857
## Dataset Creation
### Curation Rationale
Most available semantic parsing datasets, comprising of pairs of natural utterances and logical forms, were collected solely for the purpose of training and evaluation of natural language understanding systems. As a result, they do not contain any of the richness and variety of natural-occurring utterances, where humans ask about data they need or are curious about. SEDE contains a variety of real-world challenges which were rarely reflected so far in any other semantic parsing dataset. There is a large gap between the performance on SEDE compared to other common datasets, which leaves a room for future research for generalisation of Text-to-SQL models.
### Source Data
#### Initial Data Collection and Normalization
To introduce a realistic Text-to-SQL benchmark, we gather SQL queries together with their titles and descriptions from a naturally occurring dataset: the Stack Exchange Data Explorer. Stack Exchange is an online question & answers community, with over 3 million questions asked. However in its raw form many of the rows are duplicated or contain unusable queries or titles. The reason for this large difference between the original data size and the cleaned version is that any time that the author of the query executes it, an entry is saved to the log. To alleviate these issues, we write rule-based filters that remove bad queries/descriptions pairs with high precision. For example, we filter out examples with numbers in the description, if these numbers do not appear in the query (refer to the preprocessing script in the repository for the complete list of filters and the number of examples each of them filter). Whenever a query has multiple versions due to multiple executions, we take the last executed query which passed all filters. After this filtering step, we are left with 12,309 examples. Using these filters cleans most of the noise, but not all of it. To complete the cleaning process, we manually go over the examples in the validation and test sets, and either filter-out wrong examples or perform minimal changes to either the utterances or the queries (for example, fix a wrong textual value) to ensure that models are evaluated with correct data. The final number of all training, validation and test examples is 12,023.
#### Who are the source language producers?
The language producers are Stack Exchange Data Explorer (https://data.stackexchange.com/) users.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
All the data in the dataset is for public use.
## Considerations for Using the Data
### Social Impact of Dataset
We hope that the release of this challenging dataset will encourage research on improving generalisation for real-world SQL prediction that will help non technical business users acquire the data they need from their company's database.
### Discussion of Biases
[N/A]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially created by Moshe Hazoom, Vibhor Malik and Ben Bogin, during work done at Ruper.
### Licensing Information
Apache-2.0 License
### Citation Information
```
@misc{hazoom2021texttosql,
title={Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data},
author={Moshe Hazoom and Vibhor Malik and Ben Bogin},
year={2021},
eprint={2106.05006},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@Hazoom](https://github.com/Hazoom) for adding this dataset. | [
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|
sofc_materials_articles | null | 2023-03-09T10:44:46Z | 284 | 7 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:named-entity-recognition",
"task_ids:slot-filling",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2006.03039",
"region:us"
]
| [
"text-generation",
"fill-mask",
"token-classification",
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- token-classification
- text-classification
task_ids:
- named-entity-recognition
- slot-filling
- topic-classification
pretty_name: SofcMaterialsArticles
dataset_info:
features:
- name: text
dtype: string
- name: sentence_offsets
sequence:
- name: begin_char_offset
dtype: int64
- name: end_char_offset
dtype: int64
- name: sentences
sequence: string
- name: sentence_labels
sequence: int64
- name: token_offsets
sequence:
- name: offsets
sequence:
- name: begin_char_offset
dtype: int64
- name: end_char_offset
dtype: int64
- name: tokens
sequence:
sequence: string
- name: entity_labels
sequence:
sequence:
class_label:
names:
'0': B-DEVICE
'1': B-EXPERIMENT
'2': B-MATERIAL
'3': B-VALUE
'4': I-DEVICE
'5': I-EXPERIMENT
'6': I-MATERIAL
'7': I-VALUE
'8': O
- name: slot_labels
sequence:
sequence:
class_label:
names:
'0': B-anode_material
'1': B-cathode_material
'2': B-conductivity
'3': B-current_density
'4': B-degradation_rate
'5': B-device
'6': B-electrolyte_material
'7': B-experiment_evoking_word
'8': B-fuel_used
'9': B-interlayer_material
'10': B-interconnect_material
'11': B-open_circuit_voltage
'12': B-power_density
'13': B-resistance
'14': B-support_material
'15': B-thickness
'16': B-time_of_operation
'17': B-voltage
'18': B-working_temperature
'19': I-anode_material
'20': I-cathode_material
'21': I-conductivity
'22': I-current_density
'23': I-degradation_rate
'24': I-device
'25': I-electrolyte_material
'26': I-experiment_evoking_word
'27': I-fuel_used
'28': I-interlayer_material
'29': I-interconnect_material
'30': I-open_circuit_voltage
'31': I-power_density
'32': I-resistance
'33': I-support_material
'34': I-thickness
'35': I-time_of_operation
'36': I-voltage
'37': I-working_temperature
'38': O
- name: links
sequence:
- name: relation_label
dtype:
class_label:
names:
'0': coreference
'1': experiment_variation
'2': same_experiment
'3': thickness
- name: start_span_id
dtype: int64
- name: end_span_id
dtype: int64
- name: slots
sequence:
- name: frame_participant_label
dtype:
class_label:
names:
'0': anode_material
'1': cathode_material
'2': current_density
'3': degradation_rate
'4': device
'5': electrolyte_material
'6': fuel_used
'7': interlayer_material
'8': open_circuit_voltage
'9': power_density
'10': resistance
'11': support_material
'12': time_of_operation
'13': voltage
'14': working_temperature
- name: slot_id
dtype: int64
- name: spans
sequence:
- name: span_id
dtype: int64
- name: entity_label
dtype:
class_label:
names:
'0': ''
'1': DEVICE
'2': MATERIAL
'3': VALUE
- name: sentence_id
dtype: int64
- name: experiment_mention_type
dtype:
class_label:
names:
'0': ''
'1': current_exp
'2': future_work
'3': general_info
'4': previous_work
- name: begin_char_offset
dtype: int64
- name: end_char_offset
dtype: int64
- name: experiments
sequence:
- name: experiment_id
dtype: int64
- name: span_id
dtype: int64
- name: slots
sequence:
- name: frame_participant_label
dtype:
class_label:
names:
'0': anode_material
'1': cathode_material
'2': current_density
'3': degradation_rate
'4': conductivity
'5': device
'6': electrolyte_material
'7': fuel_used
'8': interlayer_material
'9': open_circuit_voltage
'10': power_density
'11': resistance
'12': support_material
'13': time_of_operation
'14': voltage
'15': working_temperature
- name: slot_id
dtype: int64
splits:
- name: train
num_bytes: 7402373
num_examples: 26
- name: test
num_bytes: 2650700
num_examples: 11
- name: validation
num_bytes: 1993857
num_examples: 8
download_size: 3733137
dataset_size: 12046930
---
# Dataset Card for SofcMaterialsArticles
## 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:** [boschresearch/sofc-exp_textmining_resources](https://github.com/boschresearch/sofc-exp_textmining_resources)
- **Repository:** [boschresearch/sofc-exp_textmining_resources](https://github.com/boschresearch/sofc-exp_textmining_resources)
- **Paper:** [The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain](https://arxiv.org/abs/2006.03039)
- **Leaderboard:**
- **Point of Contact:** [Annemarie Friedrich]([email protected])
### Dataset Summary
> The SOFC-Exp corpus contains 45 scientific publications about solid oxide fuel cells (SOFCs), published between 2013 and 2019 as open-access articles all with a CC-BY license. The dataset was manually annotated by domain experts with the following information:
>
> * Mentions of relevant experiments have been marked using a graph structure corresponding to instances of an Experiment frame (similar to the ones used in FrameNet.) We assume that an Experiment frame is introduced to the discourse by mentions of words such as report, test or measure (also called the frame-evoking elements). The nodes corresponding to the respective tokens are the heads of the graphs representing the Experiment frame.
> * The Experiment frame related to SOFC-Experiments defines a set of 16 possible participant slots. Participants are annotated as dependents of links between the frame-evoking element and the participant node.
> * In addition, we provide coarse-grained entity/concept types for all frame participants, i.e, MATERIAL, VALUE or DEVICE. Note that this annotation has not been performed on the full texts but only on sentences containing information about relevant experiments, and a few sentences in addition. In the paper, we run experiments for both tasks only on the set of sentences marked as experiment-describing in the gold standard, which is admittedly a slightly simplified setting. Entity types are only partially annotated on other sentences. Slot filling could of course also be evaluated in a fully automatic setting with automatic experiment sentence detection as a first step.
### Supported Tasks and Leaderboards
- `topic-classification`: The dataset can be used to train a model for topic-classification, to identify sentences that mention SOFC-related experiments.
- `named-entity-recognition`: The dataset can be used to train a named entity recognition model to detect `MATERIAL`, `VALUE`, `DEVICE`, and `EXPERIMENT` entities.
- `slot-filling`: The slot-filling task is approached as fine-grained entity-typing-in-context, assuming that each sentence represents a single experiment frame. Sequence tagging architectures are utilized for tagging the tokens of each experiment-describing sentence with the set of slot types.
The paper experiments with BiLSTM architectures with `BERT`- and `SciBERT`- generated token embeddings, as well as with `BERT` and `SciBERT` directly for the modeling task. A simple CRF architecture is used as a baseline for sequence-tagging tasks. Implementations of the transformer-based architectures can be found in the `huggingface/transformers` library: [BERT](https://huggingface.co/bert-base-uncased), [SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased)
### Languages
This corpus is in English.
## Dataset Structure
### Data Instances
As each example is a full text of an academic paper, plus annotations, a json formatted example is space-prohibitive for this README.
### Data Fields
- `text`: The full text of the paper
- `sentence_offsets`: Start and end character offsets for each sentence in the text.
- `begin_char_offset`: a `int64` feature.
- `end_char_offset`: a `int64` feature.
- `sentences`: A sequence of the sentences in the text (using `sentence_offsets`)
- `sentence_labels`: Sequence of binary labels for whether a sentence contains information of interest.
- `token_offsets`: Sequence of sequences containing start and end character offsets for each token in each sentence in the text.
- `offsets`: a dictionary feature containing:
- `begin_char_offset`: a `int64` feature.
- `end_char_offset`: a `int64` feature.
- `tokens`: Sequence of sequences containing the tokens for each sentence in the text.
- `feature`: a `string` feature.
- `entity_labels`: a dictionary feature containing:
- `feature`: a classification label, with possible values including `B-DEVICE`, `B-EXPERIMENT`, `B-MATERIAL`, `B-VALUE`, `I-DEVICE`.
- `slot_labels`: a dictionary feature containing:
- `feature`: a classification label, with possible values including `B-anode_material`, `B-cathode_material`, `B-conductivity`, `B-current_density`, `B-degradation_rate`.
- `links`: a dictionary feature containing:
- `relation_label`: a classification label, with possible values including `coreference`, `experiment_variation`, `same_experiment`, `thickness`.
- `start_span_id`: a `int64` feature.
- `end_span_id`: a `int64` feature.
- `slots`: a dictionary feature containing:
- `frame_participant_label`: a classification label, with possible values including `anode_material`, `cathode_material`, `current_density`, `degradation_rate`, `device`.
- `slot_id`: a `int64` feature.
- `spans`: a dictionary feature containing:
- `span_id`: a `int64` feature.
- `entity_label`: a classification label, with possible values including ``, `DEVICE`, `MATERIAL`, `VALUE`.
- `sentence_id`: a `int64` feature.
- `experiment_mention_type`: a classification label, with possible values including ``, `current_exp`, `future_work`, `general_info`, `previous_work`.
- `begin_char_offset`: a `int64` feature.
- `end_char_offset`: a `int64` feature.
- `experiments`: a dictionary feature containing:
- `experiment_id`: a `int64` feature.
- `span_id`: a `int64` feature.
- `slots`: a dictionary feature containing:
- `frame_participant_label`: a classification label, with possible values including `anode_material`, `cathode_material`, `current_density`, `degradation_rate`, `conductivity`.
- `slot_id`: a `int64` feature.
Very detailed information for each of the fields can be found in the [corpus file formats section](https://github.com/boschresearch/sofc-exp_textmining_resources#corpus-file-formats) of the associated dataset repo
### Data Splits
This dataset consists of three splits:
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| Input Examples | 26 | 8 | 11 |
The authors propose the experimental setting of using the training data in a 5-fold cross validation setting for development and tuning, and finally applying tte model(s) to the independent test set.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The corpus consists of 45
open-access scientific publications about SOFCs
and related research, annotated by domain experts.
### Annotations
#### Annotation process
For manual annotation, the authors use the InCeption annotation tool (Klie et al., 2018).
#### 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
The manual annotations created for the SOFC-Exp corpus are licensed under a [Creative Commons Attribution 4.0 International License (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@misc{friedrich2020sofcexp,
title={The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain},
author={Annemarie Friedrich and Heike Adel and Federico Tomazic and Johannes Hingerl and Renou Benteau and Anika Maruscyk and Lukas Lange},
year={2020},
eprint={2006.03039},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@ZacharySBrown](https://github.com/ZacharySBrown) for adding this dataset. | [
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|
yoruba_bbc_topics | null | 2023-01-25T15:03:35Z | 284 | 0 | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:yo",
"license:unknown",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- yo
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: Yoruba Bbc News Topic Classification Dataset (YorubaBbcTopics)
dataset_info:
features:
- name: news_title
dtype: string
- name: label
dtype:
class_label:
names:
'0': africa
'1': entertainment
'2': health
'3': nigeria
'4': politics
'5': sport
'6': world
- name: date
dtype: string
- name: bbc_url_id
dtype: string
splits:
- name: train
num_bytes: 197117
num_examples: 1340
- name: validation
num_bytes: 27771
num_examples: 189
- name: test
num_bytes: 55652
num_examples: 379
download_size: 265480
dataset_size: 280540
---
# Dataset Card for Yoruba BBC News Topic Classification dataset (yoruba_bbc_topics)
## 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:** https://github.com/uds-lsv/transfer-distant-transformer-african
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/
- **Leaderboard:** -
- **Point of Contact:** Michael A. Hedderich and David Adelani
{mhedderich, didelani} (at) lsv.uni-saarland.de
### Dataset Summary
A news headline topic classification dataset, similar to AG-news, for Yorùbá. The news headlines were collected from [BBC Yoruba](https://www.bbc.com/yoruba).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Yorùbá (ISO 639-1: yo)
## Dataset Structure
### Data Instances
An instance consists of a news title sentence and the corresponding topic label as well as publishing information (date and website id).
### Data Fields
- `news_title`: A news title.
- `label`: The label describing the topic of the news title. Can be one of the following classes: africa, entertainment, health, nigeria, politics, sport or world.
- `date`: The publication date (in Yorùbá).
- `bbc_url_id`: The identifier of the article in the BBC URL.
### 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 [@michael-aloys](https://github.com/michael-aloys) for adding this dataset. | [
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|
SuperAI2-Machima/ThaiQA_LST20 | SuperAI2-Machima | 2022-02-25T06:29:22Z | 284 | 0 | [
"license:mit",
"question-generation dataset",
"qa dataset",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
tags:
- question-generation dataset
- qa dataset
language:
- thai
- th
datasets:
- LST20
license: mit
---
[SuperAI Engineer Season 2](https://superai.aiat.or.th/) , [Machima](https://machchima.superai.me/)
Machima_ThaiQA_LST20 เป็นชุดข้อมูลที่สกัดหาคำถาม และคำตอบ จากบทความในชุดข้อมูล LST20 โดยสกัดได้คำถาม-ตอบทั้งหมด 7,642 คำถาม มีข้อมูล 4 คอลัมน์ ประกอบด้วย context, question, answer และ status ตามลำดับ
แสดงตัวอย่างดังนี้
context : ด.ต.ประสิทธิ์ ชาหอมชื่นอายุ 55 ปี ผบ.หมู่งาน ป.ตชด. 24 อุดรธานีถูกยิงด้วยอาวุธปืนอาก้าเข้าที่แขนซ้าย 3 นัดหน้าท้อง 1 นัดส.ต.อ.ประเสริฐ ใหญ่สูงเนินอายุ 35 ปี ผบ.หมู่กก. 1 ปส.2 บช.ปส. ถูกยิงเข้าที่แขนขวากระดูกแตกละเอียดร.ต.อ.ชวพล หมื่นโรจน์อายุ 32 ปีรอง สว.กก. 1 ปส. 2 บช.ปส. ถูกยิงเข้าที่แก้มและไหปลาร้าด้านขวา
question :ผบ.หมู่งาน ป.ตชด. 24 อุดรธานี ถูกยิงด้วยอาวุธปืนอะไรเข้าที่แขนซ้าย 3 นัดหน้าท้อง
answer : อาวุธปืนอาก้า
status : 1
ซึ่งใน 7,642 คำถาม จะมีคำถาม-ตอบ ที่สกัดออกมาได้ถูกต้อง และไม่ถูกต้องตาม ยกตัวอย่างเช่น ตอบไม่ตรงคำถาม หรือมีคำตอบอยู่ด้านในประโยคคำถาม
ทางทีมงานบ้านมณิมาได้ทำการตรวจสอบคำถามตอบ และทำการติด label ให้กับคู่ของคำถาม-ตอบ ที่ถูกต้อง และไม่ถูกต้อง โดย 1 = ถูกต้อง และ 0 = ไม่ถูกต้อง
จากคู่คำถาม-ตอบ 7,642 คำถาม
พบว่าถูกต้อง 4,438 คำถาม
ไม่ถูกต้อง 3,204 คำถาม
เพื่อน ๆ สามารถโหลดข้อมูลมาใช้โดยใช้โค้ดดังนี้
```python
!pip install datasets -qq #สำหรับโหลดdataset
from datasets import load_dataset
import pandas as pd
dataset = load_dataset("SuperAI2-Machima/ThaiQA_LST20")
train_df = pd.DataFrame(dataset['train'])
train_df
``` | [
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|
eugenesiow/Urban100 | eugenesiow | 2022-10-21T03:58:53Z | 284 | 0 | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"license:cc-by-4.0",
"other-image-super-resolution",
"region:us"
]
| [
"other"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: Urban100
tags:
- other-image-super-resolution
---
# Dataset Card for Urban100
## 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://github.com/jbhuang0604/SelfExSR
- **Repository**: https://huggingface.co/datasets/eugenesiow/Urban100
- **Paper**: https://openaccess.thecvf.com/content_cvpr_2015/html/Huang_Single_Image_Super-Resolution_2015_CVPR_paper.html
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
The Urban100 dataset contains 100 images of urban scenes. It commonly used as a test set to evaluate the performance of super-resolution models. It was first published by [Huang et al. (2015)](https://openaccess.thecvf.com/content_cvpr_2015/html/Huang_Single_Image_Super-Resolution_2015_CVPR_paper.html) in the paper "Single Image Super-Resolution From Transformed Self-Exemplars".
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/Urban100', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `validation` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/Urban100_HR/img_001.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/Urban100_LR_x2/img_001.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |validation|
|-------|---:|
|bicubic_x2|100|
|bicubic_x3|100|
|bicubic_x4|100|
## Dataset Creation
### Curation Rationale
The authors have created Urban100 containing 100 HR images with a variety of real-world structures.
### Source Data
#### Initial Data Collection and Normalization
The authors constructed this dataset using images from Flickr (under CC license) using keywords such as urban, city, architecture, and structure.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
No annotations.
#### Who are the annotators?
No annotators.
### 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
- **Original Authors**: [Huang et al. (2015)](https://github.com/jbhuang0604/SelfExSR)
### Licensing Information
The dataset provided uses images from Flikr under the CC (CC-BY-4.0) license.
### Citation Information
```bibtex
@InProceedings{Huang_2015_CVPR,
author = {Huang, Jia-Bin and Singh, Abhishek and Ahuja, Narendra},
title = {Single Image Super-Resolution From Transformed Self-Exemplars},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
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|
pierreguillou/DocLayNet-base | pierreguillou | 2023-05-17T08:56:30Z | 284 | 7 | [
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_categories:token-classification",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"size_categories:1K<n<10K",
"language:en",
"language:de",
"language:fr",
"language:ja",
"license:other",
"DocLayNet",
"COCO",
"PDF",
"IBM",
"Financial-Reports",
"Finance",
"Manuals",
"Scientific-Articles",
"Science",
"Laws",
"Law",
"Regulations",
"Patents",
"Government-Tenders",
"object-detection",
"image-segmentation",
"token-classification",
"arxiv:2206.01062",
"region:us"
]
| [
"object-detection",
"image-segmentation",
"token-classification"
]
| 2023-01-25T17:53:26Z | ---
language:
- en
- de
- fr
- ja
annotations_creators:
- crowdsourced
license: other
pretty_name: DocLayNet base
size_categories:
- 1K<n<10K
tags:
- DocLayNet
- COCO
- PDF
- IBM
- Financial-Reports
- Finance
- Manuals
- Scientific-Articles
- Science
- Laws
- Law
- Regulations
- Patents
- Government-Tenders
- object-detection
- image-segmentation
- token-classification
task_categories:
- object-detection
- image-segmentation
- token-classification
task_ids:
- instance-segmentation
---
# Dataset Card for DocLayNet base
## About this card (01/27/2023)
### Property and license
All information from this page but the content of this paragraph "About this card (01/27/2023)" has been copied/pasted from [Dataset Card for DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet).
DocLayNet is a dataset created by Deep Search (IBM Research) published under [license CDLA-Permissive-1.0](https://huggingface.co/datasets/ds4sd/DocLayNet#licensing-information).
I do not claim any rights to the data taken from this dataset and published on this page.
### DocLayNet dataset
[DocLayNet dataset](https://github.com/DS4SD/DocLayNet) (IBM) provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories.
Until today, the dataset can be downloaded through direct links or as a dataset from Hugging Face datasets:
- direct links: [doclaynet_core.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip) (28 GiB), [doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip) (7.5 GiB)
- Hugging Face dataset library: [dataset DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet)
Paper: [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://arxiv.org/abs/2206.01062) (06/02/2022)
### Processing into a format facilitating its use by HF notebooks
These 2 options require the downloading of all the data (approximately 30GBi), which requires downloading time (about 45 mn in Google Colab) and a large space on the hard disk. These could limit experimentation for people with low resources.
Moreover, even when using the download via HF datasets library, it is necessary to download the EXTRA zip separately ([doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip), 7.5 GiB) to associate the annotated bounding boxes with the text extracted by OCR from the PDFs. This operation also requires additional code because the boundings boxes of the texts do not necessarily correspond to those annotated (a calculation of the percentage of area in common between the boundings boxes annotated and those of the texts makes it possible to make a comparison between them).
At last, in order to use Hugging Face notebooks on fine-tuning layout models like LayoutLMv3 or LiLT, DocLayNet data must be processed in a proper format.
For all these reasons, I decided to process the DocLayNet dataset:
- into 3 datasets of different sizes:
- [DocLayNet small](https://huggingface.co/datasets/pierreguillou/DocLayNet-small) (about 1% of DocLayNet) < 1.000k document images (691 train, 64 val, 49 test)
- [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) (about 10% of DocLayNet) < 10.000k document images (6910 train, 648 val, 499 test)
- [DocLayNet large](https://huggingface.co/datasets/pierreguillou/DocLayNet-large) (about 100% of DocLayNet) < 100.000k document images (69.103 train, 6.480 val, 4.994 test)
- with associated texts and PDFs (base64 format),
- and in a format facilitating their use by HF notebooks.
*Note: the layout HF notebooks will greatly help participants of the IBM [ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents](https://ds4sd.github.io/icdar23-doclaynet/)!*
### About PDFs languages
Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062):
"We did not control the document selection with regard to language. **The vast majority of documents contained in DocLayNet (close to 95%) are published in English language.** However, **DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%)**. While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."
### About PDFs categories distribution
Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062):
"The pages in DocLayNet can be grouped into **six distinct categories**, namely **Financial Reports, Manuals, Scientific Articles, Laws & Regulations, Patents and Government Tenders**. Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories (Financial Reports and Manuals) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."
### Download & overview
The size of the DocLayNet small is about 10% of the DocLayNet dataset (random selection respectively in the train, val and test files).
```
# !pip install -q datasets
from datasets import load_dataset
dataset_base = load_dataset("pierreguillou/DocLayNet-base")
# overview of dataset_base
DatasetDict({
train: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 6910
})
validation: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 648
})
test: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 499
})
})
```
### Annotated bounding boxes
The DocLayNet base makes easy to display document image with the annotaed bounding boxes of paragraphes or lines.
Check the notebook [processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb](https://github.com/piegu/language-models/blob/master/processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb) in order to get the code.
#### Paragraphes
#### Lines
### HF notebooks
- [notebooks LayoutLM](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLM) (Niels Rogge)
- [notebooks LayoutLMv2](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv2) (Niels Rogge)
- [notebooks LayoutLMv3](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3) (Niels Rogge)
- [notebooks LiLT](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT) (Niels Rogge)
- [Document AI: Fine-tuning LiLT for document-understanding using Hugging Face Transformers](https://github.com/philschmid/document-ai-transformers/blob/main/training/lilt_funsd.ipynb) ([post](https://www.philschmid.de/fine-tuning-lilt#3-fine-tune-and-evaluate-lilt) of Phil Schmid)
## 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)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/
- **Repository:** https://github.com/DS4SD/DocLayNet
- **Paper:** https://doi.org/10.1145/3534678.3539043
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail.
4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.
### Supported Tasks and Leaderboards
We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/.
## Dataset Structure
### Data Fields
DocLayNet provides four types of data assets:
1. PNG images of all pages, resized to square `1025 x 1025px`
2. Bounding-box annotations in COCO format for each PNG image
3. Extra: Single-page PDF files matching each PNG image
4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content
The COCO image record are defined like this example
```js
...
{
"id": 1,
"width": 1025,
"height": 1025,
"file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",
// Custom fields:
"doc_category": "financial_reports" // high-level document category
"collection": "ann_reports_00_04_fancy", // sub-collection name
"doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
"page_no": 9, // page number in original document
"precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
},
...
```
The `doc_category` field uses one of the following constants:
```
financial_reports,
scientific_articles,
laws_and_regulations,
government_tenders,
manuals,
patents
```
### Data Splits
The dataset provides three splits
- `train`
- `val`
- `test`
## Dataset Creation
### Annotations
#### Annotation process
The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf).
#### Who are the annotators?
Annotations are crowdsourced.
## Additional Information
### Dataset Curators
The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
You can contact us at [[email protected]](mailto:[email protected]).
Curators:
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
### Licensing Information
License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)
### Citation Information
```bib
@article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
doi = {10.1145/3534678.353904},
url = {https://doi.org/10.1145/3534678.3539043},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {3743–3751},
numpages = {9},
location = {Washington DC, USA},
series = {KDD '22}
}
```
### Contributions
Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset. | [
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|
open-llm-leaderboard/details_Austism__chronos-hermes-13b-v2 | open-llm-leaderboard | 2023-09-23T05:49:08Z | 284 | 0 | [
"region:us"
]
| null | 2023-08-18T11:42:33Z | ---
pretty_name: Evaluation run of Austism/chronos-hermes-13b-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Austism/chronos-hermes-13b-v2](https://huggingface.co/Austism/chronos-hermes-13b-v2)\
\ 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_Austism__chronos-hermes-13b-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T05:48:56.060288](https://huggingface.co/datasets/open-llm-leaderboard/details_Austism__chronos-hermes-13b-v2/blob/main/results_2023-09-23T05-48-56.060288.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\": 0.021707214765100673,\n\
\ \"em_stderr\": 0.001492368687400628,\n \"f1\": 0.08727139261744986,\n\
\ \"f1_stderr\": 0.002006428881176311,\n \"acc\": 0.4356311405222559,\n\
\ \"acc_stderr\": 0.010489348281963759\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.021707214765100673,\n \"em_stderr\": 0.001492368687400628,\n\
\ \"f1\": 0.08727139261744986,\n \"f1_stderr\": 0.002006428881176311\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11751326762699014,\n \
\ \"acc_stderr\": 0.008870331256489995\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7537490134175217,\n \"acc_stderr\": 0.012108365307437523\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Austism/chronos-hermes-13b-v2
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_09T10_24_43.363551
path:
- '**/details_harness|arc:challenge|25_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T05_48_56.060288
path:
- '**/details_harness|drop|3_2023-09-23T05-48-56.060288.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T05-48-56.060288.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T05_48_56.060288
path:
- '**/details_harness|gsm8k|5_2023-09-23T05-48-56.060288.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T05-48-56.060288.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hellaswag|10_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:24:43.363551.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T10:24:43.363551.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T10:24:43.363551.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T05_48_56.060288
path:
- '**/details_harness|winogrande|5_2023-09-23T05-48-56.060288.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T05-48-56.060288.parquet'
- config_name: results
data_files:
- split: 2023_08_09T10_24_43.363551
path:
- results_2023-08-09T10:24:43.363551.parquet
- split: 2023_09_23T05_48_56.060288
path:
- results_2023-09-23T05-48-56.060288.parquet
- split: latest
path:
- results_2023-09-23T05-48-56.060288.parquet
---
# Dataset Card for Evaluation run of Austism/chronos-hermes-13b-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Austism/chronos-hermes-13b-v2
- **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 [Austism/chronos-hermes-13b-v2](https://huggingface.co/Austism/chronos-hermes-13b-v2) 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_Austism__chronos-hermes-13b-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T05:48:56.060288](https://huggingface.co/datasets/open-llm-leaderboard/details_Austism__chronos-hermes-13b-v2/blob/main/results_2023-09-23T05-48-56.060288.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": 0.021707214765100673,
"em_stderr": 0.001492368687400628,
"f1": 0.08727139261744986,
"f1_stderr": 0.002006428881176311,
"acc": 0.4356311405222559,
"acc_stderr": 0.010489348281963759
},
"harness|drop|3": {
"em": 0.021707214765100673,
"em_stderr": 0.001492368687400628,
"f1": 0.08727139261744986,
"f1_stderr": 0.002006428881176311
},
"harness|gsm8k|5": {
"acc": 0.11751326762699014,
"acc_stderr": 0.008870331256489995
},
"harness|winogrande|5": {
"acc": 0.7537490134175217,
"acc_stderr": 0.012108365307437523
}
}
```
### 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_lloorree__kssht-dahj-70b | open-llm-leaderboard | 2023-09-18T23:52:21Z | 284 | 0 | [
"region:us"
]
| null | 2023-09-18T23:51:21Z | ---
pretty_name: Evaluation run of lloorree/kssht-dahj-70b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [lloorree/kssht-dahj-70b](https://huggingface.co/lloorree/kssht-dahj-70b) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 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_lloorree__kssht-dahj-70b\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-18T23:50:58.093131](https://huggingface.co/datasets/open-llm-leaderboard/details_lloorree__kssht-dahj-70b/blob/main/results_2023-09-18T23-50-58.093131.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\": 0.7033014017574061,\n\
\ \"acc_stderr\": 0.03081446175839962,\n \"acc_norm\": 0.7072547203046122,\n\
\ \"acc_norm_stderr\": 0.03078306684205309,\n \"mc1\": 0.42962056303549573,\n\
\ \"mc1_stderr\": 0.017329234580409098,\n \"mc2\": 0.5891645864509103,\n\
\ \"mc2_stderr\": 0.015115214729699759\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6612627986348123,\n \"acc_stderr\": 0.013830568927974332,\n\
\ \"acc_norm\": 0.7081911262798635,\n \"acc_norm_stderr\": 0.013284525292403515\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6867157936666003,\n\
\ \"acc_stderr\": 0.0046288092584835265,\n \"acc_norm\": 0.8730332603067118,\n\
\ \"acc_norm_stderr\": 0.003322552829608905\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n\
\ \"acc_stderr\": 0.04094376269996793,\n \"acc_norm\": 0.6592592592592592,\n\
\ \"acc_norm_stderr\": 0.04094376269996793\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8289473684210527,\n \"acc_stderr\": 0.030643607071677098,\n\
\ \"acc_norm\": 0.8289473684210527,\n \"acc_norm_stderr\": 0.030643607071677098\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n\
\ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7283018867924528,\n \"acc_stderr\": 0.027377706624670713,\n\
\ \"acc_norm\": 0.7283018867924528,\n \"acc_norm_stderr\": 0.027377706624670713\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n\
\ \"acc_stderr\": 0.032166008088022675,\n \"acc_norm\": 0.8194444444444444,\n\
\ \"acc_norm_stderr\": 0.032166008088022675\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\
\ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\
\ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\
\ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7021276595744681,\n \"acc_stderr\": 0.029896145682095455,\n\
\ \"acc_norm\": 0.7021276595744681,\n \"acc_norm_stderr\": 0.029896145682095455\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.0470070803355104,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.0470070803355104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.03996629574876719,\n\
\ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.03996629574876719\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4470899470899471,\n \"acc_stderr\": 0.025606723995777025,\n \"\
acc_norm\": 0.4470899470899471,\n \"acc_norm_stderr\": 0.025606723995777025\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8193548387096774,\n\
\ \"acc_stderr\": 0.021886178567172534,\n \"acc_norm\": 0.8193548387096774,\n\
\ \"acc_norm_stderr\": 0.021886178567172534\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.541871921182266,\n \"acc_stderr\": 0.03505630140785741,\n\
\ \"acc_norm\": 0.541871921182266,\n \"acc_norm_stderr\": 0.03505630140785741\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\"\
: 0.81,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8424242424242424,\n \"acc_stderr\": 0.02845038880528437,\n\
\ \"acc_norm\": 0.8424242424242424,\n \"acc_norm_stderr\": 0.02845038880528437\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"\
acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9326424870466321,\n \"acc_stderr\": 0.018088393839078912,\n\
\ \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.018088393839078912\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7282051282051282,\n \"acc_stderr\": 0.02255655101013236,\n \
\ \"acc_norm\": 0.7282051282051282,\n \"acc_norm_stderr\": 0.02255655101013236\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \
\ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7815126050420168,\n \"acc_stderr\": 0.02684151432295894,\n \
\ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.02684151432295894\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.46357615894039733,\n \"acc_stderr\": 0.04071636065944215,\n \"\
acc_norm\": 0.46357615894039733,\n \"acc_norm_stderr\": 0.04071636065944215\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.908256880733945,\n \"acc_stderr\": 0.012376323409137103,\n \"\
acc_norm\": 0.908256880733945,\n \"acc_norm_stderr\": 0.012376323409137103\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5833333333333334,\n \"acc_stderr\": 0.03362277436608043,\n \"\
acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.03362277436608043\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"\
acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065498,\n \
\ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065498\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7982062780269058,\n\
\ \"acc_stderr\": 0.02693611191280227,\n \"acc_norm\": 0.7982062780269058,\n\
\ \"acc_norm_stderr\": 0.02693611191280227\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n\
\ \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035202,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035202\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n\
\ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\
\ \"acc_norm_stderr\": 0.036809181416738807\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8159509202453987,\n \"acc_stderr\": 0.03044677768797173,\n\
\ \"acc_norm\": 0.8159509202453987,\n \"acc_norm_stderr\": 0.03044677768797173\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8931623931623932,\n\
\ \"acc_stderr\": 0.02023714900899093,\n \"acc_norm\": 0.8931623931623932,\n\
\ \"acc_norm_stderr\": 0.02023714900899093\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8735632183908046,\n\
\ \"acc_stderr\": 0.011884488905895538,\n \"acc_norm\": 0.8735632183908046,\n\
\ \"acc_norm_stderr\": 0.011884488905895538\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7832369942196532,\n \"acc_stderr\": 0.022183477668412856,\n\
\ \"acc_norm\": 0.7832369942196532,\n \"acc_norm_stderr\": 0.022183477668412856\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6458100558659218,\n\
\ \"acc_stderr\": 0.015995644947299225,\n \"acc_norm\": 0.6458100558659218,\n\
\ \"acc_norm_stderr\": 0.015995644947299225\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7679738562091504,\n \"acc_stderr\": 0.024170840879340873,\n\
\ \"acc_norm\": 0.7679738562091504,\n \"acc_norm_stderr\": 0.024170840879340873\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.77491961414791,\n\
\ \"acc_stderr\": 0.023720088516179027,\n \"acc_norm\": 0.77491961414791,\n\
\ \"acc_norm_stderr\": 0.023720088516179027\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8240740740740741,\n \"acc_stderr\": 0.021185893615225184,\n\
\ \"acc_norm\": 0.8240740740740741,\n \"acc_norm_stderr\": 0.021185893615225184\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.574468085106383,\n \"acc_stderr\": 0.029494827600144366,\n \
\ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.029494827600144366\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5612777053455019,\n\
\ \"acc_stderr\": 0.012673969883493268,\n \"acc_norm\": 0.5612777053455019,\n\
\ \"acc_norm_stderr\": 0.012673969883493268\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.02667925227010314,\n\
\ \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.02667925227010314\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7696078431372549,\n \"acc_stderr\": 0.01703522925803403,\n \
\ \"acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.01703522925803403\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8122448979591836,\n \"acc_stderr\": 0.025000256039546195,\n\
\ \"acc_norm\": 0.8122448979591836,\n \"acc_norm_stderr\": 0.025000256039546195\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8955223880597015,\n\
\ \"acc_stderr\": 0.021628920516700637,\n \"acc_norm\": 0.8955223880597015,\n\
\ \"acc_norm_stderr\": 0.021628920516700637\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \
\ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276915,\n\
\ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276915\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.42962056303549573,\n\
\ \"mc1_stderr\": 0.017329234580409098,\n \"mc2\": 0.5891645864509103,\n\
\ \"mc2_stderr\": 0.015115214729699759\n }\n}\n```"
repo_url: https://huggingface.co/lloorree/kssht-dahj-70b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|arc:challenge|25_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hellaswag|10_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-18T23-50-58.093131.parquet'
- config_name: results
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- results_2023-09-18T23-50-58.093131.parquet
- split: latest
path:
- results_2023-09-18T23-50-58.093131.parquet
---
# Dataset Card for Evaluation run of lloorree/kssht-dahj-70b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lloorree/kssht-dahj-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 [lloorree/kssht-dahj-70b](https://huggingface.co/lloorree/kssht-dahj-70b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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_lloorree__kssht-dahj-70b",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-18T23:50:58.093131](https://huggingface.co/datasets/open-llm-leaderboard/details_lloorree__kssht-dahj-70b/blob/main/results_2023-09-18T23-50-58.093131.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": 0.7033014017574061,
"acc_stderr": 0.03081446175839962,
"acc_norm": 0.7072547203046122,
"acc_norm_stderr": 0.03078306684205309,
"mc1": 0.42962056303549573,
"mc1_stderr": 0.017329234580409098,
"mc2": 0.5891645864509103,
"mc2_stderr": 0.015115214729699759
},
"harness|arc:challenge|25": {
"acc": 0.6612627986348123,
"acc_stderr": 0.013830568927974332,
"acc_norm": 0.7081911262798635,
"acc_norm_stderr": 0.013284525292403515
},
"harness|hellaswag|10": {
"acc": 0.6867157936666003,
"acc_stderr": 0.0046288092584835265,
"acc_norm": 0.8730332603067118,
"acc_norm_stderr": 0.003322552829608905
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6592592592592592,
"acc_stderr": 0.04094376269996793,
"acc_norm": 0.6592592592592592,
"acc_norm_stderr": 0.04094376269996793
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8289473684210527,
"acc_stderr": 0.030643607071677098,
"acc_norm": 0.8289473684210527,
"acc_norm_stderr": 0.030643607071677098
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7283018867924528,
"acc_stderr": 0.027377706624670713,
"acc_norm": 0.7283018867924528,
"acc_norm_stderr": 0.027377706624670713
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8194444444444444,
"acc_stderr": 0.032166008088022675,
"acc_norm": 0.8194444444444444,
"acc_norm_stderr": 0.032166008088022675
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6705202312138728,
"acc_stderr": 0.03583901754736412,
"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.04810840148082635,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082635
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7021276595744681,
"acc_stderr": 0.029896145682095455,
"acc_norm": 0.7021276595744681,
"acc_norm_stderr": 0.029896145682095455
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.0470070803355104,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.0470070803355104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6413793103448275,
"acc_stderr": 0.03996629574876719,
"acc_norm": 0.6413793103448275,
"acc_norm_stderr": 0.03996629574876719
},
"harness|hendrycksTest-elementary_mathematics|5": {
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```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B-v1 | open-llm-leaderboard | 2023-12-01T14:55:17Z | 284 | 0 | [
"region:us"
]
| null | 2023-09-22T18:16:15Z | ---
pretty_name: Evaluation run of AIDC-ai-business/Marcoroni-70B-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [AIDC-ai-business/Marcoroni-70B-v1](https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1)\
\ 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 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_AIDC-ai-business__Marcoroni-70B-v1_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-09T10:10:41.822023](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B-v1_public/blob/main/results_2023-11-09T10-10-41.822023.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\": 0.3132340604026846,\n\
\ \"em_stderr\": 0.004749834913438157,\n \"f1\": 0.456531040268459,\n\
\ \"f1_stderr\": 0.004364621394991152,\n \"acc\": 0.5835410217852969,\n\
\ \"acc_stderr\": 0.01171539602098445\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.3132340604026846,\n \"em_stderr\": 0.004749834913438157,\n\
\ \"f1\": 0.456531040268459,\n \"f1_stderr\": 0.004364621394991152\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.332827899924185,\n \
\ \"acc_stderr\": 0.012979892496598271\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.8342541436464088,\n \"acc_stderr\": 0.010450899545370628\n\
\ }\n}\n```"
repo_url: https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1
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_11_09T10_10_41.822023
path:
- '**/details_harness|drop|3_2023-11-09T10-10-41.822023.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-09T10-10-41.822023.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_09T10_10_41.822023
path:
- '**/details_harness|gsm8k|5_2023-11-09T10-10-41.822023.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-09T10-10-41.822023.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_09T10_10_41.822023
path:
- '**/details_harness|winogrande|5_2023-11-09T10-10-41.822023.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-09T10-10-41.822023.parquet'
- config_name: results
data_files:
- split: 2023_11_09T10_10_41.822023
path:
- results_2023-11-09T10-10-41.822023.parquet
- split: latest
path:
- results_2023-11-09T10-10-41.822023.parquet
---
# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [AIDC-ai-business/Marcoroni-70B-v1](https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1) 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 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_AIDC-ai-business__Marcoroni-70B-v1_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T10:10:41.822023](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B-v1_public/blob/main/results_2023-11-09T10-10-41.822023.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": 0.3132340604026846,
"em_stderr": 0.004749834913438157,
"f1": 0.456531040268459,
"f1_stderr": 0.004364621394991152,
"acc": 0.5835410217852969,
"acc_stderr": 0.01171539602098445
},
"harness|drop|3": {
"em": 0.3132340604026846,
"em_stderr": 0.004749834913438157,
"f1": 0.456531040268459,
"f1_stderr": 0.004364621394991152
},
"harness|gsm8k|5": {
"acc": 0.332827899924185,
"acc_stderr": 0.012979892496598271
},
"harness|winogrande|5": {
"acc": 0.8342541436464088,
"acc_stderr": 0.010450899545370628
}
}
```
### 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
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### Citation Information
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### Contributions
[More Information Needed] | [
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|
wiki_qa_ar | null | 2023-01-25T15:02:18Z | 283 | 2 | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"license:unknown",
"region:us"
]
| [
"question-answering"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: wikiqaar
pretty_name: English-Arabic Wikipedia Question-Answering
dataset_info:
features:
- name: question_id
dtype: string
- name: question
dtype: string
- name: document_id
dtype: string
- name: answer_id
dtype: string
- name: answer
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
config_name: plain_text
splits:
- name: test
num_bytes: 7563127
num_examples: 20632
- name: validation
num_bytes: 3740721
num_examples: 10387
- name: train
num_bytes: 26009979
num_examples: 70264
download_size: 35226436
dataset_size: 37313827
---
# Dataset Card for WikiQAar
## 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:** [WikiQaAr](https://github.com/qcri/WikiQAar)
- **Repository:** [WikiQaAr](https://github.com/qcri/WikiQAar)
- **Paper:**
- **Point of Contact:** [Ines Abbes
]([email protected])
### Dataset Summary
Arabic Version of WikiQA by automatic automatic machine translators
and crowdsourced the selection of the best one to be incorporated into the corpus
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
Each data point contains the question and whether the answer is a valid or not.
### Data Fields
- `question_id`: the question id.
- `question`: the question text.
- `document_id`: the wikipedia document id.
- `answer_id` : the answer id.
- `answer` : a candidate answer to the question.
- `label` : 1 if the `answer` is correct or 0 otherwise.
### Data Splits
The dataset is not split.
| | train | validation | test |
|------------|-------:|-----------:|-------:|
| Data split | 70,264 | 20,632 | 10,387 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
Translation of WikiQA.
#### Who are the source language producers?
Translation of WikiQA.
### 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
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@InProceedings{YangYihMeek:EMNLP2015:WikiQA,
author = {{Yi}, Yang and {Wen-tau}, Yih and {Christopher} Meek},
title = "{WikiQA: A Challenge Dataset for Open-Domain Question Answering}",
journal = {Association for Computational Linguistics},
year = 2015,
doi = {10.18653/v1/D15-1237},
pages = {2013–2018},
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. | [
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|
eugenesiow/PIRM | eugenesiow | 2022-10-21T04:01:16Z | 283 | 0 | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"license:cc-by-nc-sa-4.0",
"other-image-super-resolution",
"arxiv:1809.07517",
"region:us"
]
| [
"other"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: PIRM
tags:
- other-image-super-resolution
---
# Dataset Card for PIRM
## 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://github.com/roimehrez/PIRM2018
- **Repository**: https://huggingface.co/datasets/eugenesiow/PIRM
- **Paper**: https://arxiv.org/abs/1809.07517
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
The PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing.
These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc.
Images vary in size, and are typically ~300K pixels in resolution.
This dataset was first used for evaluating the perceptual quality of super-resolution algorithms in The 2018 PIRM
challenge on Perceptual Super-resolution, in conjunction with ECCV 2018.
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/PIRM', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `validation` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/PIRM_valid_HR/1.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/PIRM_valid_LR_x2/1.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |validation|test|
|-------|---:|---:|
|bicubic_x2|100|100|
|bicubic_x3|100|100|
|bicubic_x4|100|100|
|unknown_x4|100|100|
## 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
No annotations.
#### Who are the annotators?
No annotators.
### 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
- **Original Authors**: [Blau et al. (2018)](https://arxiv.org/abs/1809.07517)
### Licensing Information
This dataset is published under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
```bibtex
@misc{blau20192018,
title={The 2018 PIRM Challenge on Perceptual Image Super-resolution},
author={Yochai Blau and Roey Mechrez and Radu Timofte and Tomer Michaeli and Lihi Zelnik-Manor},
year={2019},
eprint={1809.07517},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
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|
rcds/swiss_law_area_prediction | rcds | 2023-07-20T07:38:52Z | 283 | 3 | [
"task_categories:text-classification",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"language:fr",
"language:it",
"license:cc-by-sa-4.0",
"arxiv:2306.09237",
"region:us"
]
| [
"text-classification"
]
| 2023-03-25T10:51:36Z | ---
license: cc-by-sa-4.0
annotations_creators:
- machine-generated
language:
- de
- fr
- it
language_creators:
- expert-generated
multilinguality:
- multilingual
pretty_name: Law Area Prediction
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
---
# Dataset Card for Law Area Prediction
## 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:**
### Dataset Summary
The dataset contains cases to be classified into the four main areas of law: Public, Civil, Criminal and Social
These can be classified further into sub-areas:
```
"public": ['Tax', 'Urban Planning and Environmental', 'Expropriation', 'Public Administration', 'Other Fiscal'],
"civil": ['Rental and Lease', 'Employment Contract', 'Bankruptcy', 'Family', 'Competition and Antitrust', 'Intellectual Property'],
'criminal': ['Substantive Criminal', 'Criminal Procedure']
```
### Supported Tasks and Leaderboards
Law Area Prediction can be used as text classification task
### Languages
Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.
| Language | Subset | Number of Documents|
|------------|------------|--------------------|
| German | **de** | 127K |
| French | **fr** | 156K |
| Italian | **it** | 46K |
## Dataset Structure
- decision_id: unique identifier for the decision
- facts: facts section of the decision
- considerations: considerations section of the decision
- law_area: label of the decision (main area of law)
- law_sub_area: sub area of law of the decision
- language: language of the decision
- year: year of the decision
- court: court of the decision
- chamber: chamber of the decision
- canton: canton of the decision
- region: region of the decision
### Data Fields
[More Information Needed]
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
The dataset was split date-stratisfied
- Train: 2002-2015
- Validation: 2016-2017
- Test: 2018-2022
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## 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
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, 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://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
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|
Kotokin/artisticBIG | Kotokin | 2023-11-14T09:48:19Z | 283 | 0 | [
"license:unknown",
"region:us"
]
| null | 2023-11-14T09:44:42Z | ---
license: unknown
---
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|
tashkeela | null | 2022-11-03T16:07:53Z | 282 | 1 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:ar",
"license:gpl-2.0",
"diacritics-prediction",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
license:
- gpl-2.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: Tashkeela
tags:
- diacritics-prediction
dataset_info:
features:
- name: text
dtype: string
- name: book
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 1081110249
num_examples: 97
download_size: 183393530
dataset_size: 1081110249
---
# Dataset Card for Tashkeela
## 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:** [Tashkeela](https://sourceforge.net/projects/tashkeela/)
- **Repository:** [Tashkeela](https://sourceforge.net/projects/tashkeela/)
- **Paper:** [Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems](https://www.sciencedirect.com/science/article/pii/S2352340917300112)
- **Point of Contact:** [Taha Zerrouki](mailto:[email protected])
### Dataset Summary
It contains 75 million of fully vocalized words mainly
97 books from classical and modern Arabic language.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
```
{'book': 'zip://Tashkeela-arabic-diacritized-text-utf8-0.3/texts.txt/msa/al-kalema.org/أشكال-التجارب-في-مَثَل-الزارع.htm.txt::https://sourceforge.net/projects/tashkeela/files/latest/download',
'text': 'الكلمة\n\n\nصفحه اصلی\nاشترك\nالكتاب المقدس\nجميع المقالات\nالترتيب بالموضوع\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nهذا المقال على نسخة PDF\n\n\nأشكال التجارب في مَثَل الزارع\n\n\tقد رأينا في مقال " \nوسائل واشكال التجارب" الأشكال التي من الممكن أن تتخذها التجارب (وخاصة الاختبارات التي تأتي من خلال الآلام والاضطهاد وأشراك إطاعة شهوات الإنسان العتيق، الجسد)، نستطيع أيضاً أن نرى هذه الأقسام عاملة في مثال الزارع. هناك مجموعتين في مثال الزارع أنه برغم من سماعهم واستقبالهم للكلمة، إلا أنهم لم يجلبوا ثماراً. والسؤال هو لماذا؟\n\n1. التجارب في القسم الثاني من مثال الزارع\n\nفيما يخص القسم الثاني من مثال الزارع، تخبرنا عنها متى 13: 20- 21 ولوقا 8: 13 \nمتى 13: 20- 21\n" وَالْمَزْرُوعُ عَلَى الأَمَاكِنِ الْمُحْجِرَةِ هُوَ الَّذِي يَسْمَعُ الْكَلِمَةَ، وَحَالاً يَقْبَلُهَا بِفَرَحٍ، وَلكِنْ لَيْسَ لَهُ أَصْلٌ فِي ذَاتِهِ، بَلْ هُوَ إِلَى حِينٍ. فَإِذَا حَدَثَ ضِيقٌ أَوِ اضْطِهَادٌ مِنْ أَجْلِ الْكَلِمَةِ فَحَالاً يَعْثُرُ."\nلوقا 8: 13\n" وَالَّذِينَ عَلَى الصَّخْرِ هُمُ الَّذِينَ مَتَى سَمِعُوا يَقْبَلُونَ الْكَلِمَةَ بِفَرَحٍ، وَهؤُلاَءِ لَيْسَ لَهُمْ أَصْلٌ، فَيُؤْمِنُونَ إِلَى حِينٍ، وَفِي وَقْتِ التَّجْرِبَةِ يَرْتَدُّونَ."\n\nكما نرى، الناس في هذا القسم سمعوا الكلمة وحالاً قبلوها بفرح! بمعنى آخر، لقد كانوا متحمسين جداً تجاه الكلمة. ثم جاءت التجارب والاختبارات في شكل ضيق واضطهاد من أجل الكلمة، أي أنه بسبب الكلمة، اضطهد هؤلاء الناس. وعندئذ توقفوا. عوضاً عن أن يحفظوا ويتمسكوا بالكلمة التي قد حدث واستقبلوها بفرح، تراجعوا وسقطوا بعيداً، إن كنت مؤمناً صغيراً مليء بالحماسة تجاه الله، وبالرغم من أنه قد يبدو أنه لا يوجد شيطان من حولك، فهذا لن يستمر إلى الأبد. فالتجارب والاختبارات آتية. ستحتاج إلى أن تحفظ وتتمسك بالإيمان وبالكلمة التي قد حدث واستقبلتها بفرح. كما تقول لنا الكلمة:\nعبرانيين 10: 35- 39\n" فَلاَ تَطْرَحُوا ثِقَتَكُمُ الَّتِي لَهَا مُجَازَاةٌ عَظِيمَةٌ. لأَنَّكُمْ تَحْتَاجُونَ إِلَى الصَّبْرِ، حَتَّى إِذَا صَنَعْتُمْ مَشِيئَةَ اللهِ تَنَالُونَ الْمَوْعِدَ. لأَنَّهُ بَعْدَ قَلِيل جِدًّا «سَيَأْتِي الآتِي وَلاَ يُبْطِئُ. أَمَّا الْبَارُّ فَبِالإِيمَانِ يَحْيَا، وَإِنِ ارْتَدَّ لاَ تُسَرُّ بِهِ نَفْسِي». وَأَمَّا نَحْنُ فَلَسْنَا مِنَ الارْتِدَادِ لِلْهَلاَكِ، بَلْ مِنَ الإِيمَانِ لاقْتِنَاءِ النَّفْسِ."\n\nوالضيق قد يأخذ أشكالاً عديدة. رأيت أناساً يسقطون، تاركين الإيمان لأن آبائهم أو أقاربهم وأصدقائهم قد عارضوهم ورفضوهم بسبب إيمانهم. بالطبع قد يأخذ الاضطهاد أشكالاً أكثر من ذلك أيضاً، مثل أن تلقى في سجن أو أن تعذب لأجل إيمانك. قد يسبب الموت كذلك، كما حدث مع اسطفانوس ويعقوب أخو يوحنا. وتقول الكلمة من أجلك ومن أجل كل الذين حوكموا:\nرومية 16: 19- 20\n" لأَنَّ طَاعَتَكُمْ ذَاعَتْ إِلَى الْجَمِيعِ، فَأَفْرَحُ أَنَا بِكُمْ، وَأُرِيدُ أَنْ تَكُونُوا حُكَمَاءَ لِلْخَيْرِ وَبُسَطَاءَ لِلشَّرِّ. وَإِلهُ السَّلاَمِ سَيَسْحَقُ الشَّيْطَانَ تَحْتَ أَرْجُلِكُمْ سَرِيعًا."\nو بطرس الأولى 5: 8- 10\n" اُصْحُوا وَاسْهَرُوا. لأَنَّ إِبْلِيسَ خَصْمَكُمْ كَأَسَدٍ زَائِرٍ، يَجُولُ مُلْتَمِسًا مَنْ يَبْتَلِعُهُ هُوَ. فَقَاوِمُوهُ، رَاسِخِينَ فِي الإِيمَانِ، عَالِمِينَ أَنَّ نَفْسَ هذِهِ الآلاَمِ تُجْرَى عَلَى إِخْوَتِكُمُ الَّذِينَ فِي الْعَالَمِ. وَإِلهُ كُلِّ نِعْمَةٍ الَّذِي دَعَانَا إِلَى مَجْدِهِ الأَبَدِيِّ فِي الْمَسِيحِ يَسُوعَ، بَعْدَمَا تَأَلَّمْتُمْ يَسِيرًا، هُوَ يُكَمِّلُكُمْ، وَيُثَبِّتُكُمْ، وَيُقَوِّيكُمْ، وَيُمَكِّنُكُمْ."\n\nتمسك بالإيمان حتى النهاية. ضع حياتك ووضعك بين يدي الله وكن مستعداً لمواجهة أي شيء قد يحدث، أجل وحتى السخرية والعذاب. الله معك، سيقويك وسيعينك تماماً مثلما فعل مع يسوع في بستان جسثيماني. وتماماً مثلما فعل مع بولس في السجن عندما اضطهد من قِبَل اليهود (أعمال الرسل 23: 11). وكما قال بولس في كورنثوس الثانية 1: 7:" عَالِمِينَ أَنَّكُمْ كَمَا أَنْتُمْ شُرَكَاءُ فِي الآلاَمِ، كَذلِكَ فِي التَّعْزِيَةِ أَيْضًا." فالعزاء الآتي من الله يوازن أي سخرية أو أي عذاب قد يأتي إلينا من أي إنسان.\n\n2. التجارب في القسم الثالث من مثال الزارع\n\nبخصوص القسم الثالث من مثال الزارع، فنقرأ عنه في مرقس 4: 18- 19\n\n" وَهؤُلاَءِ هُمُ الَّذِينَ زُرِعُوا بَيْنَ الشَّوْكِ: هؤُلاَءِ هُمُ الَّذِينَ يَسْمَعُونَ الْكَلِمَةَ، وَهُمُومُ هذَا الْعَالَمِ وَغُرُورُ الْغِنَى وَشَهَوَاتُ سَائِرِ الأَشْيَاءِ تَدْخُلُ وَتَخْنُقُ الْكَلِمَةَ فَتَصِيرُ بِلاَ ثَمَرٍ."\nو لوقا 8: 14\n" وَالَّذِي سَقَطَ بَيْنَ الشَّوْكِ هُمُ الَّذِينَ يَسْمَعُونَ، ثُمَّ يَذْهَبُونَ فَيَخْتَنِقُونَ مِنْ هُمُومِ الْحَيَاةِ وَغِنَاهَا وَلَذَّاتِهَا، وَلاَ يُنْضِجُونَ ثَمَرًا."\n\nهؤلاء قد سمعوا الكلمة وفهموها ولكنهم صاروا بلا ثمر، وما هو السبب؟ السبب هو لأنهم تركوا أبواب قلوبهم مفتوحة لأشواك " وَهُمُومُ هذَا الْعَالَمِ وَغُرُورُ الْغِنَى وَشَهَوَاتُ سَائِرِ الأَشْيَاءِ" (مرقس 4: 19)، والتي تدخل فتخنق الكلمة، كما رأينا يعقوب دائماً ما يقول:\nيعقوب 1: 13- 15\n" لاَ يَقُلْ أَحَدٌ إِذَا جُرِّبَ: «إِنِّي أُجَرَّبُ مِنْ قِبَلِ اللهِ»، لأَنَّ اللهَ غَيْرُ مُجَرَّبٍ بِالشُّرُورِ، وَهُوَ لاَ يُجَرِّبُ أَحَدًا. وَلكِنَّ كُلَّ وَاحِدٍ يُجَرَّبُ إِذَا انْجَذَبَ وَانْخَدَعَ مِنْ شَهْوَتِهِ. ثُمَّ الشَّهْوَةُ إِذَا حَبِلَتْ تَلِدُ خَطِيَّةً، وَالْخَطِيَّةُ إِذَا كَمَلَتْ تُنْتِجُ مَوْتًا."\nوتيموثاوس الأولى 6: 9 تقول لنا\n" وَأَمَّا الَّذِينَ يُرِيدُونَ أَنْ يَكُونُوا أَغْنِيَاءَ، فَيَسْقُطُونَ فِي تَجْرِبَةٍ وَفَخٍّ وَشَهَوَاتٍ كَثِيرَةٍ غَبِيَّةٍ وَمُضِرَّةٍ، تُغَرِّقُ النَّاسَ فِي الْعَطَبِ وَالْهَلاَكِ."\n\nيجب أن نلاحظ شيئاً هنا: أن تأثير هموم الحياة هو نفس التأثير الذي لتجارب الغنى وشهوات الأشياء الأخرى. فهموم الحياة أيضاً لا تجلب الثمار، إذاً فإن اردت أن تكون مسيحياً مثمراً، أي مسيحي حقيقي وليس فقط مسيحي اسمي، فيجب عليك أن تزيل أشواك الهموم والغنى وملذات الحياة وأن تمنعهم من العودة مرة أخرى. تحتاج إلى أن تفعل شيئاً، تحتاج إلى أن تتغير والله سيعينك في هذا إن كنت حقاً تريده. التجارب في القسم الثالث من مثال الزارع لا تأتي من خلال الاضطهاد والآلام عن طريق الشيطان. ولكن هنا تأخذ التجارب صوراً أكثر مكراً والتي مع هذا تتطلب مقاومتنا. الاهتمام بما يهتم به هذا العالم ("هموم هذا العالم")، الرغبة في الغنى أو اشتهاء الأشياء الأخرى هي أمور خطيرة جداً. إنها أشواك يجب إزالتها. كما رأينا بولس يقول:\nرومية 13: 14\n" بَلِ الْبَسُوا الرَّبَّ يَسُوعَ الْمَسِيحَ، وَلاَ تَصْنَعُوا تَدْبِيرًا لِلْجَسَدِ لأَجْلِ الشَّهَوَاتِ."\n\n" لاَ تَصْنَعُوا تَدْبِيرًا لِلْجَسَدِ" والتي تعني أنه يجب علينا أن لا نهتم بالجسد وشهواته. ولكن عوضاً عن ذلك ينبغي لنا أن نطعم أنفسنا بلبن الكلمة الصافي الذي ننمو بواستطه (بطرس الأولى 2: 2).\n\n\nتاسوس كيولاشوجلو'}
```
### Data Fields
- `book` (str): Book filename.
- `text` (str): Text of the book.
### Data Splits
The dataset is not split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
The Modern Standard Arabic texts crawled from the Internet.
#### Who are the source language producers?
Websites.
### 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
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[GNU General Public License, version 2 (GPLv2)](https://opensource.org/licenses/GPL-2.0).
### Citation Information
The dataset was published on this [paper](https://www.sciencedirect.com/science/article/pii/S2352340917300112#!):
```
@article{zerrouki2017tashkeela,
title={Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems},
author={Zerrouki, Taha and Balla, Amar},
journal={Data in brief},
volume={11},
pages={147},
year={2017},
publisher={Elsevier}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. | [
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|
CALM/arwiki | CALM | 2022-08-01T16:37:23Z | 282 | 1 | [
"multilinguality:monolingual",
"language:ar",
"license:unknown",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
pretty_name: Wikipedia Arabic dumps dataset.
language:
- ar
license:
- unknown
multilinguality:
- monolingual
---
# Arabic Wiki Dataset
## Dataset Summary
This dataset is extracted using [`wikiextractor`](https://github.com/attardi/wikiextractor) tool, from [Wikipedia Arabic pages](https://dumps.wikimedia.org/arwiki/).
## Supported Tasks and Leaderboards
Intended to train **Arabic** language models on MSA (Modern Standard Arabic).
## Dataset Structure
The dataset is structured into 2 folders:
- `arwiki_20211213_txt`: dataset is divided into subfolders each of which contains no more than 100 documents.
- `arwiki_20211213_txt_single`: all documents merged together in a single txt file.
## Dataset Statistics
#### Extracts from **December 13, 2021**:
| documents | vocabulary | words |
| --- | --- | --- |
| 1,136,455 | 5,446,560 | 175,566,016 |
## Usage
Load all dataset from the single txt file:
```python
load_dataset('CALM/arwiki',
data_files='arwiki_2021_txt_single/arwiki_20211213.txt')
# OR with stream
load_dataset('CALM/arwiki',
data_files='arwiki_2021_txt_single/arwiki_20211213.txt',
streaming=True)
```
Load a smaller subset from the individual txt files:
```python
load_dataset('CALM/arwiki',
data_files='arwiki_2021_txt/AA/arwiki_20211213_1208.txt')
# OR with stream
load_dataset('CALM/arwiki',
data_files='arwiki_2021_txt/AA/arwiki_20211213_1208.txt',
streaming=True)
``` | [
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|
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