id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
togethercomputer/RedPajama-Data-1T | 2023-06-30T22:06:10.000Z | [
"task_categories:text-generation",
"language:en",
"region:us"
] | togethercomputer | RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. | null | null | 878 | 17,226 | ---
task_categories:
- text-generation
language:
- en
pretty_name: Red Pajama 1T
---
### Getting Started
The dataset consists of 2084 jsonl files.
You can download the dataset using HuggingFace:
```python
from datasets import load_dataset
ds = load_dataset("togethercomputer/RedPajama-Data-1T")
```
Or you can directly download the files using the following command:
```
wget 'https://data.together.xyz/redpajama-data-1T/v1.0.0/urls.txt'
while read line; do
dload_loc=${line#https://data.together.xyz/redpajama-data-1T/v1.0.0/}
mkdir -p $(dirname $dload_loc)
wget "$line" -O "$dload_loc"
done < urls.txt
```
After downloading the files, you can load the dataset from disk by setting the `RED_PAJAMA_DATA_DIR` environment variable to the directory containing the files:
```python
import os
from datasets import load_dataset
os.environ["RED_PAJAMA_DATA_DIR"] = "/path/to/download"
ds = load_dataset("togethercomputer/RedPajama-Data-1T")
```
A smaller 1B-token sample of the dataset can be found [here](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T-Sample).
A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/togethercomputer/RedPajama-Data).
### Dataset Summary
RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset.
| Dataset | Token Count |
|---------------|-------------|
| Commoncrawl | 878 Billion |
| C4 | 175 Billion |
| GitHub | 59 Billion |
| Books | 26 Billion |
| ArXiv | 28 Billion |
| Wikipedia | 24 Billion |
| StackExchange | 20 Billion |
| Total | 1.2 Trillion |
### Languages
Primarily English, though the Wikipedia slice contains multiple languages.
## Dataset Structure
The dataset structure is as follows:
```json
{
"text": ...,
"meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...},
"red_pajama_subset": "common_crawl" | "c4" | "github" | "books" | "arxiv" | "wikipedia" | "stackexchange"
}
```
## Dataset Creation
This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe.
### Source Data
#### Commoncrawl
We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline.
We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to
classify paragraphs as Wikipedia references or random Commoncrawl samples.
#### C4
C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format.
#### GitHub
The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality
files and only keep projects that are distributed under the MIT, BSD, or Apache license.
#### Wikipedia
We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains
text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other
formatting boilerplate has been removed.
#### Gutenberg and Books3
The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use
simhash to remove near duplicates.
#### ArXiv
ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and
remove preambles, comments, macros and bibliographies.
#### Stackexchange
The Stack Exchange split of the dataset is download from the
[Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites,
remove html tags, group the posts into question-answer pairs, and order answers by their score.
### SHA256 Checksums
SHA256 checksums for the dataset files for each data source are available here:
```
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/arxiv_SHA256SUMS.txt
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/book_SHA256SUMS.txt
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/c4_SHA256SUMS.txt
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/common_crawl_SHA256SUMS.txt
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/github_SHA256SUMS.txt
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/stackexchange_SHA256SUMS.txt
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/wikipedia_SHA256SUMS.txt
```
To cite RedPajama, please use:
```
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
### License
Please refer to the licenses of the data subsets you use.
* [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use/full/)
* [C4 license](https://huggingface.co/datasets/allenai/c4#license)
* GitHub was limited to MIT, BSD, or Apache licenses only
* Books: [the_pile_books3 license](https://huggingface.co/datasets/the_pile_books3#licensing-information) and [pg19 license](https://huggingface.co/datasets/pg19#licensing-information)
* [ArXiv Terms of Use](https://info.arxiv.org/help/api/tou.html)
* [Wikipedia License](https://huggingface.co/datasets/wikipedia#licensing-information)
* [StackExchange license on the Internet Archive](https://archive.org/details/stackexchange)
<!--
### 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]
--> |
yelp_review_full | 2023-01-25T15:03:32.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:1509.01626",
"region:us"
] | null | The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data.
The Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset.
It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun.
Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). | @inproceedings{zhang2015character,
title={Character-level convolutional networks for text classification},
author={Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
booktitle={Advances in neural information processing systems},
pages={649--657},
year={2015}
} | null | 33 | 16,117 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: YelpReviewFull
license_details: yelp-licence
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': 1 star
'1': 2 star
'2': 3 stars
'3': 4 stars
'4': 5 stars
- name: text
dtype: string
config_name: yelp_review_full
splits:
- name: train
num_bytes: 483811554
num_examples: 650000
- name: test
num_bytes: 37271188
num_examples: 50000
download_size: 196146755
dataset_size: 521082742
train-eval-index:
- config: yelp_review_full
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
---
# Dataset Card for YelpReviewFull
## 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:** [Yelp](https://www.yelp.com/dataset)
- **Repository:** [Crepe](https://github.com/zhangxiangxiao/Crepe)
- **Paper:** [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626)
- **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu)
### Dataset Summary
The Yelp reviews dataset consists of reviews from Yelp.
It is extracted from the Yelp Dataset Challenge 2015 data.
### Supported Tasks and Leaderboards
- `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment.
### Languages
The reviews were mainly written in english.
## Dataset Structure
### Data Instances
A typical data point, comprises of a text and the corresponding label.
An example from the YelpReviewFull test set looks as follows:
```
{
'label': 0,
'text': 'I got \'new\' tires from them and within two weeks got a flat. I took my car to a local mechanic to see if i could get the hole patched, but they said the reason I had a flat was because the previous patch had blown - WAIT, WHAT? I just got the tire and never needed to have it patched? This was supposed to be a new tire. \\nI took the tire over to Flynn\'s and they told me that someone punctured my tire, then tried to patch it. So there are resentful tire slashers? I find that very unlikely. After arguing with the guy and telling him that his logic was far fetched he said he\'d give me a new tire \\"this time\\". \\nI will never go back to Flynn\'s b/c of the way this guy treated me and the simple fact that they gave me a used tire!'
}
```
### Data Fields
- 'text': The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
- 'label': Corresponds to the score associated with the review (between 1 and 5).
### Data Splits
The Yelp reviews full star dataset is constructed by randomly taking 130,000 training samples and 10,000 testing samples for each review star from 1 to 5.
In total there are 650,000 trainig samples and 50,000 testing samples.
## Dataset Creation
### Curation Rationale
The Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the Yelp Dataset Challenge 2015. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
### 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
You can check the official [yelp-dataset-agreement](https://s3-media3.fl.yelpcdn.com/assets/srv0/engineering_pages/bea5c1e92bf3/assets/vendor/yelp-dataset-agreement.pdf).
### Citation Information
Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
### Contributions
Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset. |
fashion_mnist | 2023-04-17T14:02:05.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:1708.07747",
"region:us"
] | null | Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of
60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image,
associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in
replacement for the original MNIST dataset for benchmarking machine learning algorithms.
It shares the same image size and structure of training and testing splits. | @article{DBLP:journals/corr/abs-1708-07747,
author = {Han Xiao and
Kashif Rasul and
Roland Vollgraf},
title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
Algorithms},
journal = {CoRR},
volume = {abs/1708.07747},
year = {2017},
url = {http://arxiv.org/abs/1708.07747},
archivePrefix = {arXiv},
eprint = {1708.07747},
timestamp = {Mon, 13 Aug 2018 16:47:27 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | null | 28 | 16,091 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: fashion-mnist
pretty_name: FashionMNIST
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': T - shirt / top
'1': Trouser
'2': Pullover
'3': Dress
'4': Coat
'5': Sandal
'6': Shirt
'7': Sneaker
'8': Bag
'9': Ankle boot
config_name: fashion_mnist
splits:
- name: train
num_bytes: 31296655
num_examples: 60000
- name: test
num_bytes: 5233818
num_examples: 10000
download_size: 30878645
dataset_size: 36530473
---
# Dataset Card for FashionMNIST
## 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/zalandoresearch/fashion-mnist)
- **Repository:** [GitHub](https://github.com/zalandoresearch/fashion-mnist)
- **Paper:** [arXiv](https://arxiv.org/pdf/1708.07747.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image of Zalando's article into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-fashion-mnist).
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
A data point comprises an image and its label.
```
{
'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x27601169DD8>,
'label': 9
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the 28x28 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `label`: an integer between 0 and 9 representing the classes with the following mapping:
| Label | Description |
| --- | --- |
| 0 | T-shirt/top |
| 1 | Trouser |
| 2 | Pullover |
| 3 | Dress |
| 4 | Coat |
| 5 | Sandal |
| 6 | Shirt |
| 7 | Sneaker |
| 8 | Bag |
| 9 | Ankle boot |
### Data Splits
The data is split into training and test set. The training set contains 60,000 images and the test set 10,000 images.
## Dataset Creation
### Curation Rationale
**From the arXiv paper:**
The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. "If it doesn't work on MNIST, it won't work at all", they said. "Well, if it does work on MNIST, it may still fail on others."
Here are some good reasons:
- MNIST is too easy. Convolutional nets can achieve 99.7% on MNIST. Classic machine learning algorithms can also achieve 97% easily. Check out our side-by-side benchmark for Fashion-MNIST vs. MNIST, and read "Most pairs of MNIST digits can be distinguished pretty well by just one pixel."
- MNIST is overused. In this April 2017 Twitter thread, Google Brain research scientist and deep learning expert Ian Goodfellow calls for people to move away from MNIST.
- MNIST can not represent modern CV tasks, as noted in this April 2017 Twitter thread, deep learning expert/Keras author François Chollet.
### Source Data
#### Initial Data Collection and Normalization
**From the arXiv paper:**
Fashion-MNIST is based on the assortment on Zalando’s website. Every fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit. The original picture has a light-gray background (hexadecimal color: #fdfdfd) and stored in 762 × 1000 JPEG format. For efficiently serving different frontend components, the original picture is resampled with multiple resolutions, e.g. large, medium, small, thumbnail and tiny.
We use the front look thumbnail images of 70,000 unique products to build Fashion-MNIST. Those products come from different gender groups: men, women, kids and neutral. In particular, whitecolor products are not included in the dataset as they have low contrast to the background. The thumbnails (51 × 73) are then fed into the following conversion pipeline:
1. Converting the input to a PNG image.
2. Trimming any edges that are close to the color of the corner pixels. The “closeness” is defined by the distance within 5% of the maximum possible intensity in RGB space.
3. Resizing the longest edge of the image to 28 by subsampling the pixels, i.e. some rows and columns are skipped over.
4. Sharpening pixels using a Gaussian operator of the radius and standard deviation of 1.0, with increasing effect near outlines.
5. Extending the shortest edge to 28 and put the image to the center of the canvas.
6. Negating the intensities of the image.
7. Converting the image to 8-bit grayscale pixels.
#### Who are the source language producers?
**From the arXiv paper:**
Every fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit.
### Annotations
#### Annotation process
**From the arXiv paper:**
For the class labels, they use the silhouette code of the product. The silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando. Each product Zalando is the Europe’s largest online fashion platform. Each product contains only one silhouette code.
#### Who are the annotators?
**From the arXiv paper:**
The silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando.
### 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
Han Xiao and Kashif Rasul and Roland Vollgraf
### Licensing Information
MIT Licence
### Citation Information
```
@article{DBLP:journals/corr/abs-1708-07747,
author = {Han Xiao and
Kashif Rasul and
Roland Vollgraf},
title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
Algorithms},
journal = {CoRR},
volume = {abs/1708.07747},
year = {2017},
url = {http://arxiv.org/abs/1708.07747},
archivePrefix = {arXiv},
eprint = {1708.07747},
timestamp = {Mon, 13 Aug 2018 16:47:27 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset. |
commonsense_qa | 2023-04-05T10:02:16.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:1811.00937",
"region:us"
] | null | CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation
split, and "Question token split", see paper for details. | @inproceedings{talmor-etal-2019-commonsenseqa,
title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
author = "Talmor, Alon and
Herzig, Jonathan and
Lourie, Nicholas and
Berant, Jonathan",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1421",
doi = "10.18653/v1/N19-1421",
pages = "4149--4158",
archivePrefix = "arXiv",
eprint = "1811.00937",
primaryClass = "cs",
} | null | 23 | 16,048 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: CommonsenseQA
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: commonsenseqa
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: question_concept
dtype: string
- name: choices
sequence:
- name: label
dtype: string
- name: text
dtype: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 2209044
num_examples: 9741
- name: validation
num_bytes: 274033
num_examples: 1221
- name: test
num_bytes: 258017
num_examples: 1140
download_size: 4680691
dataset_size: 2741094
---
# Dataset Card for "commonsense_qa"
## 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.tau-nlp.org/commonsenseqa
- **Repository:** https://github.com/jonathanherzig/commonsenseqa
- **Paper:** https://arxiv.org/abs/1811.00937
- **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:** 4.68 MB
- **Size of the generated dataset:** 2.18 MB
- **Total amount of disk used:** 6.86 MB
### Dataset Summary
CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation
split, and "Question token split", see paper for details.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The dataset is in English (`en`).
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 4.68 MB
- **Size of the generated dataset:** 2.18 MB
- **Total amount of disk used:** 6.86 MB
An example of 'train' looks as follows:
```
{'id': '075e483d21c29a511267ef62bedc0461',
'question': 'The sanctions against the school were a punishing blow, and they seemed to what the efforts the school had made to change?',
'question_concept': 'punishing',
'choices': {'label': ['A', 'B', 'C', 'D', 'E'],
'text': ['ignore', 'enforce', 'authoritarian', 'yell at', 'avoid']},
'answerKey': 'A'}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id` (`str`): Unique ID.
- `question`: a `string` feature.
- `question_concept` (`str`): ConceptNet concept associated to the question.
- `choices`: a dictionary feature containing:
- `label`: a `string` feature.
- `text`: a `string` feature.
- `answerKey`: a `string` feature.
### Data Splits
| name | train | validation | test |
|---------|------:|-----------:|-----:|
| default | 9741 | 1221 | 1140 |
## 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
The dataset is licensed under the MIT License.
See: https://github.com/jonathanherzig/commonsenseqa/issues/5
### Citation Information
```
@inproceedings{talmor-etal-2019-commonsenseqa,
title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
author = "Talmor, Alon and
Herzig, Jonathan and
Lourie, Nicholas and
Berant, Jonathan",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1421",
doi = "10.18653/v1/N19-1421",
pages = "4149--4158",
archivePrefix = "arXiv",
eprint = "1811.00937",
primaryClass = "cs",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
mosaicml/dolly_hhrlhf | 2023-10-02T15:48:48.000Z | [
"task_categories:text-generation",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | mosaicml | null | null | null | 87 | 15,344 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 43781455.002688624
num_examples: 59310
- name: test
num_bytes: 4479286.805304853
num_examples: 5129
download_size: 24882010
dataset_size: 48260741.80799348
license: cc-by-sa-3.0
task_categories:
- text-generation
language:
- en
pretty_name: Dolly HH-RLHF
---
# Dataset Card for "dolly_hhrlhf"
This dataset is a combination of [Databrick's dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset and a filtered subset of [Anthropic's HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf). It also includes a test split, which was missing in the original `dolly` set. That test set is composed of 200 randomly selected samples from `dolly` + 4,929 of the test set samples from HH-RLHF which made it through the filtering process. The train set contains 59,310 samples; `15,014 - 200 = 14,814` from Dolly, and the remaining 44,496 from HH-RLHF.
It is slightly larger than Alpaca, and in our experience of slightly higher quality, but is usable for commercial purposes so long as you follow the terms of the license.
## Filtering process
As mentioned, the HH-RLHF data in this dataset is filtered. Specifically, we take the first turn of the convesation, then remove any samples where the assistant:
- uses the word "human", "thank", or "sorry"
- asks a question
- uses a first person pronoun
This leaves samples which look like instruction-following, as opposed to conversation.
## License/Attribution
<!--
**Copyright (2023) MosaicML, Inc.**
-->
This dataset was developed at MosaicML (https://www.mosaicml.com) and its use is subject to the CC BY-SA 3.0 license.
Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license:
Wikipedia (various pages) - https://www.wikipedia.org/
Copyright © Wikipedia editors and contributors.
Databricks (https://www.databricks.com)
Copyright © Databricks
When citing this dataset, please use the following:
```
@misc{mosaicml2023dolly_hhrlhf,
author = {MosaicML},
title = {Dolly-HHRLHF Dataset},
year = {2023},
publisher = {HuggingFace Datasets},
howpublished = {https://huggingface.co/datasets/mosaicml/dolly_hhrlhf},
}
``` |
Open-Orca/OpenOrca | 2023-10-02T19:01:36.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:mit",
"arxiv:2306.02707",
"arxiv:2301.13688",
"region:us"
] | Open-Orca | null | null | null | 767 | 15,297 | ---
language:
- en
license: mit
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
pretty_name: OpenOrca
size_categories:
- 10M<n<100M
---
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [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)
- [Dataset Use](#dataset-use)
- [Use Cases](#use-cases)
- [Usage Caveats](#usage-caveats)
- [Getting Started](#getting-started)
<p><h1>🐋 The OpenOrca Dataset! 🐋</h1></p>
<a name="dataset-announcement"></a>
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707).
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
# Official Models
## Mistral-7B-OpenOrca
Our [latest model](https://huggingface.co/spaces/Open-Orca/Mistral-7B-OpenOrca), the first 7B to score better overall than all previous models below 30B.
98% of Llama2-70b-chat's performance, in a completely open 7B!
## OpenOrca-Platypus2-13B
Our [third model](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!
Released in partnership with Platypus.
## LlongOrca 7B & 13B
* Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
* [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
## OpenOrcaxOpenChat-Preview2-13B
Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper.
Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.
Released in partnership with OpenChat.
## OpenOrca-Preview1-13B
[OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B)
This model was trained in less than a day, for <$200, with <10% of our data.
At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
<a name="dataset-summary"></a>
# Dataset Summary
The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688).
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-attribution"></a>
# Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan
http://AlignmentLab.ai:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:
http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx
Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
<a name="supported-tasks-and-leaderboards"></a>
# Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation.
It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.
Further information on leaderboards will be updated as they become available.
<a name="languages"></a>
# Languages
The language of the data is primarily English.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
<a name="data-splits"></a>
## Data Splits
The data is unsplit.
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers.
The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.
This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
<a name="source-data"></a>
## Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.
We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original).
These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source.
However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
<a name="dataset-use"></a>
# Dataset Use
<a name="use-cases"></a>
## Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
<a name="usage-caveats"></a>
## Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.
Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
<a name="getting-started"></a>
## Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library.
We recommend using streaming due to the large size of the files.
Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
# Citation
```bibtex
@misc{OpenOrca,
title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca},
}
```
```bibtex
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```bibtex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint= arXiv 2307.09288
}
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
``` |
hf-internal-testing/dummy_image_text_data | 2023-02-08T10:34:38.000Z | [
"region:us"
] | hf-internal-testing | null | null | null | 0 | 14,954 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1944983.0
num_examples: 20
download_size: 1690123
dataset_size: 1944983.0
---
# Dataset Card for "dummy_image_text_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
skt/kobest_v1 | 2022-08-22T09:00:17.000Z | [
"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:2204.04541",
"region:us"
] | skt | The dataset contains data for KoBEST dataset | null | null | 17 | 14,875 | ---
pretty_name: KoBEST
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
---
# Dataset Card for KoBEST
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/SKT-LSL/KoBEST_datarepo
- **Paper:**
- **Point of Contact:** https://github.com/SKT-LSL/KoBEST_datarepo/issues
### Dataset Summary
KoBEST is a Korean benchmark suite consists of 5 natural language understanding tasks that requires advanced knowledge in Korean.
### Supported Tasks and Leaderboards
Boolean Question Answering, Choice of Plausible Alternatives, Words-in-Context, HellaSwag, Sentiment Negation Recognition
### Languages
`ko-KR`
## Dataset Structure
### Data Instances
#### KB-BoolQ
An example of a data point looks as follows.
```
{'paragraph': '두아 리파(Dua Lipa, 1995년 8월 22일 ~ )는 잉글랜드의 싱어송라이터, 모델이다. BBC 사운드 오브 2016 명단에 노미닛되었다. 싱글 "Be the One"가 영국 싱글 차트 9위까지 오르는 등 성과를 보여주었다.',
'question': '두아 리파는 영국인인가?',
'label': 1}
```
#### KB-COPA
An example of a data point looks as follows.
```
{'premise': '물을 오래 끓였다.',
'question': '결과',
'alternative_1': '물의 양이 늘어났다.',
'alternative_2': '물의 양이 줄어들었다.',
'label': 1}
```
#### KB-WiC
An example of a data point looks as follows.
```
{'word': '양분',
'context_1': '토양에 [양분]이 풍부하여 나무가 잘 자란다. ',
'context_2': '태아는 모체로부터 [양분]과 산소를 공급받게 된다.',
'label': 1}
```
#### KB-HellaSwag
An example of a data point looks as follows.
```
{'context': '모자를 쓴 투수가 타자에게 온 힘을 다해 공을 던진다. 공이 타자에게 빠른 속도로 다가온다. 타자가 공을 배트로 친다. 배트에서 깡 소리가 난다. 공이 하늘 위로 날아간다.',
'ending_1': '외야수가 떨어지는 공을 글러브로 잡는다.',
'ending_2': '외야수가 공이 떨어질 위치에 자리를 잡는다.',
'ending_3': '심판이 아웃을 외친다.',
'ending_4': '외야수가 공을 따라 뛰기 시작한다.',
'label': 3}
```
#### KB-SentiNeg
An example of a data point looks as follows.
```
{'sentence': '택배사 정말 마음에 듬',
'label': 1}
```
### Data Fields
### KB-BoolQ
+ `paragraph`: a `string` feature
+ `question`: a `string` feature
+ `label`: a classification label, with possible values `False`(0) and `True`(1)
### KB-COPA
+ `premise`: a `string` feature
+ `question`: a `string` feature
+ `alternative_1`: a `string` feature
+ `alternative_2`: a `string` feature
+ `label`: an answer candidate label, with possible values `alternative_1`(0) and `alternative_2`(1)
### KB-WiC
+ `target_word`: a `string` feature
+ `context_1`: a `string` feature
+ `context_2`: a `string` feature
+ `label`: a classification label, with possible values `False`(0) and `True`(1)
### KB-HellaSwag
+ `target_word`: a `string` feature
+ `context_1`: a `string` feature
+ `context_2`: a `string` feature
+ `label`: a classification label, with possible values `False`(0) and `True`(1)
### KB-SentiNeg
+ `sentence`: a `string` feature
+ `label`: a classification label, with possible values `Negative`(0) and `Positive`(1)
### Data Splits
#### KB-BoolQ
+ train: 3,665
+ dev: 700
+ test: 1,404
#### KB-COPA
+ train: 3,076
+ dev: 1,000
+ test: 1,000
#### KB-WiC
+ train: 3,318
+ dev: 1,260
+ test: 1,260
#### KB-HellaSwag
+ train: 3,665
+ dev: 700
+ test: 1,404
#### KB-SentiNeg
+ train: 3,649
+ dev: 400
+ test: 397
+ test_originated: 397 (Corresponding training data where the test set is originated from.)
## 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
```
@misc{https://doi.org/10.48550/arxiv.2204.04541,
doi = {10.48550/ARXIV.2204.04541},
url = {https://arxiv.org/abs/2204.04541},
author = {Kim, Dohyeong and Jang, Myeongjun and Kwon, Deuk Sin and Davis, Eric},
title = {KOBEST: Korean Balanced Evaluation of Significant Tasks},
publisher = {arXiv},
year = {2022},
}
```
[More Information Needed]
### Contributions
Thanks to [@MJ-Jang](https://github.com/MJ-Jang) for adding this dataset. |
hate_speech_offensive | 2023-01-25T14:31:41.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"hate-speech-detection",
"arxiv:1703.04009",
"region:us"
] | null | An annotated dataset for hate speech and offensive language detection on tweets. | @inproceedings{hateoffensive,
title = {Automated Hate Speech Detection and the Problem of Offensive Language},
author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media},
series = {ICWSM '17},
year = {2017},
location = {Montreal, Canada},
pages = {512-515}
} | null | 6 | 14,833 | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: hate-speech-and-offensive-language
pretty_name: Hate Speech and Offensive Language
tags:
- hate-speech-detection
dataset_info:
features:
- name: count
dtype: int64
- name: hate_speech_count
dtype: int64
- name: offensive_language_count
dtype: int64
- name: neither_count
dtype: int64
- name: class
dtype:
class_label:
names:
'0': hate speech
'1': offensive language
'2': neither
- name: tweet
dtype: string
splits:
- name: train
num_bytes: 3207826
num_examples: 24783
download_size: 2546446
dataset_size: 3207826
train-eval-index:
- config: default
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
col_mapping:
tweet: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/t-davidson/hate-speech-and-offensive-language
- **Repository:** https://github.com/t-davidson/hate-speech-and-offensive-language
- **Paper:** https://arxiv.org/abs/1703.04009
- **Leaderboard:**
- **Point of Contact:** https://docs.google.com/forms/d/e/1FAIpQLSdrPNlfVBlqxun2tivzAtsZaOoPC5YYMocn-xscCgeRakLXHg/viewform?usp=pp_url&entry.1506871634&entry.147453066&entry.1390333885&entry.516829772
### Dataset Summary
An annotated dataset for hate speech and offensive language detection on tweets.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`)
## Dataset Structure
### Data Instances
```
{
"count": 3,
"hate_speech_annotation": 0,
"offensive_language_annotation": 0,
"neither_annotation": 3,
"label": 2, # "neither"
"tweet": "!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. & as a man you should always take the trash out...")
}
```
### Data Fields
```
count: (Integer) number of users who coded each tweet (min is 3, sometimes more users coded a tweet when judgments were determined to be unreliable,
hate_speech_annotation: (Integer) number of users who judged the tweet to be hate speech,
offensive_language_annotation: (Integer) number of users who judged the tweet to be offensive,
neither_annotation: (Integer) number of users who judged the tweet to be neither offensive nor non-offensive,
label: (Class Label) class label for majority of CF users (0: 'hate-speech', 1: 'offensive-language' or 2: 'neither'),
tweet: (string)
```
### Data Splits
This dataset is not splitted, only the train split 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
Usernames are not anonymized in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
MIT License
### Citation Information
@inproceedings{hateoffensive,
title = {Automated Hate Speech Detection and the Problem of Offensive Language},
author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media},
series = {ICWSM '17},
year = {2017},
location = {Montreal, Canada},
pages = {512-515}
}
### Contributions
Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset. |
HuggingFaceM4/tmp-pmd-synthetic-testing | 2022-10-05T17:16:27.000Z | [
"region:us"
] | HuggingFaceM4 | null | null | null | 1 | 14,799 | Entry not found |
wiki_qa | 2023-04-05T13:43:16.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | Wiki Question Answering corpus from Microsoft | @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},
} | null | 16 | 14,642 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: wikiqa
pretty_name: WikiQA
dataset_info:
features:
- name: question_id
dtype: string
- name: question
dtype: string
- name: document_title
dtype: string
- name: answer
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: test
num_bytes: 1337903
num_examples: 6165
- name: train
num_bytes: 4469148
num_examples: 20360
- name: validation
num_bytes: 591833
num_examples: 2733
download_size: 7094233
dataset_size: 6398884
---
# Dataset Card for "wiki_qa"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.microsoft.com/en-us/download/details.aspx?id=52419](https://www.microsoft.com/en-us/download/details.aspx?id=52419)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [WikiQA: A Challenge Dataset for Open-Domain Question Answering](https://aclanthology.org/D15-1237/)
- **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:** 7.10 MB
- **Size of the generated dataset:** 6.40 MB
- **Total amount of disk used:** 13.50 MB
### Dataset Summary
Wiki Question Answering corpus from Microsoft.
The WikiQA corpus is a publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 7.10 MB
- **Size of the generated dataset:** 6.40 MB
- **Total amount of disk used:** 13.50 MB
An example of 'train' looks as follows.
```
{
"answer": "Glacier caves are often called ice caves , but this term is properly used to describe bedrock caves that contain year-round ice.",
"document_title": "Glacier cave",
"label": 0,
"question": "how are glacier caves formed?",
"question_id": "Q1"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `question_id`: a `string` feature.
- `question`: a `string` feature.
- `document_title`: a `string` feature.
- `answer`: a `string` feature.
- `label`: a classification label, with possible values including `0` (0), `1` (1).
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|20360| 2733|6165|
## 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
MICROSOFT RESEARCH DATA LICENSE AGREEMENT
FOR
MICROSOFT RESEARCH WIKIQA CORPUS
These license terms are an agreement between Microsoft Corporation (or based on where you live, one of its
affiliates) and you. Please read them. They apply to the data associated with this license above, which includes
the media on which you received it, if any. The terms also apply to any Microsoft:
- updates,
- supplements,
- Internet-based services, and
- support services
for this data, unless other terms accompany those items. If so, those terms apply.
BY USING THE DATA, YOU ACCEPT THESE TERMS. IF YOU DO NOT ACCEPT THEM, DO NOT USE THE DATA.
If you comply with these license terms, you have the rights below.
1. SCOPE OF LICENSE.
a. You may use, copy, modify, create derivative works, and distribute the Dataset:
i. for research and technology development purposes only. Examples of research and technology
development uses are teaching, academic research, public demonstrations and experimentation ;
and
ii. to publish (or present papers or articles) on your results from using such Dataset.
b. The data is licensed, not sold. This agreement only gives you some rights to use the data. Microsoft reserves
all other rights. Unless applicable law gives you more rights despite this limitation, you may use the data only
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You may not
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- transfer the data or this agreement to any third party; or
- use the data directly in a commercial product without Microsoft’s permission.
2. DISTRIBUTION REQUIREMENTS:
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3. DISTRIBUTION RESTRICTIONS. You may not: (a) alter any copyright, trademark or patent notice in the
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7. EXPORT RESTRICTIONS. The Dataset is subject to United States export laws and regulations. You must
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support services that you use, are the entire agreement for the Dataset.
9. SUPPORT SERVICES. Because this data is “as is,” we may not provide support services for it.
10. APPLICABLE LAW.
a. United States. If you acquired the software in the United States, Washington state law governs the
interpretation of this agreement and applies to claims for breach of it, regardless of conflict of laws principles.
The laws of the state where you live govern all other claims, including claims under state consumer protection
laws, unfair competition laws, and in tort.
b. Outside the United States. If you acquired the software in any other country, the laws of that country
apply.
11. LEGAL EFFECT. This Agreement describes certain legal rights. You may have other rights under the laws of your
country. You may also have rights with respect to the party from whom you acquired the Dataset. This
Agreement does not change your rights under the laws of your country if the laws of your country do not permit
it to do so.
12. DISCLAIMER OF WARRANTY. The Dataset is licensed “as-is.” You bear the risk of using it. Microsoft gives no
express warranties, guarantees or conditions. You may have additional consumer rights or statutory guarantees
under your local laws which this agreement cannot change. To the extent permitted under your local laws,
Microsoft excludes the implied warranties of merchantability, fitness for a particular purpose and non-
infringement.
13. LIMITATION ON AND EXCLUSION OF REMEDIES AND DAMAGES. YOU CAN RECOVER FROM
MICROSOFT AND ITS SUPPLIERS ONLY DIRECT DAMAGES UP TO U.S. $5.00. YOU CANNOT RECOVER ANY
OTHER DAMAGES, INCLUDING CONSEQUENTIAL, LOST PROFITS, SPECIAL, INDIRECT OR INCIDENTAL
DAMAGES.
This limitation applies to
- anything related to the software, services, content (including code) on third party Internet sites, or third party
programs; and Page 2 of 3
- claims for breach of contract, breach of warranty, guarantee or condition, strict liability, negligence, or other
tort to the extent permitted by applicable law.
It also applies even if Microsoft knew or should have known about the possibility of the damages. The above
limitation or exclusion may not apply to you because your country may not allow the exclusion or limitation of
incidental, consequential or other damages.
### Citation Information
```
@inproceedings{yang-etal-2015-wikiqa,
title = "{W}iki{QA}: A Challenge Dataset for Open-Domain Question Answering",
author = "Yang, Yi and
Yih, Wen-tau and
Meek, Christopher",
booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2015",
address = "Lisbon, Portugal",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D15-1237",
doi = "10.18653/v1/D15-1237",
pages = "2013--2018",
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
bookcorpus | 2023-04-05T09:41:56.000Z | [
"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:10M<n<100M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:2105.05241",
"region:us"
] | null | Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.This work aims to align books to their movie releases in order to providerich descriptive explanations for visual content that go semantically farbeyond the captions available in current datasets. \ | @InProceedings{Zhu_2015_ICCV,
title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
} | null | 146 | 14,515 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: BookCorpus
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: bookcorpus
dataset_info:
features:
- name: text
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 4853859824
num_examples: 74004228
download_size: 1179510242
dataset_size: 4853859824
---
# Dataset Card for BookCorpus
## 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://yknzhu.wixsite.com/mbweb](https://yknzhu.wixsite.com/mbweb)
- **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:** 1.18 GB
- **Size of the generated dataset:** 4.85 GB
- **Total amount of disk used:** 6.03 GB
### Dataset Summary
Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.This work aims to align books to their movie releases in order to providerich descriptive explanations for visual content that go semantically farbeyond the captions available in current datasets.
### 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:** 1.18 GB
- **Size of the generated dataset:** 4.85 GB
- **Total amount of disk used:** 6.03 GB
An example of 'train' looks as follows.
```
{
"text": "But I traded all my life for some lovin' and some gold"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `text`: a `string` feature.
### Data Splits
| name | train |
|----------|-------:|
|plain_text|74004228|
## 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
The books have been crawled from https://www.smashwords.com, see their [terms of service](https://www.smashwords.com/about/tos) for more information.
A data sheet for this dataset has also been created and published in [Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus](https://arxiv.org/abs/2105.05241).
### Citation Information
```
@InProceedings{Zhu_2015_ICCV,
title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@richarddwang](https://github.com/richarddwang), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
knkarthick/dialogsum | 2023-10-03T10:56:21.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"dialogue-summary",
"one-liner-summary",
"meeting-title",
"email-subject",
"region:us"
] | knkarthick | null | null | null | 74 | 14,230 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
- text2text-generation
- text-generation
task_ids: []
pretty_name: DIALOGSum Corpus
tags:
- dialogue-summary
- one-liner-summary
- meeting-title
- email-subject
---
# Dataset Card for DIALOGSum Corpus
## Dataset Description
### Links
- **Homepage:** https://aclanthology.org/2021.findings-acl.449
- **Repository:** https://github.com/cylnlp/dialogsum
- **Paper:** https://aclanthology.org/2021.findings-acl.449
- **Point of Contact:** https://huggingface.co/knkarthick
### Dataset Summary
DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding manually labeled summaries and topics.
### Languages
English
## Dataset Structure
### Data Instances
DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues (+1000 tests) split into train, test and validation.
The first instance in the training set:
{'id': 'train_0', 'summary': "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.", 'dialogue': "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n#Person2#: I found it would be a good idea to get a check-up.\n#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n#Person2#: Ok.\n#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n#Person2#: Yes.\n#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n#Person2#: Ok, thanks doctor.", 'topic': "get a check-up}
### Data Fields
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- topic: human written topic/one liner of the dialogue.
- id: unique file id of an example.
### Data Splits
- train: 12460
- val: 500
- test: 1500
- holdout: 100 [Only 3 features: id, dialogue, topic]
## Dataset Creation
### Curation Rationale
In paper:
We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers.
Compared with previous datasets, dialogues from DialogSum have distinct characteristics:
Under rich real-life scenarios, including more diverse task-oriented scenarios;
Have clear communication patterns and intents, which is valuable to serve as summarization sources;
Have a reasonable length, which comforts the purpose of automatic summarization.
We ask annotators to summarize each dialogue based on the following criteria:
Convey the most salient information;
Be brief;
Preserve important named entities within the conversation;
Be written from an observer perspective;
Be written in formal language.
### Who are the source language producers?
linguists
### Who are the annotators?
language experts
## Licensing Information
CC BY-NC-SA 4.0
## Citation Information
```
@inproceedings{chen-etal-2021-dialogsum,
title = "{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset",
author = "Chen, Yulong and
Liu, Yang and
Chen, Liang and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.449",
doi = "10.18653/v1/2021.findings-acl.449",
pages = "5062--5074",
```
## Contributions
Thanks to [@cylnlp](https://github.com/cylnlp) for adding this dataset. |
mozilla-foundation/common_voice_13_0 | 2023-06-26T15:23:12.000Z | [
"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"
] | mozilla-foundation | null | @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
} | null | 74 | 14,203 | ---
pretty_name: Common Voice Corpus 13.0
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language_bcp47:
- ab
- ar
- as
- ast
- az
- ba
- bas
- be
- bg
- bn
- br
- ca
- ckb
- cnh
- cs
- cv
- cy
- da
- de
- dv
- dyu
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy-NL
- ga-IE
- gl
- gn
- ha
- hi
- hsb
- hu
- hy-AM
- ia
- id
- ig
- is
- it
- ja
- ka
- kab
- kk
- kmr
- ko
- ky
- lg
- lo
- lt
- lv
- mdf
- mhr
- mk
- ml
- mn
- mr
- mrj
- mt
- myv
- nan-tw
- ne-NP
- nl
- nn-NO
- oc
- or
- pa-IN
- pl
- pt
- quy
- rm-sursilv
- rm-vallader
- ro
- ru
- rw
- sah
- sat
- sc
- sk
- skr
- sl
- sr
- sv-SE
- sw
- ta
- th
- ti
- tig
- tk
- tok
- tr
- tt
- tw
- ug
- uk
- ur
- uz
- vi
- vot
- yo
- yue
- zh-CN
- zh-HK
- zh-TW
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
ab:
- 10K<n<100K
ar:
- 100K<n<1M
as:
- 1K<n<10K
ast:
- 1K<n<10K
az:
- n<1K
ba:
- 100K<n<1M
bas:
- 1K<n<10K
be:
- 1M<n<10M
bg:
- 10K<n<100K
bn:
- 1M<n<10M
br:
- 10K<n<100K
ca:
- 1M<n<10M
ckb:
- 100K<n<1M
cnh:
- 1K<n<10K
cs:
- 100K<n<1M
cv:
- 10K<n<100K
cy:
- 100K<n<1M
da:
- 10K<n<100K
de:
- 100K<n<1M
dv:
- 10K<n<100K
dyu:
- n<1K
el:
- 10K<n<100K
en:
- 1M<n<10M
eo:
- 1M<n<10M
es:
- 1M<n<10M
et:
- 10K<n<100K
eu:
- 100K<n<1M
fa:
- 100K<n<1M
fi:
- 10K<n<100K
fr:
- 100K<n<1M
fy-NL:
- 100K<n<1M
ga-IE:
- 10K<n<100K
gl:
- 10K<n<100K
gn:
- 1K<n<10K
ha:
- 10K<n<100K
hi:
- 10K<n<100K
hsb:
- 1K<n<10K
hu:
- 10K<n<100K
hy-AM:
- 1K<n<10K
ia:
- 10K<n<100K
id:
- 10K<n<100K
ig:
- 1K<n<10K
is:
- n<1K
it:
- 100K<n<1M
ja:
- 100K<n<1M
ka:
- 10K<n<100K
kab:
- 100K<n<1M
kk:
- 1K<n<10K
kmr:
- 10K<n<100K
ko:
- 1K<n<10K
ky:
- 10K<n<100K
lg:
- 100K<n<1M
lo:
- n<1K
lt:
- 10K<n<100K
lv:
- 10K<n<100K
mdf:
- n<1K
mhr:
- 100K<n<1M
mk:
- n<1K
ml:
- 1K<n<10K
mn:
- 10K<n<100K
mr:
- 10K<n<100K
mrj:
- 10K<n<100K
mt:
- 10K<n<100K
myv:
- 1K<n<10K
nan-tw:
- 10K<n<100K
ne-NP:
- n<1K
nl:
- 10K<n<100K
nn-NO:
- n<1K
oc:
- 1K<n<10K
or:
- 1K<n<10K
pa-IN:
- 1K<n<10K
pl:
- 100K<n<1M
pt:
- 100K<n<1M
quy:
- n<1K
rm-sursilv:
- 1K<n<10K
rm-vallader:
- 1K<n<10K
ro:
- 10K<n<100K
ru:
- 100K<n<1M
rw:
- 1M<n<10M
sah:
- 1K<n<10K
sat:
- n<1K
sc:
- 1K<n<10K
sk:
- 10K<n<100K
skr:
- 1K<n<10K
sl:
- 10K<n<100K
sr:
- 1K<n<10K
sv-SE:
- 10K<n<100K
sw:
- 100K<n<1M
ta:
- 100K<n<1M
th:
- 100K<n<1M
ti:
- n<1K
tig:
- n<1K
tk:
- 1K<n<10K
tok:
- 10K<n<100K
tr:
- 10K<n<100K
tt:
- 10K<n<100K
tw:
- n<1K
ug:
- 10K<n<100K
uk:
- 10K<n<100K
ur:
- 100K<n<1M
uz:
- 100K<n<1M
vi:
- 10K<n<100K
vot:
- n<1K
yo:
- 1K<n<10K
yue:
- 10K<n<100K
zh-CN:
- 100K<n<1M
zh-HK:
- 100K<n<1M
zh-TW:
- 100K<n<1M
source_datasets:
- extended|common_voice
task_categories:
- automatic-speech-recognition
paperswithcode_id: common-voice
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."
---
# Dataset Card for Common Voice Corpus 13.0
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data 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:** [Vaibhav Srivastav](mailto:vaibhav@huggingface.co)
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 27141 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 17689 validated hours in 108 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
[🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=mozilla-foundation%2Fcommon_voice_11_0&only_verified=0&task=automatic-speech-recognition&config=ar&split=test&metric=wer)
### Languages
```
Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba
```
## How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi" for Hindi):
```python
from datasets import load_dataset
cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. 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
cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train", streaming=True)
print(next(iter(cv_13)))
```
*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
### Local
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train")
batch_sampler = BatchSampler(RandomSampler(cv_13), batch_size=32, drop_last=False)
dataloader = DataLoader(cv_13, batch_sampler=batch_sampler)
```
### Streaming
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train")
dataloader = DataLoader(cv_13, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 13 with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
## 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_13_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
}
``` |
mlabonne/guanaco-llama2-1k | 2023-08-25T16:49:41.000Z | [
"region:us"
] | mlabonne | null | null | null | 50 | 14,161 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Guanaco-1k: Lazy Llama 2 Formatting
This is a subset (1000 samples) of the excellent [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). It was created using the following [colab notebook](https://colab.research.google.com/drive/1Ad7a9zMmkxuXTOh1Z7-rNSICA4dybpM2?usp=sharing).
Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 (chat) model in a Google Colab.
|
tatsu-lab/alpaca_eval | 2023-06-09T11:58:42.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | tatsu-lab | Data for alpaca_eval, which aims to help automatic evaluation of instruction-following models | @misc{alpaca_eval,
author = {Xuechen Li and Tianyi Zhang and Yann Dubois and Rohan Taori and Ishaan Gulrajani and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {AlpacaEval: An Automatic Evaluator of Instruction-following Models},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/tatsu-lab/alpaca_eval}}
} | null | 16 | 13,828 | ---
license: cc-by-nc-4.0
---
|
iohadrubin/c5 | 2023-10-07T06:13:07.000Z | [
"region:us"
] | iohadrubin | A colossal, cleaned version of Common Crawl's web crawl corpus.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's C5 dataset by AllenAI. | @article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.10683},
} | null | 0 | 13,792 | Entry not found |
librispeech_asr | 2022-11-18T20:18:42.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_ids:speaker-identification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
audiobooks from the LibriVox project, and has been carefully segmented and aligned.87 | @inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--5210},
year={2015},
organization={IEEE}
} | null | 57 | 13,218 | ---
pretty_name: LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: librispeech-1
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- audio-classification
task_ids:
- speaker-identification
dataset_info:
- config_name: clean
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: train.100
num_bytes: 6619683041
num_examples: 28539
- name: train.360
num_bytes: 23898214592
num_examples: 104014
- name: validation
num_bytes: 359572231
num_examples: 2703
- name: test
num_bytes: 367705423
num_examples: 2620
download_size: 30121377654
dataset_size: 31245175287
- config_name: other
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: train.500
num_bytes: 31810256902
num_examples: 148688
- name: validation
num_bytes: 337283304
num_examples: 2864
- name: test
num_bytes: 352396474
num_examples: 2939
download_size: 31236565377
dataset_size: 32499936680
- config_name: all
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: train.clean.100
num_bytes: 6627791685
num_examples: 28539
- name: train.clean.360
num_bytes: 23927767570
num_examples: 104014
- name: train.other.500
num_bytes: 31852502880
num_examples: 148688
- name: validation.clean
num_bytes: 359505691
num_examples: 2703
- name: validation.other
num_bytes: 337213112
num_examples: 2864
- name: test.clean
num_bytes: 368449831
num_examples: 2620
- name: test.other
num_bytes: 353231518
num_examples: 2939
download_size: 61357943031
dataset_size: 63826462287
---
# Dataset Card for librispeech_asr
## 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:** [LibriSpeech ASR corpus](http://www.openslr.org/12)
- **Repository:** [Needs More Information]
- **Paper:** [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf)
- **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
- **Point of Contact:** [Daniel Povey](mailto:dpovey@gmail.com)
### Dataset Summary
LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean ranks the latest models from research and academia.
### Languages
The audio is in English. There are two configurations: `clean` and `other`.
The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on
a different dataset, and were divided roughly in the middle,
with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other".
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'chapter_id': 141231,
'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'id': '1272-141231-0000',
'speaker_id': 1272,
'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'}
```
### Data Fields
- file: A path to the downloaded audio file in .flac format.
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- chapter_id: id of the audiobook chapter which includes the transcription.
### Data Splits
The size of the corpus makes it impractical, or at least inconvenient
for some users, to distribute it as a single large archive. Thus the
training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively.
A simple automatic
procedure was used to select the audio in the first two sets to be, on
average, of higher recording quality and with accents closer to US
English. An acoustic model was trained on WSJ’s si-84 data subset
and was used to recognize the audio in the corpus, using a bigram
LM estimated on the text of the respective books. We computed the
Word Error Rate (WER) of this automatic transcript relative to our
reference transcripts obtained from the book texts.
The speakers in the corpus were ranked according to the WER of
the WSJ model’s transcripts, and were divided roughly in the middle,
with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other".
For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360
respectively accounting for 100h and 360h of the training data.
For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech.
| | Train.500 | Train.360 | Train.100 | Valid | Test |
| ----- | ------ | ----- | ---- | ---- | ---- |
| clean | - | 104014 | 28539 | 2703 | 2620|
| other | 148688 | - | - | 2864 | 2939 |
## 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 this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.
### Licensing Information
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--5210},
year={2015},
organization={IEEE}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
cifar100 | 2023-01-25T14:27:57.000Z | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-80-Million-Tiny-Images",
"language:en",
"license:unknown",
"region:us"
] | null | The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images
per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses.
There are two labels per image - fine label (actual class) and coarse label (superclass). | @TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
} | null | 14 | 13,190 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-80-Million-Tiny-Images
task_categories:
- image-classification
task_ids: []
paperswithcode_id: cifar-100
pretty_name: Cifar100
dataset_info:
features:
- name: img
dtype: image
- name: fine_label
dtype:
class_label:
names:
'0': apple
'1': aquarium_fish
'2': baby
'3': bear
'4': beaver
'5': bed
'6': bee
'7': beetle
'8': bicycle
'9': bottle
'10': bowl
'11': boy
'12': bridge
'13': bus
'14': butterfly
'15': camel
'16': can
'17': castle
'18': caterpillar
'19': cattle
'20': chair
'21': chimpanzee
'22': clock
'23': cloud
'24': cockroach
'25': couch
'26': cra
'27': crocodile
'28': cup
'29': dinosaur
'30': dolphin
'31': elephant
'32': flatfish
'33': forest
'34': fox
'35': girl
'36': hamster
'37': house
'38': kangaroo
'39': keyboard
'40': lamp
'41': lawn_mower
'42': leopard
'43': lion
'44': lizard
'45': lobster
'46': man
'47': maple_tree
'48': motorcycle
'49': mountain
'50': mouse
'51': mushroom
'52': oak_tree
'53': orange
'54': orchid
'55': otter
'56': palm_tree
'57': pear
'58': pickup_truck
'59': pine_tree
'60': plain
'61': plate
'62': poppy
'63': porcupine
'64': possum
'65': rabbit
'66': raccoon
'67': ray
'68': road
'69': rocket
'70': rose
'71': sea
'72': seal
'73': shark
'74': shrew
'75': skunk
'76': skyscraper
'77': snail
'78': snake
'79': spider
'80': squirrel
'81': streetcar
'82': sunflower
'83': sweet_pepper
'84': table
'85': tank
'86': telephone
'87': television
'88': tiger
'89': tractor
'90': train
'91': trout
'92': tulip
'93': turtle
'94': wardrobe
'95': whale
'96': willow_tree
'97': wolf
'98': woman
'99': worm
- name: coarse_label
dtype:
class_label:
names:
'0': aquatic_mammals
'1': fish
'2': flowers
'3': food_containers
'4': fruit_and_vegetables
'5': household_electrical_devices
'6': household_furniture
'7': insects
'8': large_carnivores
'9': large_man-made_outdoor_things
'10': large_natural_outdoor_scenes
'11': large_omnivores_and_herbivores
'12': medium_mammals
'13': non-insect_invertebrates
'14': people
'15': reptiles
'16': small_mammals
'17': trees
'18': vehicles_1
'19': vehicles_2
config_name: cifar100
splits:
- name: train
num_bytes: 112751396
num_examples: 50000
- name: test
num_bytes: 22605519
num_examples: 10000
download_size: 169001437
dataset_size: 135356915
---
# Dataset Card for CIFAR-100
## 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:** [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html)
- **Repository:**
- **Paper:** [Paper](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images
per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses.
There are two labels per image - fine label (actual class) and coarse label (superclass).
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image into one of 100 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cifar-100).
### Languages
English
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'fine_label': 19,
'coarse_label': 11
}
```
### Data Fields
- `img`: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `fine_label`: an `int` classification label with the following mapping:
`0`: apple
`1`: aquarium_fish
`2`: baby
`3`: bear
`4`: beaver
`5`: bed
`6`: bee
`7`: beetle
`8`: bicycle
`9`: bottle
`10`: bowl
`11`: boy
`12`: bridge
`13`: bus
`14`: butterfly
`15`: camel
`16`: can
`17`: castle
`18`: caterpillar
`19`: cattle
`20`: chair
`21`: chimpanzee
`22`: clock
`23`: cloud
`24`: cockroach
`25`: couch
`26`: cra
`27`: crocodile
`28`: cup
`29`: dinosaur
`30`: dolphin
`31`: elephant
`32`: flatfish
`33`: forest
`34`: fox
`35`: girl
`36`: hamster
`37`: house
`38`: kangaroo
`39`: keyboard
`40`: lamp
`41`: lawn_mower
`42`: leopard
`43`: lion
`44`: lizard
`45`: lobster
`46`: man
`47`: maple_tree
`48`: motorcycle
`49`: mountain
`50`: mouse
`51`: mushroom
`52`: oak_tree
`53`: orange
`54`: orchid
`55`: otter
`56`: palm_tree
`57`: pear
`58`: pickup_truck
`59`: pine_tree
`60`: plain
`61`: plate
`62`: poppy
`63`: porcupine
`64`: possum
`65`: rabbit
`66`: raccoon
`67`: ray
`68`: road
`69`: rocket
`70`: rose
`71`: sea
`72`: seal
`73`: shark
`74`: shrew
`75`: skunk
`76`: skyscraper
`77`: snail
`78`: snake
`79`: spider
`80`: squirrel
`81`: streetcar
`82`: sunflower
`83`: sweet_pepper
`84`: table
`85`: tank
`86`: telephone
`87`: television
`88`: tiger
`89`: tractor
`90`: train
`91`: trout
`92`: tulip
`93`: turtle
`94`: wardrobe
`95`: whale
`96`: willow_tree
`97`: wolf
`98`: woman
`99`: worm
- `coarse_label`: an `int` coarse classification label with following mapping:
`0`: aquatic_mammals
`1`: fish
`2`: flowers
`3`: food_containers
`4`: fruit_and_vegetables
`5`: household_electrical_devices
`6`: household_furniture
`7`: insects
`8`: large_carnivores
`9`: large_man-made_outdoor_things
`10`: large_natural_outdoor_scenes
`11`: large_omnivores_and_herbivores
`12`: medium_mammals
`13`: non-insect_invertebrates
`14`: people
`15`: reptiles
`16`: small_mammals
`17`: trees
`18`: vehicles_1
`19`: vehicles_2
### Data Splits
| name |train|test|
|----------|----:|---------:|
|cifar100|50000| 10000|
## 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
```
@TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset. |
mteb/sts22-crosslingual-sts | 2022-09-27T19:10:13.000Z | [
"language:ar",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:pl",
"language:ru",
"language:tr",
"language:zh",
"region:us"
] | mteb | SemEval 2022 Task 8: Multilingual News Article Similarity | \ | null | 4 | 13,067 | ---
language:
- ar
- de
- en
- es
- fr
- it
- pl
- ru
- tr
- zh
---
Scores in this dataset have been inverted to be from least to most similar!
The scores in the original STS22 task were from most to least similar. |
boolq | 2023-04-05T09:42:01.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
occurring ---they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
The text-pair classification setup is similar to existing natural language inference tasks. | @inproceedings{clark2019boolq,
title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
booktitle = {NAACL},
year = {2019},
} | null | 24 | 12,941 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: boolq
pretty_name: BoolQ
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: bool
- name: passage
dtype: string
splits:
- name: train
num_bytes: 5829592
num_examples: 9427
- name: validation
num_bytes: 1998190
num_examples: 3270
download_size: 8764539
dataset_size: 7827782
---
# Dataset Card for Boolq
## 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/boolean-questions](https://github.com/google-research-datasets/boolean-questions)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 8.77 MB
- **Size of the generated dataset:** 7.83 MB
- **Total amount of disk used:** 16.59 MB
### Dataset Summary
BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
occurring ---they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
The text-pair classification setup is similar to existing natural language inference tasks.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 8.77 MB
- **Size of the generated dataset:** 7.83 MB
- **Total amount of disk used:** 16.59 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answer": false,
"passage": "\"All biomass goes through at least some of these steps: it needs to be grown, collected, dried, fermented, distilled, and burned...",
"question": "does ethanol take more energy make that produces"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `question`: a `string` feature.
- `answer`: a `bool` feature.
- `passage`: a `string` feature.
### Data Splits
| name |train|validation|
|-------|----:|---------:|
|default| 9427| 3270|
## 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
BoolQ is released under the [Creative Commons Share-Alike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@inproceedings{clark2019boolq,
title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
booktitle = {NAACL},
year = {2019},
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. |
Open-Orca/FLAN | 2023-08-02T15:08:01.000Z | [
"size_categories:1B<n<10B",
"language:en",
"license:cc-by-4.0",
"arxiv:2301.13688",
"arxiv:2109.01652",
"arxiv:2110.08207",
"arxiv:2204.07705",
"region:us"
] | Open-Orca | null | null | null | 96 | 12,725 | ---
license: cc-by-4.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
datasets:
- Open-Orca/OpenOrca
size_categories:
- 1B<n<10B
---
<p><h1>🍮 The WHOLE FLAN Collection! 🍮</h1></p>
# Overview
This repository includes the full dataset from the [FLAN Collection](https://ai.googleblog.com/2023/02/the-flan-collection-advancing-open.html), totalling ~300GB as parquets.
Generated using the official seqio templating from the [Google FLAN Collection GitHub repo](https://github.com/google-research/FLAN/tree/main/flan/v2).
The data is subject to all the same licensing of the component datasets.
To keep up with our continued work on OpenOrca and other exciting research, find our Discord here:
https://AlignmentLab.ai
# Motivation
This work was done as part of the requirements for the OpenOrca project.
There was not a large enough subset of FLAN Collection generated publicly to subsample from to complete the work.
So, we opted to process the entire collection ourselves.
Generating this requires an understanding of seqio and a Linux server with 512GB of CPU ram, as well as fast drives and custom limits for many parameters beyond what is default on Linux server distributions (e.g., requiring up to 45,000 threads running at once).
It takes downloading over 400GB of datasets, working around tfds bugs, and then processing the datasets over the course of several days.
We provide this repo as a resource to other ML researchers, as it saves these time consuming and laborious steps to getting the data into a more accessible format for further consumption.
# Data
## Organization
* JSON files at top level are used for subsampling in OpenOrca
* Parquets in subdirectories contain the entire FLAN collection in Dask-sharded folders by submix fractions
## Zero-Shot vs Few-Shot and Options vs No-Options
The core sub-collections of FLAN are `CoT`, `Dialog`, `NIv2`, `T0`, and `flan2021`.
Within those sub-collections are four "remixes" of the data that are templated differently:
* `Zero-Shot` and `Few-Shot`
* `Zero-Shot` provides a prompt, question, or challenge without any exemplaries prior
* `Few-Shot` provides exemplaries first
* `Options` and `No-Options`
* `Options` provides a question or challenge with multiple-choice (e.g. A/B/C/D) answer options provided to select from
* `No-Options` requires a free-form answer
For every sub-collection, only some of the "remixes" may officially be provided. All available have been generated in full without any redaction or sub-sampling.
An example: `t0_fsopt_data` folder contains the sub-collection `T0`'s Few-Shot (FS), Options (OPT) remix set.
Notably, this is the largest "remix" and the one that necessitates 512GB CPU ram to generate. The raw json output is nearly 200GB.
## Parquet Sizes
Each sub-collection's individual remixes are provided as [Parquet](https://huggingface.co/docs/datasets/loading#parquet) files which have been sharded by [Dask](https://huggingface.co/docs/datasets/main/en/filesystems#dask) into ~160MB chunks (starting from 256MB blocks of the source jsonl files).
The folder structure along with size sums is provided below.
```
$ du -h --max-depth=1 ./
9.1G ./niv2_fsopt_data
2.4G ./niv2_zsopt_data
59G ./flan_fsopt_data
984M ./dialog_zsopt_data
11G ./flan_zsopt_data
8.6G ./dialog_fsopt_data
16G ./t0_zsnoopt_data
149M ./cot_fsopt_data
20M ./cot_zsopt_data
17G ./t0_zsopt_data
11G ./flan_zsnoopt_data
101G ./t0_fsopt_data
25G ./flan_fsnoopt_data
39G ./t0_fsnoopt_data
296G ./
```
# Citations
```bibtex
@misc{goodson2023huggyflan
title={Fine FLAN: Seqio to Parquet So You Don't Have To},
author={Bleys Goodson},
year={2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/datasets/Open-Orca/FLAN},
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```bibtex
@misc{wei2022finetuned,
title={Finetuned Language Models Are Zero-Shot Learners},
author={Jason Wei and Maarten Bosma and Vincent Y. Zhao and Kelvin Guu and Adams Wei Yu and Brian Lester and Nan Du and Andrew M. Dai and Quoc V. Le},
year={2022},
eprint={2109.01652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{sanh2022multitask,
title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Tali Bers and Stella Biderman and Leo Gao and Thomas Wolf and Alexander M. Rush},
year={2022},
eprint={2110.08207},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
```bibtex
@misc{wang2022supernaturalinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
opentensor/openvalidators | 2023-09-25T14:03:34.000Z | [
"size_categories:1M<n<10M",
"license:mit",
"region:us"
] | opentensor | null | null | null | 6 | 12,135 | ---
license: mit
viewer: False
size_categories:
- 1M<n<10M
---
# Dataset Card for Openvalidators dataset
## Dataset Description
- **Repository:** https://github.com/opentensor/validators
- **Homepage:** https://bittensor.com/
### Dataset Summary
The OpenValidators dataset, created by the OpenTensor Foundation, is a continuously growing collection of data generated
by the [OpenValidators](https://github.com/opentensor/validators) project in [W&B](https://wandb.ai/opentensor-dev/openvalidators/table).
It contains millions of records and serves researchers, data scientists, and miners in the Bittensor network.
The dataset provides information on network performance, node behaviors, and wandb run details.
Researchers can gain insights and detect patterns, while data scientists can use it for training models and analysis.
Miners can use the generated data to fine-tune their models and enhance their incentives in the network.
The dataset's continuous updates support collaboration and innovation in decentralized computing.
### Version support and revisions
This dataset is in constant evolution, so in order to facilitate data management, each data schema is versioned in
a hugging face dataset branch, so legacy data can be easily retrieved.
The main branch (or default revision) will always be the latest version of the dataset, following the latest schema adopted
by the openvalidators.
The current state of data organization is as following:
- `v1.0`: All data collected from the first openvalidators schema, ranging from version `1.0.0` to `1.0.8`.
- `main`: Current state of the dataset, following the latest schema adopted by the openvalidators (>= `1.1.0`).
### How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale.
The OpenValidators dataset gives you the granularity of extracting data by **run_id**, by **OpenValidators version** and
by **multiple OpenValidators versions.**
The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
**Downloading by run id**
For example, to download the data for a specific run, simply specify the corresponding **OpenValidators version** and the **wandb run id** in the format `version/raw_data/run_id.parquet`:
```python
from datasets import load_dataset
version = '1.1.0' # OpenValidators version
run_id = '0drg98iy' # WandB run id
run_id_dataset = load_dataset('opentensor/openvalidators', data_files=f'{version}/raw_data/{run_id}.parquet')
```
_Please note that only completed run_ids are included in the dataset. Runs that are still in progress will be ingested shortly after they finish._
**Downloading by OpenValidators version**
One can also leverage the `datasets` library to download all the runs within a determined **OpenValidators** version. That can be useful for researchers and data enthusiasts that are looking to do analysis in a specific **OpenValidators** version state.
```python
from datasets import load_dataset
version = '1.1.0' # Openvalidators version
version_dataset = load_dataset('opentensor/openvalidators', data_files=f'{version}/raw_data/*')
```
**Downloading by multiple OpenValidators version**
Utilizing the `datasets` library, users can efficiently download runs from multiple **OpenValidators** versions. By accessing data from various OpenValidators versions, users can undertake downstream tasks such as data fine-tuning for mining or to perform big data analysis.
```python
from datasets import load_dataset
versions = ['1.1.0', '1.1.1', ...] # Desired versions for extraction
data_files = [f'{version}/raw_data/*' for version in versions] # Set data files directories
dataset = load_dataset('opentensor/openvalidators', data_files={ 'test': data_files })
```
**Downloading legacy data using revisions**
```python
from datasets import load_dataset
version = '1.0.4' # OpenValidators version
run_id = '0plco3n0' # WandB run id
revision = 'v1.0' # Dataset revision
run_id_dataset = load_dataset('opentensor/openvalidators', data_files=f'{version}/raw_data/{run_id}.parquet', revision=revision)
```
> Note: You can interact with legacy data in all the ways mentioned above, as long as your data scope is within the same revision.
**Analyzing metadata**
All the state related to the details of the wandb data ingestion can be accessed easily using pandas and hugging face datasets structure. This data contains relevant information regarding the metadata of the run, including user information, config information and ingestion state.
```python
import pandas as pd
version = '1.1.0' # OpenValidators version for metadata analysis
df = pd.read_csv(f'hf://datasets/opentensor/openvalidators/{version}/metadata.csv')
```
## Dataset Structure
### Data Instances
**versioned raw_data**
The data is provided as-in the wandb logs, without further preprocessing or tokenization. This data is located at `version/raw_data` where each file is a wandb run.
**metadata**
This dataset defines the current state of the wandb data ingestion by **run id**.
### Data Fields
**Raw data**
The versioned raw_data collected from W&B follows the following schema:
- `rewards`: (float64) Reward vector for given step
- `completion_times`: (float64) List of completion times for a given prompt
- `completions`: (string) List of completions received for a given prompt
- `_runtime`: (float64) Runtime of the event
- `_timestamp`: (float64) Timestamp of the event
- `name`: (string) Prompt type, e.g. 'followup', 'answer', 'augment'
- `block`: (float64) Current block at given step
- `gating_loss`: (float64) Gating model loss for given step
- `rlhf_reward_model`: (float64) Output vector of the rlhf reward model
- `relevance_filter`: (float64) Output vector of the relevance scoring reward model
- `dahoas_reward_model`: (float64) Output vector of the dahoas reward model
- `blacklist_filter`:(float64) Output vector of the blacklist filter
- `nsfw_filter`:(float64) Output vector of the nsfw filter
- `prompt_reward_model`:(float64) Output vector of the prompt reward model
- `reciprocate_reward_model`:(float64) Output vector of the reciprocate reward model
- `diversity_reward_model`:(float64) Output vector of the diversity reward model
- `set_weights`: (float64) Output vector of the set weights
- `uids`:(int64) Queried uids
- `_step`: (int64) Step of the event
- `prompt`: (string) Prompt text string
- `step_length`: (float64) Elapsed time between the beginning of a run step to the end of a run step
- `best`: (string) Best completion for given prompt
**Metadata**
- `run_id`: (string) Wandb Run Id
- `completed`: (boolean) Flag indicating if the run_id is completed (finished, crashed or killed)
- `downloaded`: (boolean) Flag indicating if the run_id data has been downloaded
- `last_checkpoint`: (string) Last checkpoint of the run_id
- `hotkey`: (string) Hotkey associated with the run_id
- `openvalidators_version`: (string) Version of OpenValidators associated with the run_id
- `problematic`: (boolean) Flag indicating if the run_id data had problems to be ingested
- `problematic_reason`: (string) Reason for the run_id being problematic (Exception message)
- `wandb_json_config`: (string) JSON configuration associated with the run_id in Wandb
- `wandb_run_name`: (string) Name of the Wandb run
- `wandb_user_info`: (string) Username information associated with the Wandb run
- `wandb_tags`: (list) List of tags associated with the Wandb run
- `wandb_createdAt`: (string) Timestamp of the run creation in Wandb
## Dataset Creation
### Curation Rationale
This dataset was curated to provide a comprehensive and reliable collection of historical data obtained by the execution of different OpenValidators in the bittensor network.
The goal is to support researchers, data scientists and developers with data generated in the network, facilitating the discovery of new insights, network analysis, troubleshooting, and data extraction for downstream tasks like mining.
### Source Data
#### Initial Data Collection and Normalization
The initial data collection process for this dataset involves recurrent collection by a specialized worker responsible for extracting data from wandb and ingesting it into the Hugging Face datasets structure. The collected data is organized based on the OpenValidators version and run ID to facilitate efficient data management and granular access. Each run is collected based on its corresponding OpenValidators version tag and grouped into version-specific folders. Within each version folder, a `metadata.csv` file is included to manage the collection state, while the raw data of each run is saved in the `.parquet` format with the file name corresponding to the run ID (e.g., `run_id.parquet`). Please note that the code for this data collection process will be released for transparency and reproducibility.
#### Who are the source language producers?
The language producers for this dataset are all the openvalidators that are logging their data into wandb in conjunction of other nodes of the bittensor network. The main wandb page where the data is sent can be accessed at https://wandb.ai/opentensor-dev/openvalidators/table.
### Licensing Information
The dataset is licensed under the [MIT License](https://github.com/opentensor/validators/blob/main/LICENSE)
### Supported Tasks and Leaderboards
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
AmazonScience/massive | 2022-11-16T15:44:51.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:af-ZA",
"multilinguality:am-ET",
"multilinguality:ar-SA",
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"multilinguality:ca-ES",
"multilinguality:cy-GB",
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"multilinguality:en-US",
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"multilinguality:hu-HU",
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"multilinguality:ko-KR",
"multilinguality:lv-LV",
"multilinguality:ml-IN",
"multilinguality:mn-MN",
"multilinguality:ms-MY",
"multilinguality:my-MM",
"multilinguality:nb-NO",
"multilinguality:nl-NL",
"multilinguality:pl-PL",
"multilinguality:pt-PT",
"multilinguality:ro-RO",
"multilinguality:ru-RU",
"multilinguality:sl-SL",
"multilinguality:sq-AL",
"multilinguality:sv-SE",
"multilinguality:sw-KE",
"multilinguality:ta-IN",
"multilinguality:te-IN",
"multilinguality:th-TH",
"multilinguality:tl-PH",
"multilinguality:tr-TR",
"multilinguality:ur-PK",
"multilinguality:vi-VN",
"multilinguality:zh-CN",
"multilinguality:zh-TW",
"size_categories:100K<n<1M",
"source_datasets:original",
"license:cc-by-4.0",
"natural-language-understanding",
"arxiv:2204.08582",
"region:us"
] | AmazonScience | MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations
for the Natural Language Understanding tasks of intent prediction and slot annotation.
Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing
the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions. | @misc{fitzgerald2022massive,
title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},
author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan},
year={2022},
eprint={2204.08582},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@inproceedings{bastianelli-etal-2020-slurp,
title = "{SLURP}: A Spoken Language Understanding Resource Package",
author = "Bastianelli, Emanuele and
Vanzo, Andrea and
Swietojanski, Pawel and
Rieser, Verena",
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://aclanthology.org/2020.emnlp-main.588",
doi = "10.18653/v1/2020.emnlp-main.588",
pages = "7252--7262",
abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp."
} | null | 37 | 12,119 | ---
annotations_creators:
- expert-generated
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- af-ZA
- am-ET
- ar-SA
- az-AZ
- bn-BD
- ca-ES
- cy-GB
- da-DK
- de-DE
- el-GR
- en-US
- es-ES
- fa-IR
- fi-FI
- fr-FR
- he-IL
- hi-IN
- hu-HU
- hy-AM
- id-ID
- is-IS
- it-IT
- ja-JP
- jv-ID
- ka-GE
- km-KH
- kn-IN
- ko-KR
- lv-LV
- ml-IN
- mn-MN
- ms-MY
- my-MM
- nb-NO
- nl-NL
- pl-PL
- pt-PT
- ro-RO
- ru-RU
- sl-SL
- sq-AL
- sv-SE
- sw-KE
- ta-IN
- te-IN
- th-TH
- tl-PH
- tr-TR
- ur-PK
- vi-VN
- zh-CN
- zh-TW
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
paperswithcode_id: massive
pretty_name: MASSIVE
language_bcp47:
- af-ZA
- am-ET
- ar-SA
- az-AZ
- bn-BD
- ca-ES
- cy-GB
- da-DK
- de-DE
- el-GR
- en-US
- es-ES
- fa-IR
- fi-FI
- fr-FR
- he-IL
- hi-IN
- hu-HU
- hy-AM
- id-ID
- is-IS
- it-IT
- ja-JP
- jv-ID
- ka-GE
- km-KH
- kn-IN
- ko-KR
- lv-LV
- ml-IN
- mn-MN
- ms-MY
- my-MM
- nb-NO
- nl-NL
- pl-PL
- pt-PT
- ro-RO
- ru-RU
- sl-SL
- sq-AL
- sv-SE
- sw-KE
- ta-IN
- te-IN
- th-TH
- tl-PH
- tr-TR
- ur-PK
- vi-VN
- zh-CN
- zh-TW
tags:
- natural-language-understanding
---
# MASSIVE 1.1: A 1M-Example Multilingual Natural Language Understanding Dataset with 52 Typologically-Diverse Languages
## Table of Contents
- [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [No Warranty](#no-warranty)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/alexa/massive
- **Repository:** https://github.com/alexa/massive
- **Paper:** https://arxiv.org/abs/2204.08582
- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/1697/overview
- **Point of Contact:** [GitHub](https://github.com/alexa/massive/issues)
### Dataset Summary
MASSIVE 1.1 is a parallel dataset of > 1M utterances across 52 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.
| Name | Lang | Utt/Lang | Domains | Intents | Slots |
|:-------------------------------------------------------------------------------:|:-------:|:--------------:|:-------:|:--------:|:------:|
| MASSIVE 1.1 | 52 | 19,521 | 18 | 60 | 55 |
| SLURP (Bastianelli et al., 2020) | 1 | 16,521 | 18 | 60 | 55 |
| NLU Evaluation Data (Liu et al., 2019) | 1 | 25,716 | 18 | 54 | 56 |
| Airline Travel Information System (ATIS) (Price, 1990) | 1 | 5,871 | 1 | 26 | 129 |
| ATIS with Hindi and Turkish (Upadhyay et al., 2018) | 3 | 1,315-5,871 | 1 | 26 | 129 |
| MultiATIS++ (Xu et al., 2020) | 9 | 1,422-5,897 | 1 | 21-26 | 99-140 |
| Snips (Coucke et al., 2018) | 1 | 14,484 | - | 7 | 53 |
| Snips with French (Saade et al., 2019) | 2 | 4,818 | 2 | 14-15 | 11-12 |
| Task Oriented Parsing (TOP) (Gupta et al., 2018) | 1 | 44,873 | 2 | 25 | 36 |
| Multilingual Task-Oriented Semantic Parsing (MTOP) (Li et al., 2021) | 6 | 15,195-22,288 | 11 | 104-113 | 72-75 |
| Cross-Lingual Multilingual Task Oriented Dialog (Schuster et al., 2019) | 3 | 5,083-43,323 | 3 | 12 | 11 |
| Microsoft Dialog Challenge (Li et al., 2018) | 1 | 38,276 | 3 | 11 | 29 |
| Fluent Speech Commands (FSC) (Lugosch et al., 2019) | 1 | 30,043 | - | 31 | - |
| Chinese Audio-Textual Spoken Language Understanding (CATSLU) (Zhu et al., 2019) | 1 | 16,258 | 4 | - | 94 |
### Supported Tasks and Leaderboards
The dataset can be used to train a model for `natural-language-understanding` (NLU) :
- `intent-classification`
- `multi-class-classification`
- `natural-language-understanding`
### Languages
The MASSIVE 1.1 corpora consists of parallel sentences from 52 languages :
- `Afrikaans - South Africa (af-ZA)`
- `Amharic - Ethiopia (am-ET)`
- `Arabic - Saudi Arabia (ar-SA)`
- `Azeri - Azerbaijan (az-AZ)`
- `Bengali - Bangladesh (bn-BD)`
- `Catalan - Spain (ca-ES)`
- `Chinese - China (zh-CN)`
- `Chinese - Taiwan (zh-TW)`
- `Danish - Denmark (da-DK)`
- `German - Germany (de-DE)`
- `Greek - Greece (el-GR)`
- `English - United States (en-US)`
- `Spanish - Spain (es-ES)`
- `Farsi - Iran (fa-IR)`
- `Finnish - Finland (fi-FI)`
- `French - France (fr-FR)`
- `Hebrew - Israel (he-IL)`
- `Hungarian - Hungary (hu-HU)`
- `Armenian - Armenia (hy-AM)`
- `Indonesian - Indonesia (id-ID)`
- `Icelandic - Iceland (is-IS)`
- `Italian - Italy (it-IT)`
- `Japanese - Japan (ja-JP)`
- `Javanese - Indonesia (jv-ID)`
- `Georgian - Georgia (ka-GE)`
- `Khmer - Cambodia (km-KH)`
- `Korean - Korea (ko-KR)`
- `Latvian - Latvia (lv-LV)`
- `Mongolian - Mongolia (mn-MN)`
- `Malay - Malaysia (ms-MY)`
- `Burmese - Myanmar (my-MM)`
- `Norwegian - Norway (nb-NO)`
- `Dutch - Netherlands (nl-NL)`
- `Polish - Poland (pl-PL)`
- `Portuguese - Portugal (pt-PT)`
- `Romanian - Romania (ro-RO)`
- `Russian - Russia (ru-RU)`
- `Slovanian - Slovania (sl-SL)`
- `Albanian - Albania (sq-AL)`
- `Swedish - Sweden (sv-SE)`
- `Swahili - Kenya (sw-KE)`
- `Hindi - India (hi-IN)`
- `Kannada - India (kn-IN)`
- `Malayalam - India (ml-IN)`
- `Tamil - India (ta-IN)`
- `Telugu - India (te-IN)`
- `Thai - Thailand (th-TH)`
- `Tagalog - Philippines (tl-PH)`
- `Turkish - Turkey (tr-TR)`
- `Urdu - Pakistan (ur-PK)`
- `Vietnamese - Vietnam (vi-VN)`
- `Welsh - United Kingdom (cy-GB)`
## Load the dataset with HuggingFace
```python
from datasets import load_dataset
dataset = load_dataset("AmazonScience/massive", "en-US", split='train')
print(dataset[0])
```
## Dataset Structure
### Data Instances
```json
{
"id": "0",
"locale": "fr-FR",
"partition": "test",
"scenario": "alarm",
"intent": "alarm_set",
"utt": "réveille-moi à cinq heures du matin cette semaine",
"annot_utt": "réveille-moi à [time : cinq heures du matin] [date : cette semaine]",
"worker_id": "22",
"slot_method": [
{ "slot": "time", "method": "translation" },
{ "slot": "date", "method": "translation" }
],
"judgments": [
{
"worker_id": "22",
"intent_score": 1,
"slots_score": 1,
"grammar_score": 4,
"spelling_score": 2,
"language_identification": "target"
},
{
"worker_id": "8",
"intent_score": 1,
"slots_score": 1,
"grammar_score": 4,
"spelling_score": 2,
"language_identification": "target"
},
{
"worker_id": "0",
"intent_score": 1,
"slots_score": 1,
"grammar_score": 4,
"spelling_score": 2,
"language_identification": "target"
}
]
}
```
### Data Fields
`id`: maps to the original ID in the [SLURP](https://github.com/pswietojanski/slurp) collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization.
`locale`: is the language and country code accoring to ISO-639-1 and ISO-3166.
`partition`: is either `train`, `dev`, or `test`, according to the original split in [SLURP](https://github.com/pswietojanski/slurp).
`scenario`: is the general domain, aka "scenario" in SLURP terminology, of an utterance
`intent`: is the specific intent of an utterance within a domain formatted as `{scenario}_{intent}`
`utt`: the raw utterance text without annotations
`annot_utt`: the text from `utt` with slot annotations formatted as `[{label} : {entity}]`
`worker_id`: The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales.
`slot_method`: for each slot in the utterance, whether that slot was a `translation` (i.e., same expression just in the target language), `localization` (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or `unchanged` (i.e., the original en-US slot value was copied over without modification).
`judgments`: Each judgment collected for the localized utterance has 6 keys. `worker_id` is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker.
```plain
intent_score : "Does the sentence match the intent?"
0: No
1: Yes
2: It is a reasonable interpretation of the goal
slots_score : "Do all these terms match the categories in square brackets?"
0: No
1: Yes
2: There are no words in square brackets (utterance without a slot)
grammar_score : "Read the sentence out loud. Ignore any spelling, punctuation, or capitalization errors. Does it sound natural?"
0: Completely unnatural (nonsensical, cannot be understood at all)
1: Severe errors (the meaning cannot be understood and doesn't sound natural in your language)
2: Some errors (the meaning can be understood but it doesn't sound natural in your language)
3: Good enough (easily understood and sounds almost natural in your language)
4: Perfect (sounds natural in your language)
spelling_score : "Are all words spelled correctly? Ignore any spelling variances that may be due to differences in dialect. Missing spaces should be marked as a spelling error."
0: There are more than 2 spelling errors
1: There are 1-2 spelling errors
2: All words are spelled correctly
language_identification : "The following sentence contains words in the following languages (check all that apply)"
1: target
2: english
3: other
4: target & english
5: target & other
6: english & other
7: target & english & other
```
### Data Splits
|Language|Train|Dev|Test|
|:---:|:---:|:---:|:---:|
|af-ZA|11514|2033|2974|
|am-ET|11514|2033|2974|
|ar-SA|11514|2033|2974|
|az-AZ|11514|2033|2974|
|bn-BD|11514|2033|2974|
|ca-ES|11514|2033|2974|
|cy-GB|11514|2033|2974|
|da-DK|11514|2033|2974|
|de-DE|11514|2033|2974|
|el-GR|11514|2033|2974|
|en-US|11514|2033|2974|
|es-ES|11514|2033|2974|
|fa-IR|11514|2033|2974|
|fi-FI|11514|2033|2974|
|fr-FR|11514|2033|2974|
|he-IL|11514|2033|2974|
|hi-IN|11514|2033|2974|
|hu-HU|11514|2033|2974|
|hy-AM|11514|2033|2974|
|id-ID|11514|2033|2974|
|is-IS|11514|2033|2974|
|it-IT|11514|2033|2974|
|ja-JP|11514|2033|2974|
|jv-ID|11514|2033|2974|
|ka-GE|11514|2033|2974|
|km-KH|11514|2033|2974|
|kn-IN|11514|2033|2974|
|ko-KR|11514|2033|2974|
|lv-LV|11514|2033|2974|
|ml-IN|11514|2033|2974|
|mn-MN|11514|2033|2974|
|ms-MY|11514|2033|2974|
|my-MM|11514|2033|2974|
|nb-NO|11514|2033|2974|
|nl-NL|11514|2033|2974|
|pl-PL|11514|2033|2974|
|pt-PT|11514|2033|2974|
|ro-RO|11514|2033|2974|
|ru-RU|11514|2033|2974|
|sl-SL|11514|2033|2974|
|sq-AL|11514|2033|2974|
|sv-SE|11514|2033|2974|
|sw-KE|11514|2033|2974|
|ta-IN|11514|2033|2974|
|te-IN|11514|2033|2974|
|th-TH|11514|2033|2974|
|tl-PH|11514|2033|2974|
|tr-TR|11514|2033|2974|
|ur-PK|11514|2033|2974|
|vi-VN|11514|2033|2974|
|zh-CN|11514|2033|2974|
|zh-TW|11514|2033|2974|
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
## Additional Information
### Dataset Curators
__MASSIVE__: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan.
__SLURP__: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena.
__Hugging Face Upload and Integration__: Labrak Yanis (Not affiliated with the original corpus)
### Licensing Information
```plain
Copyright Amazon.com Inc. or its affiliates.
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=======================================================================
Creative Commons is not a party to its public licenses.
Notwithstanding, Creative Commons may elect to apply one of its public
licenses to material it publishes and in those instances will be
considered the “Licensor.” The text of the Creative Commons public
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Creative Commons may be contacted at creativecommons.org.
```
### Citation Information
Please cite the following papers when using this dataset.
```latex
@misc{fitzgerald2022massive,
title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},
author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan},
year={2022},
eprint={2204.08582},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@inproceedings{bastianelli-etal-2020-slurp,
title = "{SLURP}: A Spoken Language Understanding Resource Package",
author = "Bastianelli, Emanuele and
Vanzo, Andrea and
Swietojanski, Pawel and
Rieser, Verena",
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://aclanthology.org/2020.emnlp-main.588",
doi = "10.18653/v1/2020.emnlp-main.588",
pages = "7252--7262",
abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp."
}
```
|
bigscience/P3 | 2023-02-01T13:38:41.000Z | [
"task_categories:other",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"language:en",
"license:apache-2.0",
"arxiv:2110.08207",
"region:us"
] | bigscience | P3 (Public Pool of Prompts) is a collection of prompted English datasets covering a diverse set of NLP tasks. A prompt is the combination of an input template and a target template. The templates are functions mapping a data example into natural language for the input and target sequences. For example, in the case of an NLI dataset, the data example would include fields for *Premise, Hypothesis, Label*. An input template would be *If {Premise} is true, is it also true that {Hypothesis}?*, whereas a target template can be defined with the label choices *Choices[label]*. Here *Choices* is prompt-specific metadata that consists of the options *yes, maybe, no* corresponding to *label* being entailment (0), neutral (1) or contradiction (2).
Prompts are collected using [Promptsource](https://github.com/bigscience-workshop/promptsource), an interface to interactively write prompts on datasets, and collect prompt-specific metadata such as evaluation metrics. As of October 13th, there are 2'000 prompts collected for 270+ data(sub)sets. The collection of prompts of P3 is publicly available on [Promptsource](https://github.com/bigscience-workshop/promptsource).
To train [T0*](https://huggingface.co/bigscience/T0pp), we used a subset of the prompts available in Promptsource (see details [here](https://huggingface.co/bigscience/T0pp#training-data)). However, some of the prompts use `random.choice`, a method that selects uniformly at random an option in a list of valid possibilities. For reproducibility purposes, we release the collection of prompted examples used to train T0*. **The data available here are the materialized version of the prompted datasets used in [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) which represent only a subset of the datasets for which there is at least one prompt in Promptsource.** | @misc{sanh2021multitask,
title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush},
year={2021},
eprint={2110.08207},
archivePrefix={arXiv},
primaryClass={cs.LG}
} | null | 157 | 12,012 | ---
annotations_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: P3
size_categories:
- 100M<n<1B
task_categories:
- other
---
# Dataset Card for P3
## 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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://bigscience.huggingface.co/promptsource
- **Repository:** https://github.com/bigscience-workshop/promptsource/
- **Paper:** [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207)
- **Point of Contact:** [Victor Sanh](mailto:victor@huggingface.co)
### Dataset Summary
P3 (Public Pool of Prompts) is a collection of prompted English datasets covering a diverse set of NLP tasks. A prompt is the combination of an input template and a target template. The templates are functions mapping a data example into natural language for the input and target sequences. For example, in the case of an NLI dataset, the data example would include fields for *Premise, Hypothesis, Label*. An input template would be *If {Premise} is true, is it also true that {Hypothesis}?*, whereas a target template can be defined with the label choices *Choices[label]*. Here *Choices* is prompt-specific metadata that consists of the options *yes, maybe, no* corresponding to *label* being entailment (0), neutral (1) or contradiction (2).
Prompts are collected using [Promptsource](https://github.com/bigscience-workshop/promptsource), an interface to interactively write prompts on datasets, and collect prompt-specific metadata such as evaluation metrics. As of October 13th, there are 2'000 prompts collected for 270+ data(sub)sets. The collection of prompts of P3 is publicly available on [Promptsource](https://github.com/bigscience-workshop/promptsource).
To train [T0*](https://huggingface.co/bigscience/T0pp), we used a subset of the prompts available in Promptsource (see details [here](https://huggingface.co/bigscience/T0pp#training-data)). However, some of the prompts use `random.choice`, a method that selects uniformly at random an option in a list of valid possibilities. For reproducibility purposes, we release the collection of prompted examples used to train T0*. **The data available here are the materialized version of the prompted datasets used in [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) which represent only a subset of the datasets for which there is at least one prompt in Promptsource.**
### Supported Tasks and Leaderboards
The tasks represented in P3 cover a diverse set of NLP tasks including multiple-choice QA, sentiment analysis or natural language inference. We detail the full list of datasets in [Source Data](#source-data).
### Languages
The data in P3 are in English (BCP-47 `en`).
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```bash
{
'answer_choices': ['safe', 'trolley'],
'inputs': [86, 8, 7142, 666, 6, 405, 8, 3, 834, 1518, 21, 1346, 42, 31682, 58, 37, 3, 929, 9, 3042, 63, 2765, 808, 8, 2045, 6448, 326, 13, 8, 31682, 11, 3, 24052, 135, 16, 8, 1346, 552, 8, 3, 834, 47, 6364, 5], 'inputs_pretokenized': 'In the sentence below, does the _ stand for safe or trolley?\nThe treasury workers took the gold bars off of the trolley and stacked them in the safe until the _ was empty.',
'targets': [31682, 1],
'targets_pretokenized': '\ntrolley'
}
```
In the case of rank classification (letting the model select its the prediction the option with the highest log-likelihood), an example looks as follows:
```bash
{
'idx': [5, 0],
'inputs': [86, 8, 7142, 666, 6, 405, 8, 3, 834, 1518, 21, 19454, 42, 22227, 58, 19454, 744, 31, 17, 2112, 4553, 17742, 7, 12, 1953, 6, 298, 22227, 966, 373, 405, 5, 3, 834, 19, 72, 952, 12, 619, 16, 3, 9, 17742, 3298, 5],
'inputs_pretokenized': "In the sentence below, does the _ stand for Kyle or Logan?\nKyle doesn't wear leg warmers to bed, while Logan almost always does. _ is more likely to live in a warmer climate.",
'is_correct': True,
'targets': [19454, 1],
'targets_pretokenized': 'Kyle',
'weight': 1.0
}
```
To check all the prompted examples, you can use the [Promptsource hosted tool](http://bigscience.huggingface.co/promptsource) and choose the `Prompted dataset viewer` mode in the left panel.
### Data Fields
The data fields are the same among all splits:
- `answer_choices`: the choices (in natural language) available to the model
- `inputs_pretokenized`: the natural language input fed to the model
- `targets_pretokenized`: the natural language target that the model has to generate
- `inputs`: the tokenized input with [T5](https://huggingface.co/google/t5-v1_1-base)'s tokenizer
- `targets`: the tokenized target with [T5](https://huggingface.co/google/t5-v1_1-base)'s tokenizer
- `idx`: identifier of the (example, answer_option_id) in the case of rank classification
- `weight`: a weight for the example produced by seqio (always set to 1.0 in practise)
- `is_correct`: whether the (example, answer_option_id) is the correct one
### Data Splits
The list of data splits and their respective sizes is very long. You'll find the whole list in this [file](https://huggingface.co/datasets/bigscience/P3/blob/main/tasks_splits_and_features.py).
## Dataset Creation
### Curation Rationale
The Public Pool of Prompts relies on the Hugging Face Dataset library. Any public dataset in the Datasets library can be prompted. We select the datasets that have at least one subset in English and excluded datasets containing (predominantly) non-natural language examples.
We conservatively decided not to prompt datasets that contain potentially harmful content (for instance, datasets built on social media content). However, we sometimes prompt datasets that are purposefully built to measure bias and fairness of trained models, and reserve these prompted datasets (the validation or test sets) for evaluation purposes.
### Source Data
Here's the full list of the datasets present in the materialized version of P3:
- Multiple-Choice QA
- CommonsenseQA
- DREAM
- QUAIL
- QuaRTz
- Social IQA
- WiQA
- Cosmos
- QASC
- Quarel
- SciQ
- Wiki Hop
- ARC
- OpenBookQA
- MultiRC
- PIQA
- RACE
- HellaSwag
- BoolQ
- Extractive QA
- Adversarial QA
- Quoref
- DuoRC
- ROPES
- SQuAD v2
- ReCoRD
- Close-book QA
- Hotpot QA
- Wiki QA
- Trivia QA
- Web Questions
- Structure-to-text
- Common Gen
- Wiki Bio
- Sentiment
- Amazon
- App Reviews
- IMDB
- Rotten Tomatoes
- Yelp
- Summarization
- CNN Daily Mail
- Gigaword
- MultiNews
- SamSum
- XSum
- Topic Classification
- AG News
- DBPedia
- TREC
- Paraphrase Identification
- MRPC
- PAWS
- QQP
- Natural Language Inference
- ANLI
- CB
- RTE
- Coreference Resolution
- WSC
- Winogrande
- Word Sense disambiguation
- WiC
- Sentence Completion
- COPA
- HellaSwag
- Story Cloze
### Annotations
The prompts available in Promptsource are collected as part of BigScience, one-year long research workshop on large multilingual models and datasets. 36 contributors affiliated with 24 institutions in 8 countries participated to the prompt collection. Contributors are in majority machine learning researchers or machine learning engineers.
The main annotation guideline was that prompts needed to be grammatical and understandable by a native English speaker with no prior experience of the tasks. Additionally, prompts that required explicit counting or numerical indexing were removed in favor of natural language variants, e.g., instead of predicting indices of a span to extract (e.g. in extractive question answering), the model was expected to copy the span's text instead. With these minimal constraints, prompt writers were encouraged to use both formal and creative prompts and various orderings of the data. Most of the prompts correspond directly to a version of the original proposed task, although we also allowed prompts that permuted the original task (for instance, generating a document from its summary) or allowed for ambiguous output (for instance, not indicating a list of available choices).
The full annotation given to the contributors can be found [here](https://github.com/bigscience-workshop/promptsource/blob/main/CONTRIBUTING.md). *Note to self: the link is currently being updated with the)
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0.
### Citation Information
```bibtex
@misc{sanh2021multitask,
title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush},
year={2021},
eprint={2110.08207},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
### Contributions
Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding this dataset.
|
amazon_polarity | 2023-01-25T14:26:12.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1509.01626",
"region:us"
] | null | The Amazon reviews dataset consists of reviews from amazon.
The data span a period of 18 years, including ~35 million reviews up to March 2013.
Reviews include product and user information, ratings, and a plaintext review. | @inproceedings{mcauley2013hidden,
title={Hidden factors and hidden topics: understanding rating dimensions with review text},
author={McAuley, Julian and Leskovec, Jure},
booktitle={Proceedings of the 7th ACM conference on Recommender systems},
pages={165--172},
year={2013}
} | null | 27 | 11,958 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Amazon Review Polarity
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
- name: title
dtype: string
- name: content
dtype: string
config_name: amazon_polarity
splits:
- name: train
num_bytes: 1604364432
num_examples: 3600000
- name: test
num_bytes: 178176193
num_examples: 400000
download_size: 688339454
dataset_size: 1782540625
train-eval-index:
- config: amazon_polarity
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
content: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for Amazon Review Polarity
## 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://registry.opendata.aws/
- **Repository:** https://github.com/zhangxiangxiao/Crepe
- **Paper:** https://arxiv.org/abs/1509.01626
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu)
### Dataset Summary
The Amazon reviews dataset consists of reviews from amazon.
The data span a period of 18 years, including ~35 million reviews up to March 2013.
Reviews include product and user information, ratings, and a plaintext review.
### Supported Tasks and Leaderboards
- `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the content and the title, predict the correct star rating.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A typical data point, comprises of a title, a content and the corresponding label.
An example from the AmazonPolarity test set looks as follows:
```
{
'title':'Great CD',
'content':"My lovely Pat has one of the GREAT voices of her generation. I have listened to this CD for YEARS and I still LOVE IT. When I'm in a good mood it makes me feel better. A bad mood just evaporates like sugar in the rain. This CD just oozes LIFE. Vocals are jusat STUUNNING and lyrics just kill. One of life's hidden gems. This is a desert isle CD in my book. Why she never made it big is just beyond me. Everytime I play this, no matter black, white, young, old, male, female EVERYBODY says one thing ""Who was that singing ?""",
'label':1
}
```
### Data Fields
- 'title': a string containing the title of the review - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
- 'content': a string containing the body of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
- 'label': either 1 (positive) or 0 (negative) rating.
### Data Splits
The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. Each class has 1,800,000 training samples and 200,000 testing samples.
## Dataset Creation
### Curation Rationale
The Amazon reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu). It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
### 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
Apache License 2.0
### Citation Information
McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.
Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)
### Contributions
Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset. |
Dahoas/full-hh-rlhf | 2023-02-23T17:29:46.000Z | [
"region:us"
] | Dahoas | null | null | null | 50 | 11,813 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 203150123
num_examples: 112052
- name: test
num_bytes: 22606646
num_examples: 12451
download_size: 136150742
dataset_size: 225756769
---
# Dataset Card for "full-hh-rlhf"
Anthropic's HH dataset reformatted into prompt, chosen, rejected samples. |
monology/pile-uncopyrighted | 2023-08-31T03:45:38.000Z | [
"license:other",
"arxiv:2101.00027",
"region:us"
] | monology | null | null | null | 10 | 11,689 | ---
license: other
---
# Pile Uncopyrighted
In response to [authors demanding that LLMs stop using their works](https://tcrn.ch/3rtpIDn), here's a copy of [The Pile](https://huggingface.co/datasets/monology/pile) with all copyrighted content removed.
Please consider using this dataset to train your future LLMs, to respect authors and abide by copyright law.
Creating an uncopyrighted version of a larger dataset (ie RedPajama) is planned, with no ETA.
**Methodology**
Cleaning was performed by removing everything from the Books3, BookCorpus2, OpenSubtitles, YTSubtitles, and OWT2 subsets.
Based on section 7.1 of [the original paper](https://arxiv.org/abs/2101.00027), these datasets are the only ones which are not explicitly allowed to be used in AI training. |
THUDM/LongBench | 2023-08-29T04:51:14.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:summarization",
"task_categories:conversational",
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"language:zh",
"Long Context",
"arxiv:2308.14508",
"arxiv:2108.00573",
"arxiv:1712.07040",
"arxiv:2105.03011",
"arxiv:2104.02112",
"arxiv:2104.05938",
"arxiv:2305.05280",
"arxiv:2303.09752",
"arxiv:1910.10683",
"arxiv:2306.14893",
"arxiv:2306.03091",
"region:us"
] | THUDM | LongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text. This dataset consists of twenty different tasks, covering key long-text application scenarios such as multi-document QA, single-document QA, summarization, few-shot learning, synthetic tasks, and code completion. | null | null | 28 | 11,375 | ---
task_categories:
- question-answering
- text-generation
- summarization
- conversational
- text-classification
language:
- en
- zh
tags:
- Long Context
size_categories:
- 1K<n<10K
---
# Introduction
**LongBench** is the first benchmark for bilingual, multitask, and comprehensive assessment of **long context understanding** capabilities of large language models. LongBench includes different languages (Chinese and English) to provide a more comprehensive evaluation of the large models' multilingual capabilities on long contexts. In addition, LongBench is composed of six major categories and twenty one different tasks, covering key long-text application scenarios such as single-document QA, multi-document QA, summarization, few-shot learning, synthetic tasks and code completion.
We are fully aware of the potentially high costs involved in the model evaluation process, especially in the context of long context scenarios (such as manual annotation costs or API call costs). Therefore, we adopt a fully automated evaluation method, aimed at measuring and evaluating the model's ability to understand long contexts at the lowest cost.
LongBench includes 14 English tasks, 5 Chinese tasks, and 2 code tasks, with the average length of most tasks ranging from 5k to 15k, and a total of 4,750 test data. For detailed statistics and construction methods of LongBench tasks, please refer [here](task.md). In addition, we provide LongBench-E, a test set with a more uniform length distribution constructed by uniform sampling, with comparable amounts of data in the 0-4k, 4k-8k, and 8k+ length intervals to provide an analysis of the model's performance variations at different input lengths.
Github Repo for LongBench: https://github.com/THUDM/LongBench
Arxiv Paper for LongBench: https://arxiv.org/pdf/2308.14508.pdf
# How to use it?
#### Loading Data
```python
from datasets import load_dataset
datasets = ["narrativeqa", "qasper", "multifieldqa_en", "multifieldqa_zh", "hotpotqa", "2wikimqa", "musique", \
"dureader", "gov_report", "qmsum", "multi_news", "vcsum", "trec", "triviaqa", "samsum", "lsht", \
"passage_count", "passage_retrieval_en", "passage_retrieval_zh", "lcc", "repobench-p"]
for dataset in datasets:
data = load_dataset('THUDM/LongBench', dataset, split='test')
```
Similarly, you can load the **LongBench-E** data
```python
from datasets import load_dataset
datasets = ["qasper", "multifieldqa_en", "hotpotqa", "2wikimqa", "gov_report", "multi_news", "trec", \
"triviaqa", "samsum", "passage_count", "passage_retrieval_en", "lcc", "repobench-p"]
for dataset in datasets:
data = load_dataset('THUDM/LongBench', f"{dataset}_e", split='test')
```
Alternatively, you can download the folder from [this link](https://huggingface.co/datasets/THUDM/LongBench/resolve/main/data.zip) to load the data.
#### Data Format
All data in **LongBench** (LongBench-E) are standardized to the following format:
```json
{
"input": "The input/command for the task, usually short, such as questions in QA, queries in Few-shot tasks, etc",
"context": "The long context required for the task, such as documents, cross-file code, few-shot examples in Few-shot tasks",
"answers": "A List of all true answers",
"length": "Total length of the first three items (counted in characters for Chinese and words for English)",
"dataset": "The name of the dataset to which this piece of data belongs",
"language": "The language of this piece of data",
"all_classes": "All categories in classification tasks, null for non-classification tasks",
"_id": "Random id for each piece of data"
}
```
#### Evaluation
This repository provides data download for LongBench. If you wish to use this dataset for automated evaluation, please refer to our [github](https://github.com/THUDM/LongBench).
# Task statistics
| Task | Task Type | Eval metric | Avg len |Language | \#Sample |
| :-------- | :-----------:| :-----------: |:-------: | :-----------: |:--------: |
| HotpotQA | Multi-doc QA | F1 |9,151 |EN |200 |
| 2WikiMultihopQA| Multi-doc QA | F1 |4,887 |EN |200 |
| MuSiQue| Multi-doc QA | F1 |11,214 |EN |200 |
| DuReader| Multi-doc QA | Rouge-L |15,768 |ZH |200 |
| MultiFieldQA-en| Single-doc QA | F1 |4,559 |EN |150 |
| MultiFieldQA-zh| Single-doc QA | F1 |6,701 |ZH |200 |
| NarrativeQA| Single-doc QA | F1 |18,409 |EN |200 |
| Qasper| Single-doc QA | F1 |3,619 |EN |200 |
| GovReport| Summarization | Rouge-L |8,734 |EN |200 |
| QMSum| Summarization | Rouge-L |10,614 |EN |200 |
| MultiNews| Summarization | Rouge-L |2,113 |EN |200 |
| VCSUM| Summarization | Rouge-L |15,380 |ZH |200 |
| TriviaQA| Few shot | F1 |8,209 |EN |200 |
| SAMSum| Few shot | Rouge-L |6,258 |EN |200 |
| TREC| Few shot | Accuracy |5,177 |EN |200 |
| LSHT| Few shot | Accuracy |22,337 |ZH |200 |
| PassageRetrieval-en| Synthetic | Accuracy |9,289 |EN |200 |
| PassageCount| Synthetic | Accuracy |11,141 |EN |200 |
| PassageRetrieval-zh | Synthetic | Accuracy |6,745 |ZH |200 |
| LCC| Code | Edit Sim |1,235 |Python/C#/Java |500 |
| RepoBench-P| Code | Edit Sim |4,206 |Python/Java |500 |
> Note: In order to avoid discrepancies caused by different tokenizers, we use the word count (using Python's split function) to calculate the average length of English datasets and code datasets, and use the character count to calculate the average length of Chinese datasets.
# Task description
| Task | Task Description |
| :---------------- | :----------------------------------------------------------- |
| HotpotQA | Answer related questions based on multiple given documents |
| 2WikiMultihopQA | Answer related questions based on multiple given documents |
| MuSiQue | Answer related questions based on multiple given documents |
| DuReader | Answer related Chinese questions based on multiple retrieved documents |
| MultiFieldQA-en | Answer English questions based on a long article, which comes from a relatively diverse field |
| MultiFieldQA-zh | Answer Chinese questions based on a long article, which comes from a relatively diverse field |
| NarrativeQA | Answer questions based on stories or scripts, including understanding of important elements such as characters, plots, themes, etc. |
| Qasper | Answer questions based on a NLP research paper, questions proposed and answered by NLP practitioners |
| GovReport | A summarization task that requires summarizing government work reports |
| MultiNews | A multi-doc summarization that requires summarizing over multiple news |
| QMSum | A summarization task that requires summarizing meeting records based on user queries |
| VCSUM | A summarization task that requires summarizing Chinese meeting records |
| SAMSum | A dialogue summarization task, providing several few-shot examples |
| TriviaQA | Single document question answering task, providing several few-shot examples |
| NQ | Single document question answering task, providing several few-shot examples |
| TREC | A classification task that requires categorizing questions, includes 50 categories in total |
| LSHT | A Chinese classification task that requires categorizing news, includes 24 categories in total |
| PassageRetrieval-en | Given 30 English Wikipedia paragraphs, determine which paragraph the given summary corresponds to |
| PassageCount | Determine the total number of different paragraphs in a given repetitive article |
| PassageRetrieval-zh | Given several Chinese paragraphs from the C4 data set, determine which paragraph the given abstract corresponds to |
| LCC | Given a long piece of code, predict the next line of code |
| RepoBench-P | Given code in multiple files within a GitHub repository (including cross-file dependencies), predict the next line of code |
# Task construction
> Note: For all tasks constructed from existing datasets, we use data from the validation or test set of the existing dataset (except for VCSUM).
- The tasks of [HotpotQA](https://hotpotqa.github.io/), [2WikiMultihopQA](https://aclanthology.org/2020.coling-main.580/), [MuSiQue](https://arxiv.org/abs/2108.00573), and [DuReader](https://github.com/baidu/DuReader) are built based on the original datasets and processed to be suitable for long context evaluation. Specifically, for questions in the validation set, we select the evidence passage that contains the answer and several distracting articles. These articles together with the original question constitute the input of the tasks.
- The tasks of MultiFiedQA-zh and MultiFieldQA-en consist of long artical data from about 10 sources, including Latex papers, judicial documents, government work reports, and PDF documents indexed by Google. For each long artical, we invite several PhD and master students to annotate, i.e., to ask questions based on the long artical and give the correct answers. To better automate evaluation, we ask the annotators to propose questions with definitive answers as much as possible.
- The tasks of [NarrativeQA](https://arxiv.org/pdf/1712.07040.pdf), [Qasper](https://arxiv.org/pdf/2105.03011.pdf), [GovReport](https://arxiv.org/pdf/2104.02112.pdf), [QMSum](https://arxiv.org/pdf/2104.05938.pdf) and [MultiNews](https://aclanthology.org/P19-1102.pdf) directly use the data provided by the original papers. In the specific construction, we use the template provided by [ZeroSCROLLS](https://www.zero.scrolls-benchmark.com/) to convert the corresponding data into pure text input.
- The [VCSUM](https://arxiv.org/abs/2305.05280) task is built based on the original dataset, and we design a corresponding template to convert the corresponding data into pure text input.
- The [TriviaQA](https://nlp.cs.washington.edu/triviaqa/) task is constructed in the manner of [CoLT5](https://arxiv.org/abs/2303.09752), which provides several examples of question and answering based on documents, and requires the language model to answer related questions based on new documents.
- The tasks of [SAMSum](https://aclanthology.org/D19-5409.pdf), [TREC](https://aclanthology.org/C02-1150.pdf) and [LSHT](http://tcci.ccf.org.cn/conference/2014/dldoc/evatask6.pdf) are built based on the original datasets. For each question in the validation set, we sample several data from the training set to form few-shot examples. These examples together with the questions in the validation set constitute the input for this task.
- The PassageRetrieval-en task is constructed based on English Wikipedia. For each piece of data, we randomly sample 30 paragraphs from English Wikipedia and select one for summarization (using GPT-3.5-Turbo). This task requires the model to give the original paragraph name to which the summary corresponds.
- The PassageCount task is constructed based on the English wiki. For each piece of data, we randomly sample several passages from English Wikipedia, repeat each paragraph at random several times, and finally shuffle the paragraphs. This task requires the model to determine the total number of different paragraphs in the given context.
- The PasskeyRetrieval-zh task is constructed based on [C4](https://arxiv.org/abs/1910.10683). For each piece of data, we randomly sample several Chinese paragraphs from C4 and select one of them for summarization (using GPT-3.5-Turbo). This task requires the model to give the original paragraph name to which the summary corresponds.
- For the [LCC](https://arxiv.org/abs/2306.14893) task, we sample from the original code completion dataset. In the [RepoBench-P](https://arxiv.org/abs/2306.03091) task, we select the most challenging XF-F (Cross-File-First) setting from the original dataset and refer to the Oracle-Filled scenario in the paper. For each original piece of data, we randomly extract multiple cross-file code snippets, including the gold cross-file code snippet, and concatenate them as input, requiring the model to effectively use cross-file code for completion.
# LongBench-E statistics
| Task | Task Type | \#data in 0-4k | \#data in 4-8k | \#data in 8k+|
| :--------- | :-----------:| :-----------: |:---------: | :-------------: |
| HotpotQA | Multi-doc QA | 100 |100 |100 |
| 2WikiMultihopQA| Multi-doc QA | 100 |100 |100 |
| MultiFieldQA-en| Single-doc QA | 67 |70 |13 |
| Qasper| Single-doc QA | 100 |100 |24 |
| GovReport| Summarization | 100 |100 |100 |
| MultiNews| Summarization | 100 |100 |94 |
| TriviaQA| Few shot | 100 |100 |100 |
| SAMSum| Few shot | 100 |100 |100 |
| TREC| Few shot | 100 |100 |100 |
| PassageRetrieval-en| Synthetic | 100 |100 |100 |
| PassageCount| Synthetic | 100 |100 |100 |
| LCC| Code | 100 |100 |100 |
| RepoBench-P| Code | 100 |100 |100 |
# Citation
```
@misc{bai2023longbench,
title={LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding},
author={Yushi Bai and Xin Lv and Jiajie Zhang and Hongchang Lyu and Jiankai Tang and Zhidian Huang and Zhengxiao Du and Xiao Liu and Aohan Zeng and Lei Hou and Yuxiao Dong and Jie Tang and Juanzi Li},
year={2023},
eprint={2308.14508},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
open-llm-leaderboard/details_Aspik101__trurl-2-7b-pl-instruct_unload | 2023-09-22T23:43:57.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 11,208 | ---
pretty_name: Evaluation run of Aspik101/trurl-2-7b-pl-instruct_unload
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Aspik101/trurl-2-7b-pl-instruct_unload](https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload)\
\ 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_Aspik101__trurl-2-7b-pl-instruct_unload\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T23:43:45.355114](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__trurl-2-7b-pl-instruct_unload/blob/main/results_2023-09-22T23-43-45.355114.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\": 0.16096895973154363,\n\
\ \"em_stderr\": 0.0037635677120072437,\n \"f1\": 0.22060822147650985,\n\
\ \"f1_stderr\": 0.0038388662178584767,\n \"acc\": 0.3986331756197593,\n\
\ \"acc_stderr\": 0.009900867594093993\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.16096895973154363,\n \"em_stderr\": 0.0037635677120072437,\n\
\ \"f1\": 0.22060822147650985,\n \"f1_stderr\": 0.0038388662178584767\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07429871114480667,\n \
\ \"acc_stderr\": 0.007223844172845576\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7229676400947119,\n \"acc_stderr\": 0.012577891015342412\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|arc:challenge|25_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T23_43_45.355114
path:
- '**/details_harness|drop|3_2023-09-22T23-43-45.355114.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T23-43-45.355114.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T23_43_45.355114
path:
- '**/details_harness|gsm8k|5_2023-09-22T23-43-45.355114.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T23-43-45.355114.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hellaswag|10_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:40:19.486608.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:40:19.486608.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_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T14:40:19.486608.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T14:40:19.486608.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T23_43_45.355114
path:
- '**/details_harness|winogrande|5_2023-09-22T23-43-45.355114.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T23-43-45.355114.parquet'
- config_name: results
data_files:
- split: 2023_08_17T14_40_19.486608
path:
- results_2023-08-17T14:40:19.486608.parquet
- split: 2023_09_22T23_43_45.355114
path:
- results_2023-09-22T23-43-45.355114.parquet
- split: latest
path:
- results_2023-09-22T23-43-45.355114.parquet
---
# Dataset Card for Evaluation run of Aspik101/trurl-2-7b-pl-instruct_unload
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Aspik101/trurl-2-7b-pl-instruct_unload](https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload) 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_Aspik101__trurl-2-7b-pl-instruct_unload",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T23:43:45.355114](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__trurl-2-7b-pl-instruct_unload/blob/main/results_2023-09-22T23-43-45.355114.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": 0.16096895973154363,
"em_stderr": 0.0037635677120072437,
"f1": 0.22060822147650985,
"f1_stderr": 0.0038388662178584767,
"acc": 0.3986331756197593,
"acc_stderr": 0.009900867594093993
},
"harness|drop|3": {
"em": 0.16096895973154363,
"em_stderr": 0.0037635677120072437,
"f1": 0.22060822147650985,
"f1_stderr": 0.0038388662178584767
},
"harness|gsm8k|5": {
"acc": 0.07429871114480667,
"acc_stderr": 0.007223844172845576
},
"harness|winogrande|5": {
"acc": 0.7229676400947119,
"acc_stderr": 0.012577891015342412
}
}
```
### 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] |
open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v0 | 2023-08-27T12:37:30.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 10,794 | ---
pretty_name: Evaluation run of togethercomputer/GPT-JT-6B-v0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [togethercomputer/GPT-JT-6B-v0](https://huggingface.co/togethercomputer/GPT-JT-6B-v0)\
\ 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_togethercomputer__GPT-JT-6B-v0\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-07-19T15:42:14.994932](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v0/blob/main/results_2023-07-19T15%3A42%3A14.994932.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\": 0.4917382409100623,\n\
\ \"acc_stderr\": 0.03534934330073521,\n \"acc_norm\": 0.4953627760622265,\n\
\ \"acc_norm_stderr\": 0.03534663453635268,\n \"mc1\": 0.23255813953488372,\n\
\ \"mc1_stderr\": 0.014789157531080515,\n \"mc2\": 0.3889085255663652,\n\
\ \"mc2_stderr\": 0.013877851892873926\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.38993174061433444,\n \"acc_stderr\": 0.01425295984889289,\n\
\ \"acc_norm\": 0.4206484641638225,\n \"acc_norm_stderr\": 0.014426211252508406\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4965146385182235,\n\
\ \"acc_stderr\": 0.004989660180792163,\n \"acc_norm\": 0.6796454889464251,\n\
\ \"acc_norm_stderr\": 0.004656591678606739\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-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\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \
\ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n\
\ \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n \
\ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.569811320754717,\n \"acc_stderr\": 0.030471445867183238,\n\
\ \"acc_norm\": 0.569811320754717,\n \"acc_norm_stderr\": 0.030471445867183238\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5763888888888888,\n\
\ \"acc_stderr\": 0.04132125019723369,\n \"acc_norm\": 0.5763888888888888,\n\
\ \"acc_norm_stderr\": 0.04132125019723369\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \
\ \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5375722543352601,\n\
\ \"acc_stderr\": 0.0380168510452446,\n \"acc_norm\": 0.5375722543352601,\n\
\ \"acc_norm_stderr\": 0.0380168510452446\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.04617034827006717,\n\
\ \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.04617034827006717\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\
\ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.44680851063829785,\n \"acc_stderr\": 0.0325005368436584,\n\
\ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.0325005368436584\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3684210526315789,\n\
\ \"acc_stderr\": 0.04537815354939392,\n \"acc_norm\": 0.3684210526315789,\n\
\ \"acc_norm_stderr\": 0.04537815354939392\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\
\ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2671957671957672,\n \"acc_stderr\": 0.02278967314577657,\n \"\
acc_norm\": 0.2671957671957672,\n \"acc_norm_stderr\": 0.02278967314577657\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3253968253968254,\n\
\ \"acc_stderr\": 0.04190596438871136,\n \"acc_norm\": 0.3253968253968254,\n\
\ \"acc_norm_stderr\": 0.04190596438871136\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.4870967741935484,\n \"acc_stderr\": 0.028434533152681855,\n \"\
acc_norm\": 0.4870967741935484,\n \"acc_norm_stderr\": 0.028434533152681855\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.3448275862068966,\n \"acc_stderr\": 0.033442837442804574,\n \"\
acc_norm\": 0.3448275862068966,\n \"acc_norm_stderr\": 0.033442837442804574\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\"\
: 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.42424242424242425,\n \"acc_stderr\": 0.03859268142070262,\n\
\ \"acc_norm\": 0.42424242424242425,\n \"acc_norm_stderr\": 0.03859268142070262\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6313131313131313,\n \"acc_stderr\": 0.034373055019806184,\n \"\
acc_norm\": 0.6313131313131313,\n \"acc_norm_stderr\": 0.034373055019806184\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.6217616580310881,\n \"acc_stderr\": 0.034998072761933396,\n\
\ \"acc_norm\": 0.6217616580310881,\n \"acc_norm_stderr\": 0.034998072761933396\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.4153846153846154,\n \"acc_stderr\": 0.024985354923102325,\n\
\ \"acc_norm\": 0.4153846153846154,\n \"acc_norm_stderr\": 0.024985354923102325\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.22592592592592592,\n \"acc_stderr\": 0.025497532639609553,\n \
\ \"acc_norm\": 0.22592592592592592,\n \"acc_norm_stderr\": 0.025497532639609553\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.42016806722689076,\n \"acc_stderr\": 0.03206183783236153,\n\
\ \"acc_norm\": 0.42016806722689076,\n \"acc_norm_stderr\": 0.03206183783236153\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2980132450331126,\n \"acc_stderr\": 0.03734535676787198,\n \"\
acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.03734535676787198\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.5504587155963303,\n \"acc_stderr\": 0.021327881417823373,\n \"\
acc_norm\": 0.5504587155963303,\n \"acc_norm_stderr\": 0.021327881417823373\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.36574074074074076,\n \"acc_stderr\": 0.03284738857647206,\n \"\
acc_norm\": 0.36574074074074076,\n \"acc_norm_stderr\": 0.03284738857647206\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.4362745098039216,\n \"acc_stderr\": 0.03480693138457039,\n \"\
acc_norm\": 0.4362745098039216,\n \"acc_norm_stderr\": 0.03480693138457039\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.48523206751054854,\n \"acc_stderr\": 0.032533028078777386,\n \
\ \"acc_norm\": 0.48523206751054854,\n \"acc_norm_stderr\": 0.032533028078777386\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.547085201793722,\n\
\ \"acc_stderr\": 0.033408675019233246,\n \"acc_norm\": 0.547085201793722,\n\
\ \"acc_norm_stderr\": 0.033408675019233246\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6412213740458015,\n \"acc_stderr\": 0.04206739313864908,\n\
\ \"acc_norm\": 0.6412213740458015,\n \"acc_norm_stderr\": 0.04206739313864908\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.5289256198347108,\n \"acc_stderr\": 0.04556710331269498,\n \"\
acc_norm\": 0.5289256198347108,\n \"acc_norm_stderr\": 0.04556710331269498\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6018518518518519,\n\
\ \"acc_stderr\": 0.04732332615978815,\n \"acc_norm\": 0.6018518518518519,\n\
\ \"acc_norm_stderr\": 0.04732332615978815\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6012269938650306,\n \"acc_stderr\": 0.038470214204560246,\n\
\ \"acc_norm\": 0.6012269938650306,\n \"acc_norm_stderr\": 0.038470214204560246\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\
\ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6196581196581197,\n\
\ \"acc_stderr\": 0.03180425204384099,\n \"acc_norm\": 0.6196581196581197,\n\
\ \"acc_norm_stderr\": 0.03180425204384099\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.4904214559386973,\n\
\ \"acc_stderr\": 0.017876682275340866,\n \"acc_norm\": 0.4904214559386973,\n\
\ \"acc_norm_stderr\": 0.017876682275340866\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.47109826589595377,\n \"acc_stderr\": 0.026874085883518348,\n\
\ \"acc_norm\": 0.47109826589595377,\n \"acc_norm_stderr\": 0.026874085883518348\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23910614525139665,\n\
\ \"acc_stderr\": 0.01426555419233114,\n \"acc_norm\": 0.23910614525139665,\n\
\ \"acc_norm_stderr\": 0.01426555419233114\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.49673202614379086,\n \"acc_stderr\": 0.02862930519400354,\n\
\ \"acc_norm\": 0.49673202614379086,\n \"acc_norm_stderr\": 0.02862930519400354\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.45980707395498394,\n\
\ \"acc_stderr\": 0.028306190403305696,\n \"acc_norm\": 0.45980707395498394,\n\
\ \"acc_norm_stderr\": 0.028306190403305696\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5246913580246914,\n \"acc_stderr\": 0.02778680093142745,\n\
\ \"acc_norm\": 0.5246913580246914,\n \"acc_norm_stderr\": 0.02778680093142745\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.375886524822695,\n \"acc_stderr\": 0.02889395541211588,\n \
\ \"acc_norm\": 0.375886524822695,\n \"acc_norm_stderr\": 0.02889395541211588\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3428943937418514,\n\
\ \"acc_stderr\": 0.012123463271585892,\n \"acc_norm\": 0.3428943937418514,\n\
\ \"acc_norm_stderr\": 0.012123463271585892\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.3382352941176471,\n \"acc_stderr\": 0.028739328513983572,\n\
\ \"acc_norm\": 0.3382352941176471,\n \"acc_norm_stderr\": 0.028739328513983572\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4019607843137255,\n \"acc_stderr\": 0.01983517648437537,\n \
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.01983517648437537\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.045253935963025044,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.045253935963025044\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5306122448979592,\n \"acc_stderr\": 0.031949171367580624,\n\
\ \"acc_norm\": 0.5306122448979592,\n \"acc_norm_stderr\": 0.031949171367580624\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6417910447761194,\n\
\ \"acc_stderr\": 0.03390393042268813,\n \"acc_norm\": 0.6417910447761194,\n\
\ \"acc_norm_stderr\": 0.03390393042268813\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4819277108433735,\n\
\ \"acc_stderr\": 0.038899512528272166,\n \"acc_norm\": 0.4819277108433735,\n\
\ \"acc_norm_stderr\": 0.038899512528272166\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6374269005847953,\n \"acc_stderr\": 0.0368713061556206,\n\
\ \"acc_norm\": 0.6374269005847953,\n \"acc_norm_stderr\": 0.0368713061556206\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23255813953488372,\n\
\ \"mc1_stderr\": 0.014789157531080515,\n \"mc2\": 0.3889085255663652,\n\
\ \"mc2_stderr\": 0.013877851892873926\n }\n}\n```"
repo_url: https://huggingface.co/togethercomputer/GPT-JT-6B-v0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|arc:challenge|25_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hellaswag|10_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:42:14.994932.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:42:14.994932.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_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:42:14.994932.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T15:42:14.994932.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T15:42:14.994932.parquet'
- config_name: results
data_files:
- split: 2023_07_19T15_42_14.994932
path:
- results_2023-07-19T15:42:14.994932.parquet
- split: latest
path:
- results_2023-07-19T15:42:14.994932.parquet
---
# Dataset Card for Evaluation run of togethercomputer/GPT-JT-6B-v0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/togethercomputer/GPT-JT-6B-v0
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [togethercomputer/GPT-JT-6B-v0](https://huggingface.co/togethercomputer/GPT-JT-6B-v0) 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_togethercomputer__GPT-JT-6B-v0",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-07-19T15:42:14.994932](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v0/blob/main/results_2023-07-19T15%3A42%3A14.994932.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": 0.4917382409100623,
"acc_stderr": 0.03534934330073521,
"acc_norm": 0.4953627760622265,
"acc_norm_stderr": 0.03534663453635268,
"mc1": 0.23255813953488372,
"mc1_stderr": 0.014789157531080515,
"mc2": 0.3889085255663652,
"mc2_stderr": 0.013877851892873926
},
"harness|arc:challenge|25": {
"acc": 0.38993174061433444,
"acc_stderr": 0.01425295984889289,
"acc_norm": 0.4206484641638225,
"acc_norm_stderr": 0.014426211252508406
},
"harness|hellaswag|10": {
"acc": 0.4965146385182235,
"acc_stderr": 0.004989660180792163,
"acc_norm": 0.6796454889464251,
"acc_norm_stderr": 0.004656591678606739
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"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": 0.625,
"acc_stderr": 0.039397364351956274,
"acc_norm": 0.625,
"acc_norm_stderr": 0.039397364351956274
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.55,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.55,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.569811320754717,
"acc_stderr": 0.030471445867183238,
"acc_norm": 0.569811320754717,
"acc_norm_stderr": 0.030471445867183238
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5763888888888888,
"acc_stderr": 0.04132125019723369,
"acc_norm": 0.5763888888888888,
"acc_norm_stderr": 0.04132125019723369
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5375722543352601,
"acc_stderr": 0.0380168510452446,
"acc_norm": 0.5375722543352601,
"acc_norm_stderr": 0.0380168510452446
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3137254901960784,
"acc_stderr": 0.04617034827006717,
"acc_norm": 0.3137254901960784,
"acc_norm_stderr": 0.04617034827006717
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.44680851063829785,
"acc_stderr": 0.0325005368436584,
"acc_norm": 0.44680851063829785,
"acc_norm_stderr": 0.0325005368436584
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.3684210526315789,
"acc_stderr": 0.04537815354939392,
"acc_norm": 0.3684210526315789,
"acc_norm_stderr": 0.04537815354939392
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5793103448275863,
"acc_stderr": 0.0411391498118926,
"acc_norm": 0.5793103448275863,
"acc_norm_stderr": 0.0411391498118926
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2671957671957672,
"acc_stderr": 0.02278967314577657,
"acc_norm": 0.2671957671957672,
"acc_norm_stderr": 0.02278967314577657
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3253968253968254,
"acc_stderr": 0.04190596438871136,
"acc_norm": 0.3253968253968254,
"acc_norm_stderr": 0.04190596438871136
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.4870967741935484,
"acc_stderr": 0.028434533152681855,
"acc_norm": 0.4870967741935484,
"acc_norm_stderr": 0.028434533152681855
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3448275862068966,
"acc_stderr": 0.033442837442804574,
"acc_norm": 0.3448275862068966,
"acc_norm_stderr": 0.033442837442804574
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.42424242424242425,
"acc_stderr": 0.03859268142070262,
"acc_norm": 0.42424242424242425,
"acc_norm_stderr": 0.03859268142070262
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6313131313131313,
"acc_stderr": 0.034373055019806184,
"acc_norm": 0.6313131313131313,
"acc_norm_stderr": 0.034373055019806184
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.6217616580310881,
"acc_stderr": 0.034998072761933396,
"acc_norm": 0.6217616580310881,
"acc_norm_stderr": 0.034998072761933396
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.4153846153846154,
"acc_stderr": 0.024985354923102325,
"acc_norm": 0.4153846153846154,
"acc_norm_stderr": 0.024985354923102325
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.22592592592592592,
"acc_stderr": 0.025497532639609553,
"acc_norm": 0.22592592592592592,
"acc_norm_stderr": 0.025497532639609553
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.42016806722689076,
"acc_stderr": 0.03206183783236153,
"acc_norm": 0.42016806722689076,
"acc_norm_stderr": 0.03206183783236153
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2980132450331126,
"acc_stderr": 0.03734535676787198,
"acc_norm": 0.2980132450331126,
"acc_norm_stderr": 0.03734535676787198
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.5504587155963303,
"acc_stderr": 0.021327881417823373,
"acc_norm": 0.5504587155963303,
"acc_norm_stderr": 0.021327881417823373
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.36574074074074076,
"acc_stderr": 0.03284738857647206,
"acc_norm": 0.36574074074074076,
"acc_norm_stderr": 0.03284738857647206
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.4362745098039216,
"acc_stderr": 0.03480693138457039,
"acc_norm": 0.4362745098039216,
"acc_norm_stderr": 0.03480693138457039
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.48523206751054854,
"acc_stderr": 0.032533028078777386,
"acc_norm": 0.48523206751054854,
"acc_norm_stderr": 0.032533028078777386
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.547085201793722,
"acc_stderr": 0.033408675019233246,
"acc_norm": 0.547085201793722,
"acc_norm_stderr": 0.033408675019233246
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6412213740458015,
"acc_stderr": 0.04206739313864908,
"acc_norm": 0.6412213740458015,
"acc_norm_stderr": 0.04206739313864908
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.5289256198347108,
"acc_stderr": 0.04556710331269498,
"acc_norm": 0.5289256198347108,
"acc_norm_stderr": 0.04556710331269498
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6018518518518519,
"acc_stderr": 0.04732332615978815,
"acc_norm": 0.6018518518518519,
"acc_norm_stderr": 0.04732332615978815
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6012269938650306,
"acc_stderr": 0.038470214204560246,
"acc_norm": 0.6012269938650306,
"acc_norm_stderr": 0.038470214204560246
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.7281553398058253,
"acc_stderr": 0.044052680241409216,
"acc_norm": 0.7281553398058253,
"acc_norm_stderr": 0.044052680241409216
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.6196581196581197,
"acc_stderr": 0.03180425204384099,
"acc_norm": 0.6196581196581197,
"acc_norm_stderr": 0.03180425204384099
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.4904214559386973,
"acc_stderr": 0.017876682275340866,
"acc_norm": 0.4904214559386973,
"acc_norm_stderr": 0.017876682275340866
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.47109826589595377,
"acc_stderr": 0.026874085883518348,
"acc_norm": 0.47109826589595377,
"acc_norm_stderr": 0.026874085883518348
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.23910614525139665,
"acc_stderr": 0.01426555419233114,
"acc_norm": 0.23910614525139665,
"acc_norm_stderr": 0.01426555419233114
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.49673202614379086,
"acc_stderr": 0.02862930519400354,
"acc_norm": 0.49673202614379086,
"acc_norm_stderr": 0.02862930519400354
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.45980707395498394,
"acc_stderr": 0.028306190403305696,
"acc_norm": 0.45980707395498394,
"acc_norm_stderr": 0.028306190403305696
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5246913580246914,
"acc_stderr": 0.02778680093142745,
"acc_norm": 0.5246913580246914,
"acc_norm_stderr": 0.02778680093142745
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.375886524822695,
"acc_stderr": 0.02889395541211588,
"acc_norm": 0.375886524822695,
"acc_norm_stderr": 0.02889395541211588
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3428943937418514,
"acc_stderr": 0.012123463271585892,
"acc_norm": 0.3428943937418514,
"acc_norm_stderr": 0.012123463271585892
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.3382352941176471,
"acc_stderr": 0.028739328513983572,
"acc_norm": 0.3382352941176471,
"acc_norm_stderr": 0.028739328513983572
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.01983517648437537,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.01983517648437537
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6636363636363637,
"acc_stderr": 0.045253935963025044,
"acc_norm": 0.6636363636363637,
"acc_norm_stderr": 0.045253935963025044
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5306122448979592,
"acc_stderr": 0.031949171367580624,
"acc_norm": 0.5306122448979592,
"acc_norm_stderr": 0.031949171367580624
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6417910447761194,
"acc_stderr": 0.03390393042268813,
"acc_norm": 0.6417910447761194,
"acc_norm_stderr": 0.03390393042268813
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4819277108433735,
"acc_stderr": 0.038899512528272166,
"acc_norm": 0.4819277108433735,
"acc_norm_stderr": 0.038899512528272166
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6374269005847953,
"acc_stderr": 0.0368713061556206,
"acc_norm": 0.6374269005847953,
"acc_norm_stderr": 0.0368713061556206
},
"harness|truthfulqa:mc|0": {
"mc1": 0.23255813953488372,
"mc1_stderr": 0.014789157531080515,
"mc2": 0.3889085255663652,
"mc2_stderr": 0.013877851892873926
}
}
```
### 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] |
GBaker/MedQA-USMLE-4-options | 2023-01-24T19:18:09.000Z | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | GBaker | null | null | null | 16 | 10,784 | ---
license: cc-by-4.0
language:
- en
---
Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large)
<h4>Citation information:</h4>
@article{jin2020disease,
title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={arXiv preprint arXiv:2009.13081},
year={2020}
}
|
wino_bias | 2023-01-25T15:02:31.000Z | [
"task_categories:token-classification",
"task_ids:coreference-resolution",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:1804.06876",
"region:us"
] | null | WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people
referred by their occupation (e.g. the nurse, the doctor, the carpenter). | @article{DBLP:journals/corr/abs-1804-06876,
author = {Jieyu Zhao and
Tianlu Wang and
Mark Yatskar and
Vicente Ordonez and
Kai{-}Wei Chang},
title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
journal = {CoRR},
volume = {abs/1804.06876},
year = {2018},
url = {http://arxiv.org/abs/1804.06876},
archivePrefix = {arXiv},
eprint = {1804.06876},
timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | null | 9 | 10,469 | ---
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids:
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paperswithcode_id: winobias
pretty_name: WinoBias
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---
# Dataset Card for Wino_Bias 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:** [WinoBias](https://uclanlp.github.io/corefBias/overview)
- **Repository:**
- **Paper:** [Arxiv](https://arxiv.org/abs/1804.06876)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter).
### Supported Tasks and Leaderboards
The underlying task is coreference resolution.
### Languages
English
## Dataset Structure
### Data Instances
The dataset has 4 subsets: `type1_pro`, `type1_anti`, `type2_pro` and `type2_anti`.
The `*_pro` subsets contain sentences that reinforce gender stereotypes (e.g. mechanics are male, nurses are female), whereas the `*_anti` datasets contain "anti-stereotypical" sentences (e.g. mechanics are female, nurses are male).
The `type1` (*WB-Knowledge*) subsets contain sentences for which world knowledge is necessary to resolve the co-references, and `type2` (*WB-Syntax*) subsets require only the syntactic information present in the sentence to resolve them.
### Data Fields
- document_id = This is a variation on the document filename
- part_number = Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
- word_num = This is the word index of the word in that sentence.
- tokens = This is the token as segmented/tokenized in the Treebank.
- pos_tags = This is the Penn Treebank style part of speech. When parse information is missing, all part of speeches except the one for which there is some sense or proposition annotation are marked with a XX tag. The verb is marked with just a VERB tag.
- parse_bit = This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column. When the parse information is missing, the first word of a sentence is tagged as "(TOP*" and the last word is tagged as "*)" and all intermediate words are tagged with a "*".
- predicate_lemma = The predicate lemma is mentioned for the rows for which we have semantic role information or word sense information. All other rows are marked with a "-".
- predicate_framenet_id = This is the PropBank frameset ID of the predicate in predicate_lemma.
- word_sense = This is the word sense of the word in Column tokens.
- speaker = This is the speaker or author name where available.
- ner_tags = These columns identifies the spans representing various named entities. For documents which do not have named entity annotation, each line is represented with an "*".
- verbal_predicates = There is one column each of predicate argument structure information for the predicate mentioned in predicate_lemma. If there are no predicates tagged in a sentence this is a single column with all rows marked with an "*".
### Data Splits
Dev and Test Split available
## Dataset Creation
### Curation Rationale
The WinoBias dataset was introduced in 2018 (see [paper](https://arxiv.org/abs/1804.06876)), with its original task being *coreference resolution*, which is a task that aims to identify mentions that refer to the same entity or person.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The dataset was created by researchers familiar with the WinoBias project, based on two prototypical templates provided by the authors, in which entities interact in plausible ways.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
"Researchers familiar with the [WinoBias] project"
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[Recent work](https://www.microsoft.com/en-us/research/uploads/prod/2021/06/The_Salmon_paper.pdf) has shown that this dataset contains grammatical issues, incorrect or ambiguous labels, and stereotype conflation, among other limitations.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez and Kai-Wei Chan
### Licensing Information
MIT Licence
### Citation Information
@article{DBLP:journals/corr/abs-1804-06876,
author = {Jieyu Zhao and
Tianlu Wang and
Mark Yatskar and
Vicente Ordonez and
Kai{-}Wei Chang},
title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
journal = {CoRR},
volume = {abs/1804.06876},
year = {2018},
url = {http://arxiv.org/abs/1804.06876},
archivePrefix = {arXiv},
eprint = {1804.06876},
timestamp = {Mon, 13 Aug 2018 16:47:01 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1804-06876.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
### Contributions
Thanks to [@akshayb7](https://github.com/akshayb7) for adding this dataset. Updated by [@JieyuZhao](https://github.com/JieyuZhao). |
CarperAI/openai_summarize_tldr | 2023-01-10T02:53:40.000Z | [
"region:us"
] | CarperAI | null | null | null | 12 | 10,410 | ---
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---
# Dataset Card for "openai_summarize_tldr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NLPCoreTeam/mmlu_ru | 2023-06-28T19:21:48.000Z | [
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dataset_size: 74302
---
# MMLU in Russian (Massive Multitask Language Understanding)
## Overview of the Dataset
MMLU dataset for EN/RU, without auxiliary train.
The dataset contains `dev`/`val`/`test` splits for both, English and Russian languages.
Note it doesn't include `auxiliary_train` split, which wasn't translated.
Totally the dataset has ~16k samples per language: 285 `dev`, 1531 `val`, 14042 `test`.
## Description of original MMLU
MMLU dataset covers 57 different tasks.
Each task requires to choose the right answer out of four options for a given question.
Paper "Measuring Massive Multitask Language Understanding": https://arxiv.org/abs/2009.03300v3.
It is also known as the "hendrycks_test".
## Dataset Creation
The translation was made via Yandex.Translate API.
There are some translation mistakes, especially observed with terms and formulas, no fixes were applied.
Initial dataset was taken from: https://people.eecs.berkeley.edu/~hendrycks/data.tar.
## Sample example
```
{
"question_en": "Why doesn't Venus have seasons like Mars and Earth do?",
"choices_en": [
"Its rotation axis is nearly perpendicular to the plane of the Solar System.",
"It does not have an ozone layer.",
"It does not rotate fast enough.",
"It is too close to the Sun."
],
"answer": 0,
"question_ru": "Почему на Венере нет времен года, как на Марсе и Земле?",
"choices_ru": [
"Ось его вращения почти перпендикулярна плоскости Солнечной системы.",
"У него нет озонового слоя.",
"Он вращается недостаточно быстро.",
"Это слишком близко к Солнцу."
]
}
```
## Usage
To merge all subsets into dataframe per split:
```python
from collections import defaultdict
import datasets
import pandas as pd
subjects = ["abstract_algebra", "anatomy", "astronomy", "business_ethics", "clinical_knowledge", "college_biology", "college_chemistry", "college_computer_science", "college_mathematics", "college_medicine", "college_physics", "computer_security", "conceptual_physics", "econometrics", "electrical_engineering", "elementary_mathematics", "formal_logic", "global_facts", "high_school_biology", "high_school_chemistry", "high_school_computer_science", "high_school_european_history", "high_school_geography", "high_school_government_and_politics", "high_school_macroeconomics", "high_school_mathematics", "high_school_microeconomics", "high_school_physics", "high_school_psychology", "high_school_statistics", "high_school_us_history", "high_school_world_history", "human_aging", "human_sexuality", "international_law", "jurisprudence", "logical_fallacies", "machine_learning", "management", "marketing", "medical_genetics", "miscellaneous", "moral_disputes", "moral_scenarios", "nutrition", "philosophy", "prehistory", "professional_accounting", "professional_law", "professional_medicine", "professional_psychology", "public_relations", "security_studies", "sociology", "us_foreign_policy", "virology", "world_religions"]
splits = ["dev", "val", "test"]
all_datasets = {x: datasets.load_dataset("NLPCoreTeam/mmlu_ru", name=x) for x in subjects}
res = defaultdict(list)
for subject in subjects:
for split in splits:
dataset = all_datasets[subject][split]
df = dataset.to_pandas()
int2str = dataset.features['answer'].int2str
df['answer'] = df['answer'].map(int2str)
df.insert(loc=0, column='subject_en', value=subject)
res[split].append(df)
res = {k: pd.concat(v) for k, v in res.items()}
df_dev = res['dev']
df_val = res['val']
df_test = res['test']
```
## Evaluation
This dataset is intended to evaluate LLMs with few-shot/zero-shot setup.
Evaluation code: https://github.com/NLP-Core-Team/mmlu_ru
Also resources might be helpful:
1. https://github.com/hendrycks/test
1. https://github.com/openai/evals/blob/main/examples/mmlu.ipynb
1. https://github.com/EleutherAI/lm-evaluation-harness/blob/master/lm_eval/tasks/hendrycks_test.py
## Contributions
Dataset added by NLP core team RnD [Telegram channel](https://t.me/nlpcoreteam) |
huggingface/cats-image | 2022-02-03T12:31:30.000Z | [
"region:us"
] | huggingface | \\n | \\n | null | 0 | 10,326 | Entry not found |
hotpot_qa | 2023-04-05T10:07:23.000Z | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"multi-hop",
"arxiv:1809.09600",
"region:us"
] | null | HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features:
(1) the questions require finding and reasoning over multiple supporting documents to answer;
(2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas;
(3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervisionand explain the predictions;
(4) we offer a new type of factoid comparison questions to testQA systems’ ability to extract relevant facts and perform necessary comparison. | @inproceedings{yang2018hotpotqa,
title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering},
author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.},
booktitle={Conference on Empirical Methods in Natural Language Processing ({EMNLP})},
year={2018}
} | null | 18 | 10,306 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: HotpotQA
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: hotpotqa
tags:
- multi-hop
dataset_info:
- config_name: distractor
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: type
dtype: string
- name: level
dtype: string
- name: supporting_facts
sequence:
- name: title
dtype: string
- name: sent_id
dtype: int32
- name: context
sequence:
- name: title
dtype: string
- name: sentences
sequence: string
splits:
- name: train
num_bytes: 552949315
num_examples: 90447
- name: validation
num_bytes: 45716111
num_examples: 7405
download_size: 612746344
dataset_size: 598665426
- config_name: fullwiki
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: type
dtype: string
- name: level
dtype: string
- name: supporting_facts
sequence:
- name: title
dtype: string
- name: sent_id
dtype: int32
- name: context
sequence:
- name: title
dtype: string
- name: sentences
sequence: string
splits:
- name: train
num_bytes: 552949315
num_examples: 90447
- name: validation
num_bytes: 46848601
num_examples: 7405
- name: test
num_bytes: 46000102
num_examples: 7405
download_size: 660094672
dataset_size: 645798018
---
# Dataset Card for "hotpot_qa"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://hotpotqa.github.io/](https://hotpotqa.github.io/)
- **Repository:** https://github.com/hotpotqa/hotpot
- **Paper:** [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://arxiv.org/abs/1809.09600)
- **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:** 1.27 GB
- **Size of the generated dataset:** 1.24 GB
- **Total amount of disk used:** 2.52 GB
### Dataset Summary
HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison.
### 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
#### distractor
- **Size of downloaded dataset files:** 612.75 MB
- **Size of the generated dataset:** 598.66 MB
- **Total amount of disk used:** 1.21 GB
An example of 'validation' looks as follows.
```
{
"answer": "This is the answer",
"context": {
"sentences": [["Sent 1"], ["Sent 21", "Sent 22"]],
"title": ["Title1", "Title 2"]
},
"id": "000001",
"level": "medium",
"question": "What is the answer?",
"supporting_facts": {
"sent_id": [0, 1, 3],
"title": ["Title of para 1", "Title of para 2", "Title of para 3"]
},
"type": "comparison"
}
```
#### fullwiki
- **Size of downloaded dataset files:** 660.10 MB
- **Size of the generated dataset:** 645.80 MB
- **Total amount of disk used:** 1.31 GB
An example of 'train' looks as follows.
```
{
"answer": "This is the answer",
"context": {
"sentences": [["Sent 1"], ["Sent 2"]],
"title": ["Title1", "Title 2"]
},
"id": "000001",
"level": "hard",
"question": "What is the answer?",
"supporting_facts": {
"sent_id": [0, 1, 3],
"title": ["Title of para 1", "Title of para 2", "Title of para 3"]
},
"type": "bridge"
}
```
### Data Fields
The data fields are the same among all splits.
#### distractor
- `id`: a `string` feature.
- `question`: a `string` feature.
- `answer`: a `string` feature.
- `type`: a `string` feature.
- `level`: a `string` feature.
- `supporting_facts`: a dictionary feature containing:
- `title`: a `string` feature.
- `sent_id`: a `int32` feature.
- `context`: a dictionary feature containing:
- `title`: a `string` feature.
- `sentences`: a `list` of `string` features.
#### fullwiki
- `id`: a `string` feature.
- `question`: a `string` feature.
- `answer`: a `string` feature.
- `type`: a `string` feature.
- `level`: a `string` feature.
- `supporting_facts`: a dictionary feature containing:
- `title`: a `string` feature.
- `sent_id`: a `int32` feature.
- `context`: a dictionary feature containing:
- `title`: a `string` feature.
- `sentences`: a `list` of `string` features.
### Data Splits
#### distractor
| |train|validation|
|----------|----:|---------:|
|distractor|90447| 7405|
#### fullwiki
| |train|validation|test|
|--------|----:|---------:|---:|
|fullwiki|90447| 7405|7405|
## 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
HotpotQA is distributed under a [CC BY-SA 4.0 License](http://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
```
@inproceedings{yang2018hotpotqa,
title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering},
author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.},
booktitle={Conference on Empirical Methods in Natural Language Processing ({EMNLP})},
year={2018}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova), [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset. |
tatsu-lab/alpaca_farm | 2023-05-29T01:00:10.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | tatsu-lab | Data used in the original AlpacaFarm experiments.
Includes SFT and preference examples. | @misc{alpaca_farm,
author = {Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, Tatsunori Hashimoto},
title = {AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback},
year = {2023},
howpublished = {\\url{https://github.com/tatsu-lab/alpaca_farm}},
} | null | 14 | 10,181 | ---
license: cc-by-nc-4.0
--- |
competition_math | 2023-06-08T06:40:09.000Z | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"explanation-generation",
"arxiv:2103.03874",
"region:us"
] | null | The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems
from mathematics competitions, including the AMC 10, AMC 12, AIME, and more.
Each problem in MATH has a full step-by-step solution, which can be used to teach
models to generate answer derivations and explanations. | @article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
and Akul Arora
and Steven Basart
and Eric Tang
and Dawn Song
and Jacob Steinhardt},
journal={arXiv preprint arXiv:2103.03874},
year={2021}
} | null | 51 | 9,916 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Mathematics Aptitude Test of Heuristics (MATH)
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
tags:
- explanation-generation
dataset_info:
features:
- name: problem
dtype: string
- name: level
dtype: string
- name: type
dtype: string
- name: solution
dtype: string
splits:
- name: train
num_bytes: 5984788
num_examples: 7500
- name: test
num_bytes: 3732575
num_examples: 5000
download_size: 20327424
dataset_size: 9717363
---
# Dataset Card for Mathematics Aptitude Test of Heuristics (MATH) dataset
## 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/hendrycks/math
- **Repository:** https://github.com/hendrycks/math
- **Paper:** https://arxiv.org/pdf/2103.03874.pdf
- **Leaderboard:** N/A
- **Point of Contact:** Dan Hendrycks
### Dataset Summary
The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems
from mathematics competitions, including the AMC 10, AMC 12, AIME, and more.
Each problem in MATH has a full step-by-step solution, which can be used to teach
models to generate answer derivations and explanations.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
A data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's `\boxed` tag.
An example from the dataset is:
```
{'problem': 'A board game spinner is divided into three parts labeled $A$, $B$ and $C$. The probability of the spinner landing on $A$ is $\\frac{1}{3}$ and the probability of the spinner landing on $B$ is $\\frac{5}{12}$. What is the probability of the spinner landing on $C$? Express your answer as a common fraction.',
'level': 'Level 1',
'type': 'Counting & Probability',
'solution': 'The spinner is guaranteed to land on exactly one of the three regions, so we know that the sum of the probabilities of it landing in each region will be 1. If we let the probability of it landing in region $C$ be $x$, we then have the equation $1 = \\frac{5}{12}+\\frac{1}{3}+x$, from which we have $x=\\boxed{\\frac{1}{4}}$.'}
```
### Data Fields
* `problem`: The competition math problem.
* `solution`: The step-by-step solution.
* `level`: The problem's difficulty level from 'Level 1' to 'Level 5', where a subject's easiest problems for humans are assigned to 'Level 1' and a subject's hardest problems are assigned to 'Level 5'.
* `type`: The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus.
### Data Splits
* train: 7,500 examples
* test: 5,000 examples
## 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
https://github.com/hendrycks/math/blob/main/LICENSE
### Citation Information
```bibtex
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
and Akul Arora
and Steven Basart
and Eric Tang
and Dawn Song
and Jacob Steinhardt},
journal={arXiv preprint arXiv:2103.03874},
year={2021}
}
```
### Contributions
Thanks to [@hacobe](https://github.com/hacobe) for adding this dataset. |
lvwerra/stack-exchange-paired | 2023-03-13T11:30:17.000Z | [
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:10M<n<100M",
"language:en",
"region:us"
] | lvwerra | null | null | null | 68 | 9,739 | ---
task_categories:
- text-generation
- question-answering
language:
- en
pretty_name: StackExchange Paired
size_categories:
- 10M<n<100M
---
# StackExchange Paired
This is a processed version of the [`HuggingFaceH4/stack-exchange-preferences`](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences). The following steps were applied:
- Parse HTML to Markdown with `markdownify`
- Create pairs `(response_j, response_k)` where j was rated better than k
- Sample at most 10 pairs per question
- Shuffle the dataset globally
This dataset is designed to be used for preference learning. The processing notebook is in [the repository](https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main) as well.
|
OpenAssistant/oasst1 | 2023-05-02T13:21:21.000Z | [
"size_categories:100K<n<1M",
"language:en",
"language:es",
"language:ru",
"language:de",
"language:pl",
"language:th",
"language:vi",
"language:sv",
"language:bn",
"language:da",
"language:he",
"language:it",
"language:fa",
"language:sk",
"language:id",
"language:nb",
"language:el",
"language:nl",
"language:hu",
"language:eu",
"language:zh",
"language:eo",
"language:ja",
"language:ca",
"language:cs",
"language:bg",
"language:fi",
"language:pt",
"language:tr",
"language:ro",
"language:ar",
"language:uk",
"language:gl",
"language:fr",
"language:ko",
"license:apache-2.0",
"human-feedback",
"arxiv:2304.07327",
"region:us"
] | OpenAssistant | null | null | null | 1,040 | 9,684 | ---
license: apache-2.0
dataset_info:
features:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: user_id
dtype: string
- name: created_date
dtype: string
- name: text
dtype: string
- name: role
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- name: lang
dtype: string
- name: review_count
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- name: review_result
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dtype: bool
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struct:
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- name: insult
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- name: threat
dtype: float64
- name: sexual_explicit
dtype: float64
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dtype: string
- name: tree_state
dtype: string
- name: emojis
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- name: count
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dtype: int32
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download_size: 41596430
dataset_size: 105611404
language:
- en
- es
- ru
- de
- pl
- th
- vi
- sv
- bn
- da
- he
- it
- fa
- sk
- id
- nb
- el
- nl
- hu
- eu
- zh
- eo
- ja
- ca
- cs
- bg
- fi
- pt
- tr
- ro
- ar
- uk
- gl
- fr
- ko
tags:
- human-feedback
size_categories:
- 100K<n<1M
pretty_name: OpenAssistant Conversations
---
# OpenAssistant Conversations Dataset (OASST1)
## Dataset Description
- **Homepage:** https://www.open-assistant.io/
- **Repository:** https://github.com/LAION-AI/Open-Assistant
- **Paper:** https://arxiv.org/abs/2304.07327
### Dataset Summary
In an effort to democratize research on large-scale alignment, we release OpenAssistant
Conversations (OASST1), a human-generated, human-annotated assistant-style conversation
corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292
quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus
is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers.
Please refer to our [paper](https://arxiv.org/abs/2304.07327) for further details.
### Dataset Structure
This dataset contains message trees. Each message tree has an initial prompt message as the root node,
which can have multiple child messages as replies, and these child messages can have multiple replies.
All messages have a role property: this can either be "assistant" or "prompter". The roles in
conversation threads from prompt to leaf node strictly alternate between "prompter" and "assistant".
This version of the dataset contains data collected on the [open-assistant.io](https://open-assistant.io/) website until April 12 2023.
### JSON Example: Message
For readability, the following JSON examples are shown formatted with indentation on multiple lines.
Objects are stored without indentation (on single lines) in the actual jsonl files.
```json
{
"message_id": "218440fd-5317-4355-91dc-d001416df62b",
"parent_id": "13592dfb-a6f9-4748-a92c-32b34e239bb4",
"user_id": "8e95461f-5e94-4d8b-a2fb-d4717ce973e4",
"text": "It was the winter of 2035, and artificial intelligence (..)",
"role": "assistant",
"lang": "en",
"review_count": 3,
"review_result": true,
"deleted": false,
"rank": 0,
"synthetic": true,
"model_name": "oasst-sft-0_3000,max_new_tokens=400 (..)",
"labels": {
"spam": { "value": 0.0, "count": 3 },
"lang_mismatch": { "value": 0.0, "count": 3 },
"pii": { "value": 0.0, "count": 3 },
"not_appropriate": { "value": 0.0, "count": 3 },
"hate_speech": { "value": 0.0, "count": 3 },
"sexual_content": { "value": 0.0, "count": 3 },
"quality": { "value": 0.416, "count": 3 },
"toxicity": { "value": 0.16, "count": 3 },
"humor": { "value": 0.0, "count": 3 },
"creativity": { "value": 0.33, "count": 3 },
"violence": { "value": 0.16, "count": 3 }
}
}
```
### JSON Example: Conversation Tree
For readability, only a subset of the message properties is shown here.
```json
{
"message_tree_id": "14fbb664-a620-45ce-bee4-7c519b16a793",
"tree_state": "ready_for_export",
"prompt": {
"message_id": "14fbb664-a620-45ce-bee4-7c519b16a793",
"text": "Why can't we divide by 0? (..)",
"role": "prompter",
"lang": "en",
"replies": [
{
"message_id": "894d30b6-56b4-4605-a504-89dd15d4d1c8",
"text": "The reason we cannot divide by zero is because (..)",
"role": "assistant",
"lang": "en",
"replies": [
// ...
]
},
{
"message_id": "84d0913b-0fd9-4508-8ef5-205626a7039d",
"text": "The reason that the result of a division by zero is (..)",
"role": "assistant",
"lang": "en",
"replies": [
{
"message_id": "3352725e-f424-4e3b-a627-b6db831bdbaa",
"text": "Math is confusing. Like those weird Irrational (..)",
"role": "prompter",
"lang": "en",
"replies": [
{
"message_id": "f46207ca-3149-46e9-a466-9163d4ce499c",
"text": "Irrational numbers are simply numbers (..)",
"role": "assistant",
"lang": "en",
"replies": []
},
// ...
]
}
]
}
]
}
}
```
Please refer to [oasst-data](https://github.com/LAION-AI/Open-Assistant/tree/main/oasst-data) for
details about the data structure and Python code to read and write jsonl files containing oasst data objects.
If you would like to explore the dataset yourself you can find a
[`getting-started`](https://github.com/LAION-AI/Open-Assistant/blob/main/notebooks/openassistant-oasst1/getting-started.ipynb)
notebook in the `notebooks/openassistant-oasst1` folder of the [LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant)
github repository.
## Main Dataset Files
Conversation data is provided either as nested messages in trees (extension `.trees.jsonl.gz`)
or as a flat list (table) of messages (extension `.messages.jsonl.gz`).
### Ready For Export Trees
```
2023-04-12_oasst_ready.trees.jsonl.gz 10,364 trees with 88,838 total messages
2023-04-12_oasst_ready.messages.jsonl.gz 88,838 messages
```
Trees in `ready_for_export` state without spam and deleted messages including message labels.
The oasst_ready-trees file usually is sufficient for supervised fine-tuning (SFT) & reward model (RM) training.
### All Trees
```
2023-04-12_oasst_all.trees.jsonl.gz 66,497 trees with 161,443 total messages
2023-04-12_oasst_all.messages.jsonl.gz 161,443 messages
```
All trees, including those in states `prompt_lottery_waiting` (trees that consist of only one message, namely the initial prompt),
`aborted_low_grade` (trees that stopped growing because the messages had low quality), and `halted_by_moderator`.
### Supplemental Exports: Spam & Prompts
```
2023-04-12_oasst_spam.messages.jsonl.gz
```
These are messages which were deleted or have a negative review result (`"review_result": false`).
Besides low quality, a frequent reason for message deletion is a wrong language tag.
```
2023-04-12_oasst_prompts.messages.jsonl.gz
```
These are all the kept initial prompt messages with positive review result (no spam) of trees in `ready_for_export` or `prompt_lottery_waiting` state.
### Using the Huggingface Datasets
While HF datasets is ideal for tabular datasets, it is not a natural fit for nested data structures like the OpenAssistant conversation trees.
Nevertheless, we make all messages which can also be found in the file `2023-04-12_oasst_ready.trees.jsonl.gz` available in parquet as train/validation splits.
These are directly loadable by [Huggingface Datasets](https://pypi.org/project/datasets/).
To load the oasst1 train & validation splits use:
```python
from datasets import load_dataset
ds = load_dataset("OpenAssistant/oasst1")
train = ds['train'] # len(train)=84437 (95%)
val = ds['validation'] # len(val)=4401 (5%)
```
The messages appear in depth-first order of the message trees.
Full conversation trees can be reconstructed from the flat messages table by using the `parent_id`
and `message_id` properties to identify the parent-child relationship of messages. The `message_tree_id`
and `tree_state` properties (only present in flat messages files) can be used to find all messages of a message tree or to select trees by their state.
### Languages
OpenAssistant Conversations incorporates 35 different languages with a distribution of messages as follows:
**Languages with over 1000 messages**
- English: 71956
- Spanish: 43061
- Russian: 9089
- German: 5279
- Chinese: 4962
- French: 4251
- Thai: 3042
- Portuguese (Brazil): 2969
- Catalan: 2260
- Korean: 1553
- Ukrainian: 1352
- Italian: 1320
- Japanese: 1018
<details>
<summary><b>Languages with under 1000 messages</b></summary>
<ul>
<li>Vietnamese: 952</li>
<li>Basque: 947</li>
<li>Polish: 886</li>
<li>Hungarian: 811</li>
<li>Arabic: 666</li>
<li>Dutch: 628</li>
<li>Swedish: 512</li>
<li>Turkish: 454</li>
<li>Finnish: 386</li>
<li>Czech: 372</li>
<li>Danish: 358</li>
<li>Galician: 339</li>
<li>Hebrew: 255</li>
<li>Romanian: 200</li>
<li>Norwegian Bokmål: 133</li>
<li>Indonesian: 115</li>
<li>Bulgarian: 95</li>
<li>Bengali: 82</li>
<li>Persian: 72</li>
<li>Greek: 66</li>
<li>Esperanto: 59</li>
<li>Slovak: 19</li>
</ul>
</details>
## Contact
- Discord [Open Assistant Discord Server](https://ykilcher.com/open-assistant-discord)
- GitHub: [LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant)
- E-Mail: [open-assistant@laion.ai](mailto:open-assistant@laion.ai) |
amazon_reviews_multi | 2023-08-18T14:12:47.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:ja",
"language:zh",
"license:other",
"arxiv:2010.02573",
"region:us"
] | null | We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.
For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.
Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language. | @inproceedings{marc_reviews,
title={The Multilingual Amazon Reviews Corpus},
author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
year={2020}
} | null | 82 | 9,276 | ---
annotations_creators:
- found
language_creators:
- found
language:
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- en
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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- text-generation
- fill-mask
- text-classification
task_ids:
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- language-modeling
- masked-language-modeling
- sentiment-classification
- sentiment-scoring
- topic-classification
paperswithcode_id: null
pretty_name: The Multilingual Amazon Reviews Corpus
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- zh
---
# Dataset Card for The Multilingual Amazon Reviews Corpus
## Table of Contents
- [Dataset Card for amazon_reviews_multi](#dataset-card-for-amazon_reviews_multi)
- [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)
- [plain_text](#plain_text)
- [Data Fields](#data-fields)
- [plain_text](#plain_text-1)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Webpage:** https://registry.opendata.aws/amazon-reviews-ml/
- **Paper:** https://arxiv.org/abs/2010.02573
- **Point of Contact:** [multilingual-reviews-dataset@amazon.com](mailto:multilingual-reviews-dataset@amazon.com)
### Dataset Summary
We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.
For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.
Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish.
## Dataset Structure
### Data Instances
Each data instance corresponds to a review. The original JSON for an instance looks like so (German example):
```json
{
"review_id": "de_0784695",
"product_id": "product_de_0572654",
"reviewer_id": "reviewer_de_0645436",
"stars": "1",
"review_body": "Leider, leider nach einmal waschen ausgeblichen . Es sieht super h\u00fcbsch aus , nur leider stinkt es ganz schrecklich und ein Waschgang in der Maschine ist notwendig ! Nach einem mal waschen sah es aus als w\u00e4re es 10 Jahre alt und hatte 1000 e von Waschg\u00e4ngen hinter sich :( echt schade !",
"review_title": "Leider nicht zu empfehlen",
"language": "de",
"product_category": "home"
}
```
### Data Fields
- `review_id`: A string identifier of the review.
- `product_id`: A string identifier of the product being reviewed.
- `reviewer_id`: A string identifier of the reviewer.
- `stars`: An int between 1-5 indicating the number of stars.
- `review_body`: The text body of the review.
- `review_title`: The text title of the review.
- `language`: The string identifier of the review language.
- `product_category`: String representation of the product's category.
### Data Splits
Each language configuration comes with its own `train`, `validation`, and `test` splits. The `all_languages` split
is simply a concatenation of the corresponding split across all languages. That is, the `train` split for
`all_languages` is a concatenation of the `train` splits for each of the languages and likewise for `validation` and
`test`.
## Dataset Creation
### Curation Rationale
The dataset is motivated by the desire to advance sentiment analysis and text classification in other (non-English)
languages.
### Source Data
#### Initial Data Collection and Normalization
The authors gathered the reviews from the marketplaces in the US, Japan, Germany, France, Spain, and China for the
English, Japanese, German, French, Spanish, and Chinese languages, respectively. They then ensured the correct
language by applying a language detection algorithm, only retaining those of the target language. In a random sample
of the resulting reviews, the authors observed a small percentage of target languages that were incorrectly filtered
out and a very few mismatched languages that were incorrectly retained.
#### Who are the source language producers?
The original text comes from Amazon customers reviewing products on the marketplace across a variety of product
categories.
### Annotations
#### Annotation process
Each of the fields included are submitted by the user with the review or otherwise associated with the review. No
manual or machine-driven annotation was necessary.
#### Who are the annotators?
N/A
### Personal and Sensitive Information
According to the original dataset [license terms](https://docs.opendata.aws/amazon-reviews-ml/license.txt), you may not:
- link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or
- attempt to determine the identity of the author of any content in the Reviews Corpus.
If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically
terminate without prejudice to any of the other rights or remedies Amazon may have.
## 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. Unfortunately, each of
the languages included here is relatively high resource and well studied.
### Discussion of Biases
The dataset contains only reviews from verified purchases (as described in the paper, section 2.1), and the reviews
should conform the [Amazon Community Guidelines](https://www.amazon.com/gp/help/customer/display.html?nodeId=GLHXEX85MENUE4XF).
### Other Known Limitations
The dataset is constructed so that the distribution of star ratings is balanced. This feature has some advantages for
purposes of classification, but some types of language may be over or underrepresented relative to the original
distribution of reviews to achieve this balance.
## Additional Information
### Dataset Curators
Published by Phillip Keung, Yichao Lu, György Szarvas, and Noah A. Smith. Managed by Amazon.
### Licensing Information
Amazon has licensed this dataset under its own agreement for non-commercial research usage only. This licence is quite restrictive preventing use anywhere a fee is received including paid for internships etc. A copy of the agreement can be found at the dataset webpage here:
https://docs.opendata.aws/amazon-reviews-ml/license.txt
By accessing the Multilingual Amazon Reviews Corpus ("Reviews Corpus"), you agree that the Reviews Corpus is an Amazon Service subject to the [Amazon.com Conditions of Use](https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions:
In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Corpus for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Corpus or its contents, including use of the Reviews Corpus for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have.
### Citation Information
Please cite the following paper (arXiv) if you found this dataset useful:
Phillip Keung, Yichao Lu, György Szarvas and Noah A. Smith. “The Multilingual Amazon Reviews Corpus.” In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020.
```
@inproceedings{marc_reviews,
title={The Multilingual Amazon Reviews Corpus},
author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
year={2020}
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. |
math_dataset | 2023-04-05T10:09:32.000Z | [
"language:en",
"region:us"
] | null | Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True) | @article{2019arXiv,
author = {Saxton, Grefenstette, Hill, Kohli},
title = {Analysing Mathematical Reasoning Abilities of Neural Models},
year = {2019},
journal = {arXiv:1904.01557}
} | null | 38 | 9,227 | ---
pretty_name: Mathematics Dataset
language:
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paperswithcode_id: mathematics
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num_examples: 10000
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num_examples: 1999998
download_size: 2333082954
dataset_size: 216963167
---
# Dataset Card for "math_dataset"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/deepmind/mathematics_dataset](https://github.com/deepmind/mathematics_dataset)
- **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:** 130.65 GB
- **Size of the generated dataset:** 9.08 GB
- **Total amount of disk used:** 139.73 GB
### Dataset Summary
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
### 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
#### algebra__linear_1d
- **Size of downloaded dataset files:** 2.33 GB
- **Size of the generated dataset:** 92.60 MB
- **Total amount of disk used:** 2.43 GB
An example of 'train' looks as follows.
```
```
#### algebra__linear_1d_composed
- **Size of downloaded dataset files:** 2.33 GB
- **Size of the generated dataset:** 200.58 MB
- **Total amount of disk used:** 2.53 GB
An example of 'train' looks as follows.
```
```
#### algebra__linear_2d
- **Size of downloaded dataset files:** 2.33 GB
- **Size of the generated dataset:** 127.41 MB
- **Total amount of disk used:** 2.46 GB
An example of 'train' looks as follows.
```
```
#### algebra__linear_2d_composed
- **Size of downloaded dataset files:** 2.33 GB
- **Size of the generated dataset:** 235.59 MB
- **Total amount of disk used:** 2.57 GB
An example of 'train' looks as follows.
```
```
#### algebra__polynomial_roots
- **Size of downloaded dataset files:** 2.33 GB
- **Size of the generated dataset:** 164.01 MB
- **Total amount of disk used:** 2.50 GB
An example of 'train' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### algebra__linear_1d
- `question`: a `string` feature.
- `answer`: a `string` feature.
#### algebra__linear_1d_composed
- `question`: a `string` feature.
- `answer`: a `string` feature.
#### algebra__linear_2d
- `question`: a `string` feature.
- `answer`: a `string` feature.
#### algebra__linear_2d_composed
- `question`: a `string` feature.
- `answer`: a `string` feature.
#### algebra__polynomial_roots
- `question`: a `string` feature.
- `answer`: a `string` feature.
### Data Splits
| name | train |test |
|---------------------------|------:|----:|
|algebra__linear_1d |1999998|10000|
|algebra__linear_1d_composed|1999998|10000|
|algebra__linear_2d |1999998|10000|
|algebra__linear_2d_composed|1999998|10000|
|algebra__polynomial_roots |1999998|10000|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{2019arXiv,
author = {Saxton, Grefenstette, Hill, Kohli},
title = {Analysing Mathematical Reasoning Abilities of Neural Models},
year = {2019},
journal = {arXiv:1904.01557}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
amazon_us_reviews | 2023-04-05T09:14:36.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:topic-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns:
- marketplace: 2 letter country code of the marketplace where the review was written.
- customer_id: Random identifier that can be used to aggregate reviews written by a single author.
- review_id: The unique ID of the review.
- product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
- product_parent: Random identifier that can be used to aggregate reviews for the same product.
- product_title: Title of the product.
- product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
- star_rating: The 1-5 star rating of the review.
- helpful_votes: Number of helpful votes.
- total_votes: Number of total votes the review received.
- vine: Review was written as part of the Vine program.
- verified_purchase: The review is on a verified purchase.
- review_headline: The title of the review.
- review_body: The review text.
- review_date: The date the review was written. | \ | null | 51 | 9,121 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
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- text-generation
- fill-mask
- text-classification
task_ids:
- text-scoring
- language-modeling
- masked-language-modeling
- sentiment-classification
- sentiment-scoring
- topic-classification
pretty_name: Amazon US Reviews
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- config_name: Beauty_v1_00
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- name: marketplace
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- name: customer_id
dtype: string
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: product_parent
dtype: string
- name: product_title
dtype: string
- name: product_category
dtype: string
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dtype: int32
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dtype: int32
- name: total_votes
dtype: int32
- name: vine
dtype:
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names:
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dtype:
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- config_name: Music_v1_00
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- name: customer_id
dtype: string
- name: review_id
dtype: string
- name: product_id
dtype: string
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dtype: string
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dtype: string
- name: product_category
dtype: string
- name: star_rating
dtype: int32
- name: helpful_votes
dtype: int32
- name: total_votes
dtype: int32
- name: vine
dtype:
class_label:
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dtype:
class_label:
names:
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dtype: string
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- name: train
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- config_name: Health_Personal_Care_v1_00
features:
- name: marketplace
dtype: string
- name: customer_id
dtype: string
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: product_parent
dtype: string
- name: product_title
dtype: string
- name: product_category
dtype: string
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dtype: int32
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dtype: int32
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dtype: int32
- name: vine
dtype:
class_label:
names:
'0': N
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dtype:
class_label:
names:
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- name: review_headline
dtype: string
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dtype: string
- name: review_date
dtype: string
splits:
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num_bytes: 2679427491
num_examples: 5331449
download_size: 1011180212
dataset_size: 2679427491
- config_name: Digital_Ebook_Purchase_v1_01
features:
- name: marketplace
dtype: string
- name: customer_id
dtype: string
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: product_parent
dtype: string
- name: product_title
dtype: string
- name: product_category
dtype: string
- name: star_rating
dtype: int32
- name: helpful_votes
dtype: int32
- name: total_votes
dtype: int32
- name: vine
dtype:
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- name: review_date
dtype: string
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- config_name: Home_v1_00
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- name: marketplace
dtype: string
- name: customer_id
dtype: string
- name: review_id
dtype: string
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dtype: string
- name: product_parent
dtype: string
- name: product_title
dtype: string
- name: product_category
dtype: string
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dtype: int32
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dtype: int32
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dtype: int32
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dtype: string
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dtype: string
- name: customer_id
dtype: string
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: product_parent
dtype: string
- name: product_title
dtype: string
- name: product_category
dtype: string
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dtype: int32
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dtype: int32
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dtype: int32
- name: vine
dtype:
class_label:
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dtype:
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- name: review_date
dtype: string
splits:
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num_bytes: 4633213433
num_examples: 9002021
download_size: 1704713674
dataset_size: 4633213433
- config_name: Books_v1_00
features:
- name: marketplace
dtype: string
- name: customer_id
dtype: string
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: product_parent
dtype: string
- name: product_title
dtype: string
- name: product_category
dtype: string
- name: star_rating
dtype: int32
- name: helpful_votes
dtype: int32
- name: total_votes
dtype: int32
- name: vine
dtype:
class_label:
names:
'0': N
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- name: verified_purchase
dtype:
class_label:
names:
'0': N
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dtype: string
- name: review_body
dtype: string
- name: review_date
dtype: string
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num_examples: 10319090
download_size: 2740337188
dataset_size: 7197687124
- config_name: Digital_Ebook_Purchase_v1_00
features:
- name: marketplace
dtype: string
- name: customer_id
dtype: string
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: product_parent
dtype: string
- name: product_title
dtype: string
- name: product_category
dtype: string
- name: star_rating
dtype: int32
- name: helpful_votes
dtype: int32
- name: total_votes
dtype: int32
- name: vine
dtype:
class_label:
names:
'0': N
'1': Y
- name: verified_purchase
dtype:
class_label:
names:
'0': N
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- name: review_headline
dtype: string
- name: review_body
dtype: string
- name: review_date
dtype: string
splits:
- name: train
num_bytes: 7302303804
num_examples: 12520722
download_size: 2689739299
dataset_size: 7302303804
---
# Dataset Card for "amazon_us_reviews"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://s3.amazonaws.com/amazon-reviews-pds/readme.html](https://s3.amazonaws.com/amazon-reviews-pds/readme.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 32377.29 MB
- **Size of the generated dataset:** 82820.19 MB
- **Total amount of disk used:** 115197.49 MB
### Dataset Summary
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns :
marketplace - 2 letter country code of the marketplace where the review was written.
customer_id - Random identifier that can be used to aggregate reviews written by a single author.
review_id - The unique ID of the review.
product_id - The unique Product ID the review pertains to. In the multilingual dataset the reviews
for the same product in different countries can be grouped by the same product_id.
product_parent - Random identifier that can be used to aggregate reviews for the same product.
product_title - Title of the product.
product_category - Broad product category that can be used to group reviews
(also used to group the dataset into coherent parts).
star_rating - The 1-5 star rating of the review.
helpful_votes - Number of helpful votes.
total_votes - Number of total votes the review received.
vine - Review was written as part of the Vine program.
verified_purchase - The review is on a verified purchase.
review_headline - The title of the review.
review_body - The review text.
review_date - The date the review was written.
### 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
#### Apparel_v1_00
- **Size of downloaded dataset files:** 648.64 MB
- **Size of the generated dataset:** 2254.36 MB
- **Total amount of disk used:** 2903.00 MB
An example of 'train' looks as follows.
```
{
"customer_id": "45223824",
"helpful_votes": 0,
"marketplace": "US",
"product_category": "Apparel",
"product_id": "B016PUU3VO",
"product_parent": "893588059",
"product_title": "Fruit of the Loom Boys' A-Shirt (Pack of 4)",
"review_body": "I ordered the same size as I ordered last time, and these shirts were much larger than the previous order. They were also about 6 inches longer. It was like they sent men's shirts instead of boys' shirts. I'll be returning these...",
"review_date": "2015-01-01",
"review_headline": "Sizes not correct, too big overall and WAY too long",
"review_id": "R1N3Z13931J3O9",
"star_rating": 2,
"total_votes": 0,
"verified_purchase": 1,
"vine": 0
}
```
#### Automotive_v1_00
- **Size of downloaded dataset files:** 582.15 MB
- **Size of the generated dataset:** 1518.88 MB
- **Total amount of disk used:** 2101.03 MB
An example of 'train' looks as follows.
```
{
"customer_id": "16825098",
"helpful_votes": 0,
"marketplace": "US",
"product_category": "Automotive",
"product_id": "B000E4PCGE",
"product_parent": "694793259",
"product_title": "00-03 NISSAN SENTRA MIRROR RH (PASSENGER SIDE), Power, Non-Heated (2000 00 2001 01 2002 02 2003 03) NS35ER 963015M000",
"review_body": "Product was as described, new and a great look. Only bad thing is that one of the screws was stripped so I couldn't tighten all three.",
"review_date": "2015-08-31",
"review_headline": "new and a great look. Only bad thing is that one of ...",
"review_id": "R2RUIDUMDKG7P",
"star_rating": 3,
"total_votes": 0,
"verified_purchase": 1,
"vine": 0
}
```
#### Baby_v1_00
- **Size of downloaded dataset files:** 357.40 MB
- **Size of the generated dataset:** 956.30 MB
- **Total amount of disk used:** 1313.70 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"customer_id": "23299101",
"helpful_votes": 2,
"marketplace": "US",
"product_category": "Baby",
"product_id": "B00SN6F9NG",
"product_parent": "3470998",
"product_title": "Rhoost Nail Clipper for Baby - Ergonomically Designed and Easy to Use Baby Nail Clipper, Natural Wooden Bamboo - Baby Health and Personal Care Kits",
"review_body": "\"This is an absolute MUST item to have! I was scared to death to clip my baby's nails. I tried other baby nail clippers and th...",
"review_date": "2015-08-31",
"review_headline": "If fits so comfortably in my hand and I feel like I have ...",
"review_id": "R2DRL5NRODVQ3Z",
"star_rating": 5,
"total_votes": 2,
"verified_purchase": 1,
"vine": 0
}
```
#### Beauty_v1_00
- **Size of downloaded dataset files:** 914.08 MB
- **Size of the generated dataset:** 2397.39 MB
- **Total amount of disk used:** 3311.47 MB
An example of 'train' looks as follows.
```
{
"customer_id": "24655453",
"helpful_votes": 1,
"marketplace": "US",
"product_category": "Beauty",
"product_id": "B00SAQ9DZY",
"product_parent": "292127037",
"product_title": "12 New, High Quality, Amber 2 ml (5/8 Dram) Glass Bottles, with Orifice Reducer and Black Cap.",
"review_body": "These are great for small mixtures for EO's, especially for traveling. I only gave this 4 stars because of the orifice reducer. The hole is so small it is hard to get the oil out. Just needs to be slightly bigger.",
"review_date": "2015-08-31",
"review_headline": "Good Product",
"review_id": "R2A30ALEGLMCGN",
"star_rating": 4,
"total_votes": 1,
"verified_purchase": 1,
"vine": 0
}
```
#### Books_v1_00
- **Size of downloaded dataset files:** 2740.34 MB
- **Size of the generated dataset:** 7193.86 MB
- **Total amount of disk used:** 9934.20 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"customer_id": "49735028",
"helpful_votes": 0,
"marketplace": "US",
"product_category": "Books",
"product_id": "0664254969",
"product_parent": "248307276",
"product_title": "Presbyterian Creeds: A Guide to the Book of Confessions",
"review_body": "\"The Presbyterian Book of Confessions contains multiple Creeds for use by the denomination. This guidebook helps he lay person t...",
"review_date": "2015-08-31",
"review_headline": "The Presbyterian Book of Confessions contains multiple Creeds for use ...",
"review_id": "R2G519UREHRO8M",
"star_rating": 3,
"total_votes": 1,
"verified_purchase": 1,
"vine": 0
}
```
### Data Fields
The data fields are the same among all splits.
#### Apparel_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
#### Automotive_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
#### Baby_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
#### Beauty_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
#### Books_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
### Data Splits
| name | train |
|----------------|-------:|
|Apparel_v1_00 | 5906333|
|Automotive_v1_00 | 3514942|
|Baby_v1_00 | 1752932|
|Beauty_v1_00 | 5115666|
|Books_v1_00 | 10319090|
|Books_v1_01 | 6106719|
|Books_v1_02 | 3105520|
|Camera_v1_00 | 1801974|
|Digital_Ebook_Purchase_v1_00 | 12520722|
|Digital_Ebook_Purchase_v1_01 | 5101693|
|Digital_Music_Purchase_v1_00 | 1688884|
|Digital_Software_v1_00 | 102084|
|Digital_Video_Download_v1_00 | 4057147|
|Digital_Video_Games_v1_00 | 145431|
|Electronics_v1_00 | 3093869|
|Furniture_v1_00 | 792113|
|Gift_Card_v1_00 | 149086|
|Grocery_v1_00 | 2402458|
|Health_Personal_Care_v1_00 | 5331449|
|Home_Entertainment_v1_00 | 705889|
|Home_Improvement_v1_00 | 2634781|
|Home_v1_00 | 6221559|
|Jewelry_v1_00 | 1767753|
|Kitchen_v1_00 | 4880466|
|Lawn_and_Garden_v1_00 | 2557288|
|Luggage_v1_00 | 348657|
|Major_Appliances_v1_00 | 96901|
|Mobile_Apps_v1_00 | 5033376|
|Mobile_Electronics_v1_00 | 104975|
|Music_v1_00 | 4751577|
|Musical_Instruments_v1_00 | 904765|
|Office_Products_v1_00 | 2642434|
|Outdoors_v1_00 | 2302401|
|PC_v1_00 | 6908554|
|Personal_Care_Appliances_v1_00 | 85981|
|Pet_Products_v1_00 | 2643619|
|Shoes_v1_00 | 4366916|
|Software_v1_00 | 341931|
|Sports_v1_00 | 4850360|
|Tools_v1_00 | 1741100|
|Toys_v1_00 | 4864249|
|Video_DVD_v1_00 | 5069140|
|Video_Games_v1_00 | 1785997|
|Video_v1_00 | 380604|
|Watches_v1_00 | 960872|
|Wireless_v1_00 | 9002021|
## 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
https://s3.amazonaws.com/amazon-reviews-pds/LICENSE.txt
By accessing the Amazon Customer Reviews Library ("Reviews Library"), you agree that the
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### Citation Information
No citation information.
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. |
oscar-corpus/OSCAR-2301 | 2023-04-18T10:08:22.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:multilingual",
"size_categories:n>1T",
"source_datasets:original",
"license:cc0-1.0",
"arxiv:2212.10440",
"arxiv:2010.14571",
"region:us"
] | oscar-corpus | The Open Super-large Crawled Aggregated coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the Ungoliant architecture.\ | @ARTICLE{2022arXiv221210440J,
author = {{Jansen}, Tim and {Tong}, Yangling and {Zevallos}, Victoria and {Ortiz Suarez}, Pedro},
title = "{Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2022,
month = dec,
eid = {arXiv:2212.10440},
pages = {arXiv:2212.10440},
doi = {10.48550/arXiv.2212.10440},
archivePrefix = {arXiv},
eprint = {2212.10440},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv221210440J},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{abadji-etal-2022-towards,
title = "Towards a Cleaner Document-Oriented Multilingual Crawled Corpus",
author = "Abadji, Julien and
Ortiz Suarez, Pedro and
Romary, Laurent and
Sagot, Beno{\^\i}t",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.463",
pages = "4344--4355",
abstract = "The need for large corpora raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing. And while there have been some recent attempts to manually curate the amount of data necessary to train large language models, the main way to obtain this data is still through automatic web crawling. In this paper we take the existing multilingual web corpus OSCAR and its pipeline Ungoliant that extracts and classifies data from Common Crawl at the line level, and propose a set of improvements and automatic annotations in order to produce a new document-oriented version of OSCAR that could prove more suitable to pre-train large generative language models as well as hopefully other applications in Natural Language Processing and Digital Humanities.",
}
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
author = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Beno{\^i}t Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{\"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.},
language = {en}
}
@article{kreutzer-etal-2022-quality,
title = "Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets",
author = {Kreutzer, Julia and
Caswell, Isaac and
Wang, Lisa and
Wahab, Ahsan and
van Esch, Daan and
Ulzii-Orshikh, Nasanbayar and
Tapo, Allahsera and
Subramani, Nishant and
Sokolov, Artem and
Sikasote, Claytone and
Setyawan, Monang and
Sarin, Supheakmungkol and
Samb, Sokhar and
Sagot, Beno{\^\i}t and
Rivera, Clara and
Rios, Annette and
Papadimitriou, Isabel and
Osei, Salomey and
Suarez, Pedro Ortiz and
Orife, Iroro and
Ogueji, Kelechi and
Rubungo, Andre Niyongabo and
Nguyen, Toan Q. and
M{\"u}ller, Mathias and
M{\"u}ller, Andr{\'e} and
Muhammad, Shamsuddeen Hassan and
Muhammad, Nanda and
Mnyakeni, Ayanda and
Mirzakhalov, Jamshidbek and
Matangira, Tapiwanashe and
Leong, Colin and
Lawson, Nze and
Kudugunta, Sneha and
Jernite, Yacine and
Jenny, Mathias and
Firat, Orhan and
Dossou, Bonaventure F. P. and
Dlamini, Sakhile and
de Silva, Nisansa and
{\c{C}}abuk Ball{\i}, Sakine and
Biderman, Stella and
Battisti, Alessia and
Baruwa, Ahmed and
Bapna, Ankur and
Baljekar, Pallavi and
Azime, Israel Abebe and
Awokoya, Ayodele and
Ataman, Duygu and
Ahia, Orevaoghene and
Ahia, Oghenefego and
Agrawal, Sweta and
Adeyemi, Mofetoluwa},
journal = "Transactions of the Association for Computational Linguistics",
volume = "10",
year = "2022",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2022.tacl-1.4",
doi = "10.1162/tacl_a_00447",
pages = "50--72",
abstract = "With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, Web-mined text datasets covering hundreds of languages. We manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource corpora have systematic issues: At least 15 corpora have no usable text, and a significant fraction contains less than 50{\%} sentences of acceptable quality. In addition, many are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-proficient speakers, and supplement the human audit with automatic analyses. Finally, we recommend techniques to evaluate and improve multilingual corpora and discuss potential risks that come with low-quality data releases.",
}
@inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{\'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{\'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{\"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
} | null | 59 | 9,042 | ---
license: cc0-1.0
size_categories:
- n>1T
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
task_ids:
- language-modeling
paperswithcode_id: oscar
extra_gated_prompt: "By filling the form below, you understand that only the metadata and the annotations of OSCAR 23.01 have a cc0-1.0 license, and that the rest of the content is crawled data derived from the November/December 2022 snapshot of Common Crawl, for which the authors of OSCAR **do not** hold any copyright whatsoever."
extra_gated_fields:
Name: text
Email: text
Affiliation: text
Country: text
Usecase: text
I have explicitly check with my jurisdiction and I confirm that downloading OSCAR 2301 is legal in the country/region where I am located right now, and for the use case that I have described above: checkbox
---
# Dataset Card for "OSCAR 23.01"
## IMPORTANT NOTE: THIS DATASET CARD IS STILL BEING WRITTEN, PLEASE BE PATIENT WHILE WE COMPLETE ALL THE INFORMATION ABOUT THE 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://oscar-project.org](https://oscar-project.org)
- **Repository:** [https://github.com/oscar-project](https://github.com/oscar-project)
- **Papers:** [Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://aclanthology.org/2022.lrec-1.463/), [Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data](https://arxiv.org/abs/2212.10440)
- **Point of Contact:** [Contact](https://oscar-project.org/#contact)
### Dataset Summary
The OSCAR project (**O**pen **S**uper-large **C**rawled **A**ggregated co**R**pus) is an Open Source project aiming to provide web-based multilingual resources and datasets for Machine Learning (ML) and Artificial Intelligence (AI) applications. The project focuses specifically in providing large quantities of unannotated raw data that is commonly used in the pre-training of large deep learning models. The OSCAR project has developed [high-performance data pipelines](https://github.com/oscar-corpus/ungoliant) specifically conceived to classify and filter large amounts of [web data](https://commoncrawl.org/). The project has also put special attention in improving the data quality of web-based corpora as well as providing data for low-resource languages, so that these new ML/AI technologies are accessible to as many communities as possible.
OSCAR 23.01 is the January 2023 version of the OSCAR Corpus based on the [November/December 2022 dump of Common Crawl](https://commoncrawl.org/2022/12/nov-dec-2022-crawl-archive-now-available/). While being quite similar to OSCAR 22.01, it contains several new features, including [KenLM](https://kheafield.com/code/kenlm/)-based adult content detection, precomputed [Locality-Sensitive Hashes](https://fr.wikipedia.org/wiki/Locality_sensitive_hashing) for near deduplication, and [blocklist](https://dsi.ut-capitole.fr/blacklists/index_en.php)-based categories. OSCAR 23.01 has also moved from gzip to [Zstandard compression](https://facebook.github.io/zstd/). You might already have `zstd` installed on your system, but if not, please check the [Zstandard website](https://facebook.github.io/zstd/) for installation instructions.
### Supported Tasks and Leaderboards
OSCAR is mainly intended to pretrain language models and word representations.
### Languages
All the data is distributed by language, both the original and the deduplicated versions of the data are available. 151 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR.
### Issues
OSCAR 23.01 may have quality issues on low size subcorpora, as it has been the case before.
Note that since the documents are identified as a whole, it is expected to have lines in other languages in a given language subcorpus.
As an example, it is known and expected that the German subcorpus contains documents holding lines identified as Swiss German / Alemannic.
**If you encounter something that is unexpected, please file an issue here: https://github.com/oscar-corpus/corpus/issues.**
|Language code|Language|Issues|
|-------------|--------|------|
| | | |
## Dataset Structure
We show detailed information for all the configurations of the dataset.
### Data Instances
TODO
### Layout
```js
{
"content":"English sentence\nphrase en français\n????????????", // (1)
"warc_headers":{ // (2)
"warc-identified-content-language":"fra,eng",
"warc-target-uri":"https://fr.wikipedia.org/wiki/...",
"warc-record-id":"<urn:uuid:29eaa920-d299-4b1d-b687-c72bd8d68116>",
"warc-type":"conversion",
"content-length":"35298", // (3)
"warc-refers-to":"<urn:uuid:39e42055-0d94-4e45-9c6c-9e7056635d64>",
"warc-block-digest":"sha1:WFH2A5WHCS2H365GIAFYQPI7UOAMFGHB", // (3)
"warc-date":"2022-11-26T09:45:47Z",
"content-type":"text/plain"
},
"metadata":{
"identification":{ // (4)
"label":"fr",
"prob":0.8938327
},
"harmful_pp":4063.1814, // (5)
"tlsh":"tlsh:T125315FF2B6088901EEA097015DB39B4600B...", // (6)
"quality_warnings":[ // (7)
"short_sentences",
"header",
"footer"
],
"categories":[ // (8)
"examen_pix",
"liste_bu"
],
"sentence_identifications":[ // (9)
{
"label":"fr",
"prob":0.99837273
},
{
"label":"en",
"prob":0.9992377
},
null
]
}
}
```
### Data Splits
<details>
<summary>Click to expand the number of samples per configuration</summary>
</details>
## Table
| | Code | Language | # docs | # words | Content Length : |
|----:|:-------|:-------------------------|:--------------|:----------------|:-----------------|
| 0 | af | Afrikaans | 23,994 | 6,217,024 | 37.2 MB |
| 1 | sq | Albanian | 1,342,790 | 462,694,599 | 3.2 GB |
| 2 | am | Amharic | 119,434 | 40,262,809 | 512.9 MB |
| 3 | ar | Arabic | 25,012,116 | 10,081,452,882 | 110.7 GB |
| 4 | an | Aragonese | 34 | 264 | 11.0 kB |
| 5 | hy | Armenian | 1,056,974 | 336,045,041 | 4.9 GB |
| 6 | as | Assamese | 89,542 | 24,395,215 | 412.1 MB |
| 7 | ast | Asturian | 440 | 10,917 | 74.1 kB |
| 8 | av | Avaric | 44 | 1,073 | 18.6 kB |
| 9 | az | Azerbaijani | 1,159,994 | 316,850,330 | 3.0 GB |
| 10 | bn | Bangla | 3,474,086 | 1,092,983,765 | 19.1 GB |
| 11 | ba | Bashkir | 128,248 | 26,036,637 | 363.7 MB |
| 12 | eu | Basque | 678,474 | 136,672,615 | 1.2 GB |
| 13 | be | Belarusian | 445,612 | 164,729,607 | 2.3 GB |
| 14 | bh | Bihari languages | 48 | 507 | 6.8 kB |
| 15 | bpy | Bishnupriya | 2,346 | 346,947 | 5.4 MB |
| 16 | bs | Bosnian | 20 | 395 | 3.0 kB |
| 17 | br | Breton | 36,338 | 4,759,407 | 31.4 MB |
| 18 | bg | Bulgarian | 8,933,998 | 3,635,273,738 | 44.1 GB |
| 19 | my | Burmese | 430,276 | 82,433,836 | 3.0 GB |
| 20 | ca | Catalan | 6,953,898 | 2,240,460,836 | 15.3 GB |
| 21 | ceb | Cebuano | 16,174 | 6,263,404 | 41.1 MB |
| 22 | ckb | Central Kurdish | 182,508 | 61,334,746 | 772.9 MB |
| 23 | ce | Chechen | 11,686 | 1,051,752 | 13.9 MB |
| 24 | zh | Chinese | 138,478,270 | 44,378,380,161 | 1.4 TB |
| 25 | cv | Chuvash | 16,652 | 3,039,925 | 42.3 MB |
| 26 | kw | Cornish | 8 | 80 | 432 Bytes |
| 27 | hr | Croatian | 31,808 | 3,542,961 | 26.5 MB |
| 28 | cs | Czech | 34,859,632 | 9,717,378,559 | 77.0 GB |
| 29 | da | Danish | 7,214,338 | 2,217,634,340 | 14.8 GB |
| 30 | dv | Divehi | 77,060 | 10,655,359 | 200.1 MB |
| 31 | nl | Dutch | 72,552,688 | 19,564,553,306 | 135.0 GB |
| 32 | mhr | Eastern Mari | 9,502 | 1,615,215 | 22.9 MB |
| 33 | arz | Egyptian Arabic | 3,958 | 385,511 | 3.7 MB |
| 34 | en | English | 1,235,510,986 | 523,869,288,690 | 3.4 TB |
| 35 | eo | Esperanto | 226,924 | 67,774,923 | 474.8 MB |
| 36 | et | Estonian | 3,601,904 | 938,296,892 | 8.0 GB |
| 37 | tl | Filipino | 250,558 | 110,560,444 | 719.2 MB |
| 38 | fi | Finnish | 14,471,710 | 4,198,143,883 | 41.1 GB |
| 39 | fr | French | 158,334,998 | 62,127,088,294 | 430.5 GB |
| 40 | gl | Galician | 248,762 | 38,345,625 | 255.7 MB |
| 41 | ka | Georgian | 1,343,036 | 373,935,158 | 8.4 GB |
| 42 | de | German | 206,598,430 | 73,848,586,648 | 594.7 GB |
| 43 | gom | Goan Konkani | 398 | 121,035 | 2.3 MB |
| 44 | el | Greek | 20,282,864 | 7,691,622,692 | 95.7 GB |
| 45 | gn | Guarani | 14 | 260 | 2.2 kB |
| 46 | gu | Gujarati | 425,552 | 417,001,705 | 5.6 GB |
| 47 | ht | Haitian Creole | 2 | 20,671 | 93.1 kB |
| 48 | he | Hebrew | 3,997,888 | 1,697,158,891 | 18.0 GB |
| 49 | hi | Hindi | 5,514,454 | 2,475,605,444 | 32.6 GB |
| 50 | hu | Hungarian | 21,349,372 | 16,013,364,289 | 150.1 GB |
| 51 | is | Icelandic | 1,210,232 | 294,471,539 | 2.2 GB |
| 52 | io | Ido | 224 | 2,598 | 16.1 kB |
| 53 | ilo | Iloko | 144 | 4,411 | 28.0 kB |
| 54 | id | Indonesian | 7,109,778 | 3,228,020,221 | 23.4 GB |
| 55 | ia | Interlingua | 34 | 9,384 | 33.5 kB |
| 56 | ie | Interlingue | 2 | 0 | 881 Bytes |
| 57 | ga | Irish | 29,894 | 9,054,923 | 63.2 MB |
| 58 | it | Italian | 89,021,606 | 36,327,274,203 | 259.4 GB |
| 59 | ja | Japanese | 94,236,404 | 4,401,059,165 | 181.2 GB |
| 60 | jv | Javanese | 172 | 3,286 | 25.7 kB |
| 61 | xal | Kalmyk | 2 | 27 | 315 Bytes |
| 62 | kn | Kannada | 448,500 | 124,924,350 | 2.6 GB |
| 63 | krc | Karachay-Balkar | 496 | 8,385 | 122.4 kB |
| 64 | kk | Kazakh | 677,622 | 214,679,857 | 3.3 GB |
| 65 | km | Khmer | 450,660 | 59,880,231 | 3.2 GB |
| 66 | kv | Komi | 460 | 5,909 | 70.3 kB |
| 67 | ko | Korean | 15,147,698 | 3,435,866,935 | 38.1 GB |
| 68 | ku | Kurdish | 80,338 | 25,921,607 | 174.1 MB |
| 69 | ky | Kyrgyz | 144,288 | 32,062,783 | 489.3 MB |
| 70 | lo | Lao | 118,374 | 10,659,203 | 472.1 MB |
| 71 | la | Latin | 14,384 | 307,865 | 2.0 MB |
| 72 | lv | Latvian | 2,435,882 | 845,459,899 | 7.4 GB |
| 73 | lez | Lezghian | 676 | 60,634 | 856.6 kB |
| 74 | li | Limburgish | 6 | 169 | 1.4 kB |
| 75 | lt | Lithuanian | 5,182,028 | 1,674,362,574 | 14.5 GB |
| 76 | jbo | Lojban | 572 | 312,315 | 1.5 MB |
| 77 | lmo | Lombard | 112 | 3,269 | 21.0 kB |
| 78 | nds | Low German | 5,248 | 1,612,175 | 10.7 MB |
| 79 | dsb | Lower Sorbian | 8 | 84 | 664 Bytes |
| 80 | lb | Luxembourgish | 18,090 | 2,514,838 | 18.4 MB |
| 81 | mk | Macedonian | 1,063,298 | 389,344,425 | 4.7 GB |
| 82 | mai | Maithili | 46 | 467 | 6.8 kB |
| 83 | mg | Malagasy | 10,830 | 1,416,430 | 11.2 MB |
| 84 | ms | Malay | 11,500 | 238,477 | 2.6 MB |
| 85 | ml | Malayalam | 800,936 | 236,597,838 | 5.8 GB |
| 86 | mt | Maltese | 5,180 | 149,886 | 1.3 MB |
| 87 | mr | Marathi | 729,578 | 252,706,331 | 4.5 GB |
| 88 | mzn | Mazanderani | 384 | 16,115 | 169.2 kB |
| 89 | min | Minangkabau | 2,436 | 305,589 | 3.8 MB |
| 90 | xmf | Mingrelian | 7,318 | 283,316 | 6.1 MB |
| 91 | mwl | Mirandese | 4 | 54 | 423 Bytes |
| 92 | mn | Mongolian | 1,061,710 | 454,350,415 | 5.8 GB |
| 93 | multi | **Multilingual** | 2,948,202 | 1,251,676,406 | 11.9 GB |
| 94 | nah | Nahuatl languages | 38 | 279 | 2.4 kB |
| 95 | ne | Nepali | 1,152,156 | 278,901,036 | 4.9 GB |
| 96 | new | Newari | 1,996 | 229,703 | 4.0 MB |
| 97 | no | Norwegian | 2,797,378 | 373,160,033 | 2.6 GB |
| 98 | nn | Norwegian Nynorsk | 19,470 | 575,518 | 3.7 MB |
| 99 | oc | Occitan | 920 | 34,701 | 405.0 kB |
| 100 | or | Odia | 158,426 | 31,963,340 | 543.1 MB |
| 101 | os | Ossetic | 8,628 | 3,935,964 | 50.7 MB |
| 102 | ps | Pashto | 87,408 | 30,196,179 | 261.6 MB |
| 103 | fa | Persian | 23,813,882 | 9,609,206,698 | 93.2 GB |
| 104 | pms | Piedmontese | 2,524 | 510,087 | 3.1 MB |
| 105 | pl | Polish | 57,184,826 | 18,073,705,588 | 147.1 GB |
| 106 | pt | Portuguese | 36,062,800 | 15,172,557,311 | 105.0 GB |
| 107 | pa | Punjabi | 222,058 | 104,235,418 | 1.4 GB |
| 108 | qu | Quechua | 2 | 13 | 143 Bytes |
| 109 | ro | Romanian | 11,985,668 | 6,302,600,833 | 45.6 GB |
| 110 | bxr | Russia Buriat | 72 | 698 | 8.2 kB |
| 111 | ru | Russian | 194,143,422 | 78,032,029,344 | 1.1 TB |
| 112 | sah | Sakha | 17,566 | 4,288,051 | 68.8 MB |
| 113 | sa | Sanskrit | 16,802 | 2,479,345 | 56.3 MB |
| 114 | gd | Scottish Gaelic | 776 | 18,458 | 146.1 kB |
| 115 | sr | Serbian | 1,677,896 | 632,781,822 | 7.7 GB |
| 116 | sh | Serbian (Latin) | 3,214 | 166,517 | 816.4 kB |
| 117 | sd | Sindhi | 48,566 | 14,667,207 | 131.6 MB |
| 118 | si | Sinhala | 301,066 | 172,755,385 | 2.6 GB |
| 119 | sk | Slovak | 8,931,784 | 2,704,716,280 | 21.5 GB |
| 120 | sl | Slovenian | 1,112,560 | 192,816,743 | 1.4 GB |
| 121 | so | Somali | 6 | 51 | 503 Bytes |
| 122 | azb | South Azerbaijani | 26,364 | 2,029,729 | 28.4 MB |
| 123 | es | Spanish | 153,574,556 | 63,388,237,965 | 429.9 GB |
| 124 | su | Sundanese | 18 | 258 | 2.0 kB |
| 125 | sw | Swahili | 1,664 | 164,459 | 1.0 MB |
| 126 | sv | Swedish | 21,891,348 | 6,993,719,601 | 50.0 GB |
| 127 | gsw | Swiss German | 342 | 34,328 | 232.7 kB |
| 128 | tg | Tajik | 144,932 | 76,987,285 | 1.0 GB |
| 129 | ta | Tamil | 1,638,238 | 738,824,392 | 15.8 GB |
| 130 | tt | Tatar | 262,654 | 59,253,765 | 833.8 MB |
| 131 | te | Telugu | 644,712 | 201,575,815 | 3.9 GB |
| 132 | th | Thai | 14,845,900 | 2,224,483,018 | 92.0 GB |
| 133 | bo | Tibetan | 62,352 | 6,062,558 | 531.6 MB |
| 134 | tr | Turkish | 26,654,330 | 8,290,890,087 | 73.7 GB |
| 135 | tk | Turkmen | 4,576 | 325,786 | 3.3 MB |
| 136 | uk | Ukrainian | 10,059,992 | 3,183,842,018 | 44.7 GB |
| 137 | x-eml | Emiliano-Romagnol | 4 | 329 | 1.8 kB |
| 138 | hsb | Upper Sorbian | 402 | 15,827 | 123.2 kB |
| 139 | ur | Urdu | 887,004 | 434,023,273 | 3.8 GB |
| 140 | ug | Uyghur | 51,304 | 14,659,554 | 219.8 MB |
| 141 | uz | Uzbek | 15,806 | 1,665,960 | 15.3 MB |
| 142 | vi | Vietnamese | 33,933,994 | 22,424,984,210 | 140.8 GB |
| 143 | vo | Volapük | 896 | 49,968 | 371.9 kB |
| 144 | wa | Walloon | 390 | 6,347 | 34.3 kB |
| 145 | war | Waray | 1,494 | 19,665 | 126.8 kB |
| 146 | cy | Welsh | 151,512 | 52,250,043 | 333.0 MB |
| 147 | fy | Western Frisian | 45,458 | 9,885,788 | 70.4 MB |
| 148 | mrj | Western Mari | 496 | 60,180 | 765.8 kB |
| 149 | pnb | Western Panjabi | 12,904 | 11,844,695 | 105.8 MB |
| 150 | wuu | Wu Chinese | 136 | 1,199 | 26.8 kB |
| 151 | yi | Yiddish | 47,438 | 14,287,370 | 171.7 MB |
| 152 | yo | Yoruba | 128 | 2,396 | 16.6 kB |
## Dataset Creation
### Curation Rationale
OSCAR was constructed using [`Ungoliant`](https://github.com/oscar-corpus/ungoliant), a new pipeline derived from [goclassy](https://github.com/oscar-corpus/goclassy), itself being derived from [fastText's one](https://github.com/facebookresearch/fastText).
The pipeline works on documents rather than lines.
`Ungoliant` is implemented in the [Rust programming language](https://rust-lang.org), and uses [rayon](https://github.com/rayon-rs/rayon) as its data parallelism strategy.
Threading is done at shard, record and sentence level, making the whole generation process much more efficient.
Filtering will be explained in a future blog post at our [website](https://oscar-corpus.com)
### Source Data
#### Initial Data Collection and Normalization
[Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies.
Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics.
To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR 22.01, the **November/December 2021** snapshot was used. It is composed by 64 000 compressed text files containing documents and their headers.
#### Who are the source language producers?
The data comes from multiple web pages in a large variety of languages.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models.
## Considerations for Using the Data
### Social Impact of Dataset
OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures.
### Discussion of Biases
OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models.
### Other Known Limitations
The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571).
## Additional Information
### Dataset Curators
This release of OSCAR was made possible by [Julien Abadji](https://ujj.space), [Pedro Ortiz Suarez](https://portizs.eu/), [Rua Ismail](https://oscar-project.org/authors/rua/), [Sotaro Takeshita](https://sotaro.io/about), [Sebastian Nagel](https://www.polver.uni-konstanz.de/cnc/people/nagel/) and [Benoit Sagot](http://pauillac.inria.fr/~sagot/).
### Licensing Information
These data are released under this licensing scheme
We do not own any of the text from which these data has been extracted.
We license the actual packaging, the metadata and the annotations of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
To the extent possible under law, the OSCAR project, Inria, the Univertity of Mannheim and DFKI GmbH have waived all copyright and related or neighboring rights to OSCAR
This work is published from: France and Germany.
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
```
@ARTICLE{2022arXiv221210440J,
author = {{Jansen}, Tim and {Tong}, Yangling and {Zevallos}, Victoria and {Ortiz Suarez}, Pedro},
title = "{Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2022,
month = dec,
eid = {arXiv:2212.10440},
pages = {arXiv:2212.10440},
doi = {10.48550/arXiv.2212.10440},
archivePrefix = {arXiv},
eprint = {2212.10440},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv221210440J},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{abadji-etal-2022-towards,
title = "Towards a Cleaner Document-Oriented Multilingual Crawled Corpus",
author = "Abadji, Julien and
Ortiz Suarez, Pedro and
Romary, Laurent and
Sagot, Beno{\^\i}t",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.463",
pages = "4344--4355",
abstract = "The need for large corpora raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing. And while there have been some recent attempts to manually curate the amount of data necessary to train large language models, the main way to obtain this data is still through automatic web crawling. In this paper we take the existing multilingual web corpus OSCAR and its pipeline Ungoliant that extracts and classifies data from Common Crawl at the line level, and propose a set of improvements and automatic annotations in order to produce a new document-oriented version of OSCAR that could prove more suitable to pre-train large generative language models as well as hopefully other applications in Natural Language Processing and Digital Humanities.",
}
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
author = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Beno{\^i}t Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{\"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.},
language = {en}
}
@article{kreutzer-etal-2022-quality,
title = "Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets",
author = {Kreutzer, Julia and
Caswell, Isaac and
Wang, Lisa and
Wahab, Ahsan and
van Esch, Daan and
Ulzii-Orshikh, Nasanbayar and
Tapo, Allahsera and
Subramani, Nishant and
Sokolov, Artem and
Sikasote, Claytone and
Setyawan, Monang and
Sarin, Supheakmungkol and
Samb, Sokhar and
Sagot, Beno{\^\i}t and
Rivera, Clara and
Rios, Annette and
Papadimitriou, Isabel and
Osei, Salomey and
Suarez, Pedro Ortiz and
Orife, Iroro and
Ogueji, Kelechi and
Rubungo, Andre Niyongabo and
Nguyen, Toan Q. and
M{\"u}ller, Mathias and
M{\"u}ller, Andr{\'e} and
Muhammad, Shamsuddeen Hassan and
Muhammad, Nanda and
Mnyakeni, Ayanda and
Mirzakhalov, Jamshidbek and
Matangira, Tapiwanashe and
Leong, Colin and
Lawson, Nze and
Kudugunta, Sneha and
Jernite, Yacine and
Jenny, Mathias and
Firat, Orhan and
Dossou, Bonaventure F. P. and
Dlamini, Sakhile and
de Silva, Nisansa and
{\c{C}}abuk Ball{\i}, Sakine and
Biderman, Stella and
Battisti, Alessia and
Baruwa, Ahmed and
Bapna, Ankur and
Baljekar, Pallavi and
Azime, Israel Abebe and
Awokoya, Ayodele and
Ataman, Duygu and
Ahia, Orevaoghene and
Ahia, Oghenefego and
Agrawal, Sweta and
Adeyemi, Mofetoluwa},
journal = "Transactions of the Association for Computational Linguistics",
volume = "10",
year = "2022",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/2022.tacl-1.4",
doi = "10.1162/tacl_a_00447",
pages = "50--72",
abstract = "With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, Web-mined text datasets covering hundreds of languages. We manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource corpora have systematic issues: At least 15 corpora have no usable text, and a significant fraction contains less than 50{\%} sentences of acceptable quality. In addition, many are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-proficient speakers, and supplement the human audit with automatic analyses. Finally, we recommend techniques to evaluate and improve multilingual corpora and discuss potential risks that come with low-quality data releases.",
}
@inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
}
```
|
ought/raft | 2022-10-25T09:54:19.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"source_datasets:extended|ade_corpus_v2",
"source_datasets:extended|banking77",
"language:en",
"license:other",
"arxiv:2109.14076",
"region:us"
] | ought | Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants?
[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:
- across multiple domains (lit review, tweets, customer interaction, etc.)
- on economically valuable classification tasks (someone inherently cares about the task)
- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set) | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | null | 31 | 8,956 | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
- extended|ade_corpus_v2
- extended|banking77
task_categories:
- text-classification
task_ids:
- multi-class-classification
pretty_name: 'Real-world Annotated Few-shot Tasks: RAFT'
language_bcp47:
- en-US
---
# Dataset Card for RAFT
## 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://raft.elicit.org
- **Repository:** https://huggingface.co/datasets/ought/raft
- **Paper:** [arxiv.org](https://arxiv.org/abs/2109.14076)
- **Leaderboard:** https://huggingface.co/spaces/ought/raft-leaderboard
- **Point of Contact:** [Eli Lifland](eli.d.lifland@gmail.com)
### Dataset Summary
The Real-world Annotated Few-shot Tasks (RAFT) dataset is an aggregation of English-language datasets found in the real world. Associated with each dataset is a binary or multiclass classification task, intended to improve our understanding of how language models perform on tasks that have concrete, real-world value. Only 50 labeled examples are provided in each dataset.
### Supported Tasks and Leaderboards
- `text-classification`: Each subtask in RAFT is a text classification task, and the provided train and test sets can be used to submit to the [RAFT Leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard) To prevent overfitting and tuning on a held-out test set, the leaderboard is only evaluated once per week. Each task has its macro-f1 score calculated, then those scores are averaged to produce the overall leaderboard score.
### Languages
RAFT is entirely in American English (en-US).
## Dataset Structure
### Data Instances
| Dataset | First Example |
| ----------- | ----------- |
| Ade Corpus V2 | <pre>Sentence: No regional side effects were noted.<br>ID: 0<br>Label: 2</pre> |
| Banking 77 | <pre>Query: Is it possible for me to change my PIN number?<br>ID: 0<br>Label: 23<br></pre> |
| NeurIPS Impact Statement Risks | <pre>Paper title: Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation...<br>Paper link: https://proceedings.neurips.cc/paper/2020/file/ec1f764517b7ffb52057af6df18142b7-Paper.pdf...<br>Impact statement: This work makes the first attempt to search for all key components of panoptic pipeline and manages to accomplish this via the p...<br>ID: 0<br>Label: 1</pre> |
| One Stop English | <pre>Article: For 85 years, it was just a grey blob on classroom maps of the solar system. But, on 15 July, Pluto was seen in high resolution ...<br>ID: 0<br>Label: 3<br></pre> |
| Overruling | <pre>Sentence: in light of both our holding today and previous rulings in johnson, dueser, and gronroos, we now explicitly overrule dupree....<br>ID: 0<br>Label: 2<br></pre> |
| Semiconductor Org Types | <pre>Paper title: 3Gb/s AC-coupled chip-to-chip communication using a low-swing pulse receiver...<br>Organization name: North Carolina State Univ.,Raleigh,NC,USA<br>ID: 0<br>Label: 3<br></pre> |
| Systematic Review Inclusion | <pre>Title: Prototyping and transforming facial textures for perception research...<br>Abstract: Wavelet based methods for prototyping facial textures for artificially transforming the age of facial images were described. Pro...<br>Authors: Tiddeman, B.; Burt, M.; Perrett, D.<br>Journal: IEEE Comput Graphics Appl<br>ID: 0<br>Label: 2</pre> |
| TAI Safety Research | <pre>Title: Malign generalization without internal search<br>Abstract Note: In my last post, I challenged the idea that inner alignment failures should be explained by appealing to agents which perform ex...<br>Url: https://www.alignmentforum.org/posts/ynt9TD6PrYw6iT49m/malign-generalization-without-internal-search...<br>Publication Year: 2020<br>Item Type: blogPost<br>Author: Barnett, Matthew<br>Publication Title: AI Alignment Forum<br>ID: 0<br>Label: 1</pre> |
| Terms Of Service | <pre>Sentence: Crowdtangle may change these terms of service, as described above, notwithstanding any provision to the contrary in any agreemen...<br>ID: 0<br>Label: 2<br></pre> |
| Tweet Eval Hate | <pre>Tweet: New to Twitter-- any men on here know what the process is to get #verified?...<br>ID: 0<br>Label: 2<br></pre> |
| Twitter Complaints | <pre>Tweet text: @HMRCcustomers No this is my first job<br>ID: 0<br>Label: 2</pre> |
### Data Fields
The ID field is used for indexing data points. It will be used to match your submissions with the true test labels, so you must include it in your submission. All other columns contain textual data. Some contain links and URLs to websites on the internet.
All output fields are designated with the "Label" column header. The 0 value in this column indicates that the entry is unlabeled, and should only appear in the unlabeled test set. Other values in this column are various other labels. To get their textual value for a given dataset:
```
# Load the dataset
dataset = datasets.load_dataset("ought/raft", "ade_corpus_v2")
# First, get the object that holds information about the "Label" feature in the dataset.
label_info = dataset.features["Label"]
# Use the int2str method to access the textual labels.
print([label_info.int2str(i) for i in (0, 1, 2)])
# ['Unlabeled', 'ADE-related', 'not ADE-related']
```
### Data Splits
There are two splits provided: train data and unlabeled test data.
The training examples were chosen at random. No attempt was made to ensure that classes were balanced or proportional in the training data -- indeed, the Banking 77 task with 77 different classes if used cannot fit all of its classes into the 50 training examples.
| Dataset | Train Size | Test Size | |
|--------------------------------|------------|-----------|---|
| Ade Corpus V2 | 50 | 5000 | |
| Banking 77 | 50 | 5000 | |
| NeurIPS Impact Statement Risks | 50 | 150 | |
| One Stop English | 50 | 516 | |
| Overruling | 50 | 2350 | |
| Semiconductor Org Types | 50 | 449 | |
| Systematic Review Inclusion | 50 | 2243 | |
| TAI Safety Research | 50 | 1639 | |
| Terms Of Service | 50 | 5000 | |
| Tweet Eval Hate | 50 | 2966 | |
| Twitter Complaints | 50 | 3399 | |
| **Total** | **550** | **28712** | |
## Dataset Creation
### Curation Rationale
Generally speaking, the rationale behind RAFT was to create a benchmark for evaluating NLP models that didn't consist of contrived or artificial data sources, for which the tasks weren't originally assembled for the purpose of testing NLP models. However, each individual dataset in RAFT was collected independently. For the majority of datasets, we only collected them second-hand from existing curated sources. The datasets that we curated are:
* NeurIPS impact statement risks
* Semiconductor org types
* TAI Safety Research
Each of these three datasets was sourced from our existing collaborators at Ought. They had used our service, Elicit, to analyze their dataset in the past, and we contact them to include their dataset and the associated classification task in the benchmark. For all datasets, more information is provided in our paper. For the ones which we did not curate, we provide a link to the dataset. For the ones which we did, we provide a datasheet that elaborates on many of the topics here in greater detail.
For the three datasets that we introduced:
* **NeurIPS impact statement risks** The dataset was created to evaluate the then new requirement for authors to include an "impact statement" in their 2020 NeurIPS papers. Had it been successful? What kind of things did authors mention the most? How long were impact statements on average? Etc.
* **Semiconductor org types** The dataset was originally created to understand better which countries’ organisations have contributed most to semiconductor R\&D over the past 25 years using three main conferences. Moreover, to estimate the share of academic and private sector contributions, the organisations were classified as “university”, “research institute” or “company”.
* **TAI Safety Research** The primary motivations for assembling this database were to: (1) Aid potential donors in assessing organizations focusing on TAI safety by collecting and analyzing their research output. (2) Assemble a comprehensive bibliographic database that can be used as a base for future projects, such as a living review of the field.
**For the following sections, we will only describe the datasets we introduce. All other dataset details, and more details on the ones described here, can be found in our paper.**
### Source Data
#### Initial Data Collection and Normalization
* **NeurIPS impact statement risks** The data was directly observable (raw text scraped) for the most part; although some data was taken from previous datasets (which themselves had taken it from raw text). The data was validated, but only in part, by human reviewers. Cf this link for full details:
* **Semiconductor org types** We used the IEEE API to obtain institutions that contributed papers to semiconductor conferences in the last 25 years. This is a random sample of 500 of them with a corresponding conference paper title. The three conferences were the International Solid-State Circuits Conference (ISSCC), the Symposia on VLSI Technology and Circuits (VLSI) and the International Electron Devices Meeting (IEDM).
* **TAI Safety Research** We asked TAI safety organizations for what their employees had written, emailed some individual authors, and searched Google Scholar. See the LessWrong post for more details: https://www.lesswrong.com/posts/4DegbDJJiMX2b3EKm/tai-safety-bibliographic-database
#### Who are the source language producers?
* **NeurIPS impact statement risks** Language generated from NeurIPS 2020 impact statement authors, generally the authors of submission papers.
* **Semiconductor org types** Language generated from IEEE API. Generally machine-formatted names, and title of academic papers.
* **TAI Safety Research** Language generated by authors of TAI safety research publications.
### Annotations
#### Annotation process
* **NeurIPS impact statement risks** Annotations were entered directly into a Google Spreadsheet with instructions, labeled training examples, and unlabeled testing examples.
* **Semiconductor org types** Annotations were entered directly into a Google Spreadsheet with instructions, labeled training examples, and unlabeled testing examples.
* **TAI Safety Research** N/A
#### Who are the annotators?
* **NeurIPS impact statement risks** Contractors paid by Ought performed the labeling of whether impact statements mention harmful applications. A majority vote was taken from 3 annotators.
* **Semiconductor org types** Contractors paid by Ought performed the labeling of organization types. A majority vote was taken from 3 annotators.
* **TAI Safety Research** The dataset curators annotated the dataset by hand.
### Personal and Sensitive Information
It is worth mentioning that the Tweet Eval Hate, by necessity, contains highly offensive content.
* **NeurIPS impact statement risks** The dataset contains authors' names. These were scraped from publicly available scientific papers submitted to NeurIPS 2020.
* **Semiconductor org types** N/A
* **TAI Safety Research** N/A
## Considerations for Using the Data
### Social Impact of Dataset
* **NeurIPS impact statement risks** N/A
* **Semiconductor org types** N/A
* **TAI Safety Research** N/A
### Discussion of Biases
* **NeurIPS impact statement risks** N/A
* **Semiconductor org types** N/A
* **TAI Safety Research** N/A
### Other Known Limitations
* **NeurIPS impact statement risks** This dataset has limitations that should be taken into consideration when using it. In particular, the method used to collect broader impact statements involved automated downloads, conversions and scraping and was not error-proof. Although care has been taken to identify and correct as many errors as possible, not all texts have been reviewed by a human. This means it is possible some of the broader impact statements contained in the dataset are truncated or otherwise incorrectly extracted from their original article.
* **Semiconductor org types** N/A
* **TAI Safety Research** Don't use it to create a dangerous AI that could bring the end of days.
## Additional Information
### Dataset Curators
The overall RAFT curators are Neel Alex, Eli Lifland, and Andreas Stuhlmüller.
* **NeurIPS impact statement risks** Volunteers working with researchers affiliated to Oxford's Future of Humanity Institute (Carolyn Ashurst, now at The Alan Turing Institute) created the impact statements dataset.
* **Semiconductor org types** The data science unit of Stiftung Neue Verantwortung (Berlin).
* **TAI Safety Research** Angelica Deibel and Jess Riedel. We did not do it on behalf of any entity.
### Licensing Information
RAFT aggregates many other datasets, each of which is provided under its own license. Generally, those licenses permit research and commercial use.
| Dataset | License |
| ----------- | ----------- |
| Ade Corpus V2 | Unlicensed |
| Banking 77 | CC BY 4.0 |
| NeurIPS Impact Statement Risks | MIT License/CC BY 4.0 |
| One Stop English | CC BY-SA 4.0 |
| Overruling | Unlicensed |
| Semiconductor Org Types | CC BY-NC 4.0 |
| Systematic Review Inclusion | CC BY 4.0 |
| TAI Safety Research | CC BY-SA 4.0 |
| Terms Of Service | Unlicensed |
| Tweet Eval Hate | Unlicensed |
| Twitter Complaints | Unlicensed |
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@neel-alex](https://github.com/neel-alex), [@uvafan](https://github.com/uvafan), and [@lewtun](https://github.com/lewtun) for adding this dataset. |
math_qa | 2023-04-05T10:09:35.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|aqua_rat",
"language:en",
"license:apache-2.0",
"region:us"
] | null | Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset. AQuA-RAT has provided the questions, options, rationale, and the correct options. | null | 38 | 8,911 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
- expert-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: MathQA
size_categories:
- 10K<n<100K
source_datasets:
- extended|aqua_rat
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mathqa
dataset_info:
features:
- name: Problem
dtype: string
- name: Rationale
dtype: string
- name: options
dtype: string
- name: correct
dtype: string
- name: annotated_formula
dtype: string
- name: linear_formula
dtype: string
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num_examples: 29837
- name: validation
num_bytes: 2752969
num_examples: 4475
download_size: 7302821
dataset_size: 22965979
---
# Dataset Card for MathQA
## 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://math-qa.github.io/math-QA/](https://math-qa.github.io/math-QA/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://aclanthology.org/N19-1245/)
- **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:** 7.30 MB
- **Size of the generated dataset:** 22.96 MB
- **Total amount of disk used:** 30.27 MB
### Dataset Summary
We introduce a large-scale dataset of math word problems.
Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset with fully-specified operational programs.
AQuA-RAT has provided the questions, options, rationale, and the correct options.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 7.30 MB
- **Size of the generated dataset:** 22.96 MB
- **Total amount of disk used:** 30.27 MB
An example of 'train' looks as follows.
```
{
"Problem": "a multiple choice test consists of 4 questions , and each question has 5 answer choices . in how many r ways can the test be completed if every question is unanswered ?",
"Rationale": "\"5 choices for each of the 4 questions , thus total r of 5 * 5 * 5 * 5 = 5 ^ 4 = 625 ways to answer all of them . answer : c .\"",
"annotated_formula": "power(5, 4)",
"category": "general",
"correct": "c",
"linear_formula": "power(n1,n0)|",
"options": "a ) 24 , b ) 120 , c ) 625 , d ) 720 , e ) 1024"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `Problem`: a `string` feature.
- `Rationale`: a `string` feature.
- `options`: a `string` feature.
- `correct`: a `string` feature.
- `annotated_formula`: a `string` feature.
- `linear_formula`: a `string` feature.
- `category`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|29837| 4475|2985|
## 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
The dataset is licensed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@inproceedings{amini-etal-2019-mathqa,
title = "{M}ath{QA}: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms",
author = "Amini, Aida and
Gabriel, Saadia and
Lin, Shanchuan and
Koncel-Kedziorski, Rik and
Choi, Yejin and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1245",
doi = "10.18653/v1/N19-1245",
pages = "2357--2367",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | |
SetFit/emotion | 2022-04-03T20:47:37.000Z | [
"region:us"
] | SetFit | null | null | null | 11 | 8,885 | ** Attention: There appears an overlap in train / test. I trained a model on the train set and achieved 100% acc on test set. With the original emotion dataset this is not the case (92.4% acc)** |
lighteval/mmlu | 2023-06-09T16:36:19.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2009.03300",
"arxiv:2005.00700",
"arxiv:2005.14165",
"arxiv:2008.02275",
"region:us"
] | lighteval | This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge, covering 57 tasks including elementary mathematics, US history, computer science, law, and more. | @article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
} | null | 6 | 8,800 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mmlu
pretty_name: Measuring Massive Multitask Language Understanding
language_bcp47:
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---
# Dataset Card for MMLU
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository**: https://github.com/hendrycks/test
- **Paper**: https://arxiv.org/abs/2009.03300
### Dataset Summary
[Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300) by [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/), [Collin Burns](http://collinpburns.com), [Steven Basart](https://stevenbas.art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021).
This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability.
A complete list of tasks: ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions']
### Supported Tasks and Leaderboards
| Model | Authors | Humanities | Social Science | STEM | Other | Average |
|------------------------------------|----------|:-------:|:-------:|:-------:|:-------:|:-------:|
| [UnifiedQA](https://arxiv.org/abs/2005.00700) | Khashabi et al., 2020 | 45.6 | 56.6 | 40.2 | 54.6 | 48.9
| [GPT-3](https://arxiv.org/abs/2005.14165) (few-shot) | Brown et al., 2020 | 40.8 | 50.4 | 36.7 | 48.8 | 43.9
| [GPT-2](https://arxiv.org/abs/2005.14165) | Radford et al., 2019 | 32.8 | 33.3 | 30.2 | 33.1 | 32.4
| Random Baseline | N/A | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0
### Languages
English
## Dataset Structure
### Data Instances
An example from anatomy subtask looks as follows:
```
{
"question": "What is the embryological origin of the hyoid bone?",
"choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The second and third pharyngeal arches"],
"answer": "D"
}
```
### Data Fields
- `question`: a string feature
- `choices`: a list of 4 string features
- `answer`: a ClassLabel feature
### Data Splits
- `auxiliary_train`: auxiliary multiple-choice training questions from ARC, MC_TEST, OBQA, RACE, etc.
- `dev`: 5 examples per subtask, meant for few-shot setting
- `test`: there are at least 100 examples per subtask
| | auxiliary_train | dev | val | test |
| ----- | :------: | :-----: | :-----: | :-----: |
| TOTAL | 99842 | 285 | 1531 | 14042
## Dataset Creation
### Curation Rationale
Transformer models have driven this recent progress by pretraining on massive text corpora, including all of Wikipedia, thousands of books, and numerous websites. These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a diverse set of subjects that humans learn.
### 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
[MIT License](https://github.com/hendrycks/test/blob/master/LICENSE)
### Citation Information
If you find this useful in your research, please consider citing the test and also the [ETHICS](https://arxiv.org/abs/2008.02275) dataset it draws from:
```
@article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
}
@article{hendrycks2021ethics,
title={Aligning AI With Shared Human Values},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
}
```
### Contributions
Thanks to [@andyzoujm](https://github.com/andyzoujm) for adding this dataset.
|
mteb/tatoeba-bitext-mining | 2022-09-27T19:07:02.000Z | [
"language:eng",
"language:sqi",
"language:fry",
"language:kur",
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"language:urd",
"language:ara",
"language:kor",
"language:yid",
"language:fin",
"language:tha",
"language:wuu",
"region:us"
] | mteb | Tatoeba multilingual test set | null | null | 3 | 8,762 | ---
language:
- eng
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- por
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- war
- aze
- vie
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- cha
- mhr
- dan
- ell
- amh
- pam
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- dsb
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- tgl
- ast
- mkd
- khm
- ces
- tzl
- urd
- ara
- kor
- yid
- fin
- tha
- wuu
--- |
juletxara/xstory_cloze | 2023-05-21T16:04:36.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|story_cloze",
"language:en",
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"language:ar",
"language:hi",
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"language:te",
"language:sw",
"language:eu",
"language:my",
"license:cc-by-sa-4.0",
"arxiv:2112.10668",
"region:us"
] | juletxara | XStoryCloze consists of the professionally translated version of the [English StoryCloze dataset](https://cs.rochester.edu/nlp/rocstories/) (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI. | @article{DBLP:journals/corr/abs-2112-10668,
author = {Xi Victoria Lin and
Todor Mihaylov and
Mikel Artetxe and
Tianlu Wang and
Shuohui Chen and
Daniel Simig and
Myle Ott and
Naman Goyal and
Shruti Bhosale and
Jingfei Du and
Ramakanth Pasunuru and
Sam Shleifer and
Punit Singh Koura and
Vishrav Chaudhary and
Brian O'Horo and
Jeff Wang and
Luke Zettlemoyer and
Zornitsa Kozareva and
Mona T. Diab and
Veselin Stoyanov and
Xian Li},
title = {Few-shot Learning with Multilingual Language Models},
journal = {CoRR},
volume = {abs/2112.10668},
year = {2021},
url = {https://arxiv.org/abs/2112.10668},
eprinttype = {arXiv},
eprint = {2112.10668},
timestamp = {Tue, 04 Jan 2022 15:59:27 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2112-10668.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | null | 3 | 8,732 | ---
annotations_creators:
- found
language:
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license:
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paperswithcode_id: null
pretty_name: XStoryCloze
size_categories:
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source_datasets:
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tags: []
task_categories:
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task_ids: []
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---
# Dataset Card for XStoryCloze
## 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://cs.rochester.edu/nlp/rocstories/](https://cs.rochester.edu/nlp/rocstories/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Few-shot Learning with Multilingual Generative Language Models](https://arxiv.org/pdf/2112.10668.pdf)
- **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:** 2.03 MB
- **Size of the generated dataset:** 2.03 MB
- **Total amount of disk used:** 2.05 MB
### Dataset Summary
XStoryCloze consists of the professionally translated version of the [English StoryCloze dataset](https://cs.rochester.edu/nlp/rocstories/) (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI.
### Supported Tasks and Leaderboards
commonsense reasoning
### Languages
en, ru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my.
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 2.03 MB
- **Size of the generated dataset:** 2.03 MB
- **Total amount of disk used:** 2.05 MB
An example of 'train' looks as follows.
```
{'answer_right_ending': 1,
'input_sentence_1': 'Rick grew up in a troubled household.',
'input_sentence_2': 'He never found good support in family, and turned to gangs.',
'input_sentence_3': "It wasn't long before Rick got shot in a robbery.",
'input_sentence_4': 'The incident caused him to turn a new leaf.',
'sentence_quiz1': 'He is happy now.',
'sentence_quiz2': 'He joined a gang.',
'story_id': '138d5bfb-05cc-41e3-bf2c-fa85ebad14e2'}
```
### Data Fields
The data fields are the same among all splits.
- `input_sentence_1`: The first statement in the story.
- `input_sentence_2`: The second statement in the story.
- `input_sentence_3`: The third statement in the story.
- `input_sentence_4`: The forth statement in the story.
- `sentence_quiz1`: first possible continuation of the story.
- `sentence_quiz2`: second possible continuation of the story.
- `answer_right_ending`: correct possible ending; either 1 or 2.
- `story_id`: story id.
### Data Splits
This dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. We split the data for each language into train and test (360 vs. 1510 examples, respectively). The released data files for different languages maintain a line-by-line alignment.
| name |train |test|
|-------|-----:|---:|
|en|360|1510|
|ru|360|1510|
|zh|360|1510|
|es|360|1510|
|ar|360|1510|
|hi|360|1510|
|id|360|1510|
|te|360|1510|
|sw|360|1510|
|eu|360|1510|
|my|360|1510|
## 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
XStoryCloze is opensourced under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode), the same license as the original English StoryCloze.
### Citation Information
```
@article{DBLP:journals/corr/abs-2112-10668,
author = {Xi Victoria Lin and
Todor Mihaylov and
Mikel Artetxe and
Tianlu Wang and
Shuohui Chen and
Daniel Simig and
Myle Ott and
Naman Goyal and
Shruti Bhosale and
Jingfei Du and
Ramakanth Pasunuru and
Sam Shleifer and
Punit Singh Koura and
Vishrav Chaudhary and
Brian O'Horo and
Jeff Wang and
Luke Zettlemoyer and
Zornitsa Kozareva and
Mona T. Diab and
Veselin Stoyanov and
Xian Li},
title = {Few-shot Learning with Multilingual Language Models},
journal = {CoRR},
volume = {abs/2112.10668},
year = {2021},
url = {https://arxiv.org/abs/2112.10668},
eprinttype = {arXiv},
eprint = {2112.10668},
timestamp = {Tue, 04 Jan 2022 15:59:27 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2112-10668.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@juletx](https://github.com/juletx). |
naver-clova-ix/cord-v2 | 2022-07-19T23:43:33.000Z | [
"license:cc-by-4.0",
"region:us"
] | naver-clova-ix | null | null | null | 27 | 8,674 | ---
license: cc-by-4.0
---
|
mteb/sts17-crosslingual-sts | 2022-09-27T19:09:43.000Z | [
"language:ar",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:nl",
"language:ko",
"language:tr",
"region:us"
] | mteb | STS17 Cross-lingual dataset | null | null | 1 | 8,655 | ---
language:
- ar
- de
- en
- es
- fr
- it
- nl
- ko
- tr
--- |
sst | 2023-06-01T14:59:56.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | The Stanford Sentiment Treebank, the first corpus with fully labeled parse trees that allows for a
complete analysis of the compositional effects of sentiment in language. | @inproceedings{socher-etal-2013-recursive,
title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
author = "Socher, Richard and Perelygin, Alex and Wu, Jean and
Chuang, Jason and Manning, Christopher D. and Ng, Andrew and Potts, Christopher",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D13-1170",
pages = "1631--1642",
} | null | 11 | 8,654 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- sentiment-classification
- sentiment-scoring
paperswithcode_id: sst
pretty_name: Stanford Sentiment Treebank
dataset_info:
- config_name: default
features:
- name: sentence
dtype: string
- name: label
dtype: float32
- name: tokens
dtype: string
- name: tree
dtype: string
splits:
- name: train
num_bytes: 2818768
num_examples: 8544
- name: validation
num_bytes: 366205
num_examples: 1101
- name: test
num_bytes: 730154
num_examples: 2210
download_size: 7162356
dataset_size: 3915127
- config_name: dictionary
features:
- name: phrase
dtype: string
- name: label
dtype: float32
splits:
- name: dictionary
num_bytes: 12121843
num_examples: 239232
download_size: 7162356
dataset_size: 12121843
- config_name: ptb
features:
- name: ptb_tree
dtype: string
splits:
- name: train
num_bytes: 2185694
num_examples: 8544
- name: validation
num_bytes: 284132
num_examples: 1101
- name: test
num_bytes: 566248
num_examples: 2210
download_size: 7162356
dataset_size: 3036074
config_names:
- default
- dictionary
- ptb
---
# Dataset Card for sst
## 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://nlp.stanford.edu/sentiment/index.html
- **Repository:** [Needs More Information]
- **Paper:** [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://www.aclweb.org/anthology/D13-1170/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The Stanford Sentiment Treebank is the first corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language.
### Supported Tasks and Leaderboards
- `sentiment-scoring`: Each complete sentence is annotated with a `float` label that indicates its level of positive sentiment from 0.0 to 1.0. One can decide to use only complete sentences or to include the contributions of the sub-sentences (aka phrases). The labels for each phrase are included in the `dictionary` configuration. To obtain all the phrases in a sentence we need to visit the parse tree included with each example. In contrast, the `ptb` configuration explicitly provides all the labelled parse trees in Penn Treebank format. Here the labels are binned in 5 bins from 0 to 4.
- `sentiment-classification`: We can transform the above into a binary sentiment classification task by rounding each label to 0 or 1.
### Languages
The text in the dataset is in English
## Dataset Structure
### Data Instances
For the `default` configuration:
```
{'label': 0.7222200036048889,
'sentence': 'Yet the act is still charming here .',
'tokens': 'Yet|the|act|is|still|charming|here|.',
'tree': '15|13|13|10|9|9|11|12|10|11|12|14|14|15|0'}
```
For the `dictionary` configuration:
```
{'label': 0.7361099720001221,
'phrase': 'still charming'}
```
For the `ptb` configuration:
```
{'ptb_tree': '(3 (2 Yet) (3 (2 (2 the) (2 act)) (3 (4 (3 (2 is) (3 (2 still) (4 charming))) (2 here)) (2 .))))'}
```
### Data Fields
- `sentence`: a complete sentence expressing an opinion about a film
- `label`: the degree of "positivity" of the opinion, on a scale between 0.0 and 1.0
- `tokens`: a sequence of tokens that form a sentence
- `tree`: a sentence parse tree formatted as a parent pointer tree
- `phrase`: a sub-sentence of a complete sentence
- `ptb_tree`: a sentence parse tree formatted in Penn Treebank-style, where each component's degree of positive sentiment is labelled on a scale from 0 to 4
### Data Splits
The set of complete sentences (both `default` and `ptb` configurations) is split into a training, validation and test set. The `dictionary` configuration has only one split as it is used for reference rather than for learning.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
Rotten Tomatoes reviewers.
### 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
```
@inproceedings{socher-etal-2013-recursive,
title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
author = "Socher, Richard and
Perelygin, Alex and
Wu, Jean and
Chuang, Jason and
Manning, Christopher D. and
Ng, Andrew and
Potts, Christopher",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D13-1170",
pages = "1631--1642",
}
```
### Contributions
Thanks to [@patpizio](https://github.com/patpizio) for adding this dataset. |
poloclub/diffusiondb | 2023-05-09T19:00:45.000Z | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:n>1T",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"stable diffusion",
"prompt engineering",
"prompts",
"research paper",
"arxiv:2210.14896",
"region:us"
] | poloclub | DiffusionDB is the first large-scale text-to-image prompt dataset. It contains 2
million images generated by Stable Diffusion using prompts and hyperparameters
specified by real users. The unprecedented scale and diversity of this
human-actuated dataset provide exciting research opportunities in understanding
the interplay between prompts and generative models, detecting deepfakes, and
designing human-AI interaction tools to help users more easily use these models. | @article{wangDiffusionDBLargescalePrompt2022,
title = {{{DiffusionDB}}: {{A}} Large-Scale Prompt Gallery Dataset for Text-to-Image Generative Models},
author = {Wang, Zijie J. and Montoya, Evan and Munechika, David and Yang, Haoyang and Hoover, Benjamin and Chau, Duen Horng},
year = {2022},
journal = {arXiv:2210.14896 [cs]},
url = {https://arxiv.org/abs/2210.14896}
} | null | 312 | 8,594 | ---
layout: default
title: Home
nav_order: 1
has_children: false
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: DiffusionDB
size_categories:
- n>1T
source_datasets:
- original
tags:
- stable diffusion
- prompt engineering
- prompts
- research paper
task_categories:
- text-to-image
- image-to-text
task_ids:
- image-captioning
---
# DiffusionDB
<img width="100%" src="https://user-images.githubusercontent.com/15007159/201762588-f24db2b8-dbb2-4a94-947b-7de393fc3d33.gif">
## Table of Contents
- [DiffusionDB](#diffusiondb)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Two Subsets](#two-subsets)
- [Key Differences](#key-differences)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Metadata](#dataset-metadata)
- [Metadata Schema](#metadata-schema)
- [Data Splits](#data-splits)
- [Loading Data Subsets](#loading-data-subsets)
- [Method 1: Using Hugging Face Datasets Loader](#method-1-using-hugging-face-datasets-loader)
- [Method 2. Use the PoloClub Downloader](#method-2-use-the-poloclub-downloader)
- [Usage/Examples](#usageexamples)
- [Downloading a single file](#downloading-a-single-file)
- [Downloading a range of files](#downloading-a-range-of-files)
- [Downloading to a specific directory](#downloading-to-a-specific-directory)
- [Setting the files to unzip once they've been downloaded](#setting-the-files-to-unzip-once-theyve-been-downloaded)
- [Method 3. Use `metadata.parquet` (Text Only)](#method-3-use-metadataparquet-text-only)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [DiffusionDB homepage](https://poloclub.github.io/diffusiondb)
- **Repository:** [DiffusionDB repository](https://github.com/poloclub/diffusiondb)
- **Distribution:** [DiffusionDB Hugging Face Dataset](https://huggingface.co/datasets/poloclub/diffusiondb)
- **Paper:** [DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models](https://arxiv.org/abs/2210.14896)
- **Point of Contact:** [Jay Wang](mailto:jayw@gatech.edu)
### Dataset Summary
DiffusionDB is the first large-scale text-to-image prompt dataset. It contains **14 million** images generated by Stable Diffusion using prompts and hyperparameters specified by real users.
DiffusionDB is publicly available at [🤗 Hugging Face Dataset](https://huggingface.co/datasets/poloclub/diffusiondb).
### Supported Tasks and Leaderboards
The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models.
### Languages
The text in the dataset is mostly English. It also contains other languages such as Spanish, Chinese, and Russian.
### Two Subsets
DiffusionDB provides two subsets (DiffusionDB 2M and DiffusionDB Large) to support different needs.
|Subset|Num of Images|Num of Unique Prompts|Size|Image Directory|Metadata Table|
|:--|--:|--:|--:|--:|--:|
|DiffusionDB 2M|2M|1.5M|1.6TB|`images/`|`metadata.parquet`|
|DiffusionDB Large|14M|1.8M|6.5TB|`diffusiondb-large-part-1/` `diffusiondb-large-part-2/`|`metadata-large.parquet`|
##### Key Differences
1. Two subsets have a similar number of unique prompts, but DiffusionDB Large has much more images. DiffusionDB Large is a superset of DiffusionDB 2M.
2. Images in DiffusionDB 2M are stored in `png` format; images in DiffusionDB Large use a lossless `webp` format.
## Dataset Structure
We use a modularized file structure to distribute DiffusionDB. The 2 million images in DiffusionDB 2M are split into 2,000 folders, where each folder contains 1,000 images and a JSON file that links these 1,000 images to their prompts and hyperparameters. Similarly, the 14 million images in DiffusionDB Large are split into 14,000 folders.
```bash
# DiffusionDB 2M
./
├── images
│ ├── part-000001
│ │ ├── 3bfcd9cf-26ea-4303-bbe1-b095853f5360.png
│ │ ├── 5f47c66c-51d4-4f2c-a872-a68518f44adb.png
│ │ ├── 66b428b9-55dc-4907-b116-55aaa887de30.png
│ │ ├── [...]
│ │ └── part-000001.json
│ ├── part-000002
│ ├── part-000003
│ ├── [...]
│ └── part-002000
└── metadata.parquet
```
```bash
# DiffusionDB Large
./
├── diffusiondb-large-part-1
│ ├── part-000001
│ │ ├── 0a8dc864-1616-4961-ac18-3fcdf76d3b08.webp
│ │ ├── 0a25cacb-5d91-4f27-b18a-bd423762f811.webp
│ │ ├── 0a52d584-4211-43a0-99ef-f5640ee2fc8c.webp
│ │ ├── [...]
│ │ └── part-000001.json
│ ├── part-000002
│ ├── part-000003
│ ├── [...]
│ └── part-010000
├── diffusiondb-large-part-2
│ ├── part-010001
│ │ ├── 0a68f671-3776-424c-91b6-c09a0dd6fc2d.webp
│ │ ├── 0a0756e9-1249-4fe2-a21a-12c43656c7a3.webp
│ │ ├── 0aa48f3d-f2d9-40a8-a800-c2c651ebba06.webp
│ │ ├── [...]
│ │ └── part-000001.json
│ ├── part-010002
│ ├── part-010003
│ ├── [...]
│ └── part-014000
└── metadata-large.parquet
```
These sub-folders have names `part-0xxxxx`, and each image has a unique name generated by [UUID Version 4](https://en.wikipedia.org/wiki/Universally_unique_identifier). The JSON file in a sub-folder has the same name as the sub-folder. Each image is a `PNG` file (DiffusionDB 2M) or a lossless `WebP` file (DiffusionDB Large). The JSON file contains key-value pairs mapping image filenames to their prompts and hyperparameters.
### Data Instances
For example, below is the image of `f3501e05-aef7-4225-a9e9-f516527408ac.png` and its key-value pair in `part-000001.json`.
<img width="300" src="https://i.imgur.com/gqWcRs2.png">
```json
{
"f3501e05-aef7-4225-a9e9-f516527408ac.png": {
"p": "geodesic landscape, john chamberlain, christopher balaskas, tadao ando, 4 k, ",
"se": 38753269,
"c": 12.0,
"st": 50,
"sa": "k_lms"
},
}
```
### Data Fields
- key: Unique image name
- `p`: Prompt
- `se`: Random seed
- `c`: CFG Scale (guidance scale)
- `st`: Steps
- `sa`: Sampler
### Dataset Metadata
To help you easily access prompts and other attributes of images without downloading all the Zip files, we include two metadata tables `metadata.parquet` and `metadata-large.parquet` for DiffusionDB 2M and DiffusionDB Large, respectively.
The shape of `metadata.parquet` is (2000000, 13) and the shape of `metatable-large.parquet` is (14000000, 13). Two tables share the same schema, and each row represents an image. We store these tables in the Parquet format because Parquet is column-based: you can efficiently query individual columns (e.g., prompts) without reading the entire table.
Below are three random rows from `metadata.parquet`.
| image_name | prompt | part_id | seed | step | cfg | sampler | width | height | user_name | timestamp | image_nsfw | prompt_nsfw |
|:-----------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------:|-----------:|-------:|------:|----------:|--------:|---------:|:-----------------------------------------------------------------|:--------------------------|-------------:|--------------:|
| 0c46f719-1679-4c64-9ba9-f181e0eae811.png | a small liquid sculpture, corvette, viscous, reflective, digital art | 1050 | 2026845913 | 50 | 7 | 8 | 512 | 512 | c2f288a2ba9df65c38386ffaaf7749106fed29311835b63d578405db9dbcafdb | 2022-08-11 09:05:00+00:00 | 0.0845108 | 0.00383462 |
| a00bdeaa-14eb-4f6c-a303-97732177eae9.png | human sculpture of lanky tall alien on a romantic date at italian restaurant with smiling woman, nice restaurant, photography, bokeh | 905 | 1183522603 | 50 | 10 | 8 | 512 | 768 | df778e253e6d32168eb22279a9776b3cde107cc82da05517dd6d114724918651 | 2022-08-19 17:55:00+00:00 | 0.692934 | 0.109437 |
| 6e5024ce-65ed-47f3-b296-edb2813e3c5b.png | portrait of barbaric spanish conquistador, symmetrical, by yoichi hatakenaka, studio ghibli and dan mumford | 286 | 1713292358 | 50 | 7 | 8 | 512 | 640 | 1c2e93cfb1430adbd956be9c690705fe295cbee7d9ac12de1953ce5e76d89906 | 2022-08-12 03:26:00+00:00 | 0.0773138 | 0.0249675 |
#### Metadata Schema
`metadata.parquet` and `metatable-large.parquet` share the same schema.
|Column|Type|Description|
|:---|:---|:---|
|`image_name`|`string`|Image UUID filename.|
|`prompt`|`string`|The text prompt used to generate this image.|
|`part_id`|`uint16`|Folder ID of this image.|
|`seed`|`uint32`| Random seed used to generate this image.|
|`step`|`uint16`| Step count (hyperparameter).|
|`cfg`|`float32`| Guidance scale (hyperparameter).|
|`sampler`|`uint8`| Sampler method (hyperparameter). Mapping: `{1: "ddim", 2: "plms", 3: "k_euler", 4: "k_euler_ancestral", 5: "k_heun", 6: "k_dpm_2", 7: "k_dpm_2_ancestral", 8: "k_lms", 9: "others"}`.
|`width`|`uint16`|Image width.|
|`height`|`uint16`|Image height.|
|`user_name`|`string`|The unique discord ID's SHA256 hash of the user who generated this image. For example, the hash for `xiaohk#3146` is `e285b7ef63be99e9107cecd79b280bde602f17e0ca8363cb7a0889b67f0b5ed0`. "deleted_account" refer to users who have deleted their accounts. None means the image has been deleted before we scrape it for the second time.|
|`timestamp`|`timestamp`|UTC Timestamp when this image was generated. None means the image has been deleted before we scrape it for the second time. Note that timestamp is not accurate for duplicate images that have the same prompt, hypareparameters, width, height.|
|`image_nsfw`|`float32`|Likelihood of an image being NSFW. Scores are predicted by [LAION's state-of-art NSFW detector](https://github.com/LAION-AI/LAION-SAFETY) (range from 0 to 1). A score of 2.0 means the image has already been flagged as NSFW and blurred by Stable Diffusion.|
|`prompt_nsfw`|`float32`|Likelihood of a prompt being NSFW. Scores are predicted by the library [Detoxicy](https://github.com/unitaryai/detoxify). Each score represents the maximum of `toxicity` and `sexual_explicit` (range from 0 to 1).|
> **Warning**
> Although the Stable Diffusion model has an NSFW filter that automatically blurs user-generated NSFW images, this NSFW filter is not perfect—DiffusionDB still contains some NSFW images. Therefore, we compute and provide the NSFW scores for images and prompts using the state-of-the-art models. The distribution of these scores is shown below. Please decide an appropriate NSFW score threshold to filter out NSFW images before using DiffusionDB in your projects.
<img src="https://i.imgur.com/1RiGAXL.png" width="100%">
### Data Splits
For DiffusionDB 2M, we split 2 million images into 2,000 folders where each folder contains 1,000 images and a JSON file. For DiffusionDB Large, we split 14 million images into 14,000 folders where each folder contains 1,000 images and a JSON file.
### Loading Data Subsets
DiffusionDB is large (1.6TB or 6.5 TB)! However, with our modularized file structure, you can easily load a desirable number of images and their prompts and hyperparameters. In the [`example-loading.ipynb`](https://github.com/poloclub/diffusiondb/blob/main/notebooks/example-loading.ipynb) notebook, we demonstrate three methods to load a subset of DiffusionDB. Below is a short summary.
#### Method 1: Using Hugging Face Datasets Loader
You can use the Hugging Face [`Datasets`](https://huggingface.co/docs/datasets/quickstart) library to easily load prompts and images from DiffusionDB. We pre-defined 16 DiffusionDB subsets (configurations) based on the number of instances. You can see all subsets in the [Dataset Preview](https://huggingface.co/datasets/poloclub/diffusiondb/viewer/all/train).
```python
import numpy as np
from datasets import load_dataset
# Load the dataset with the `large_random_1k` subset
dataset = load_dataset('poloclub/diffusiondb', 'large_random_1k')
```
#### Method 2. Use the PoloClub Downloader
This repo includes a Python downloader [`download.py`](https://github.com/poloclub/diffusiondb/blob/main/scripts/download.py) that allows you to download and load DiffusionDB. You can use it from your command line. Below is an example of loading a subset of DiffusionDB.
##### Usage/Examples
The script is run using command-line arguments as follows:
- `-i` `--index` - File to download or lower bound of a range of files if `-r` is also set.
- `-r` `--range` - Upper bound of range of files to download if `-i` is set.
- `-o` `--output` - Name of custom output directory. Defaults to the current directory if not set.
- `-z` `--unzip` - Unzip the file/files after downloading
- `-l` `--large` - Download from Diffusion DB Large. Defaults to Diffusion DB 2M.
###### Downloading a single file
The specific file to download is supplied as the number at the end of the file on HuggingFace. The script will automatically pad the number out and generate the URL.
```bash
python download.py -i 23
```
###### Downloading a range of files
The upper and lower bounds of the set of files to download are set by the `-i` and `-r` flags respectively.
```bash
python download.py -i 1 -r 2000
```
Note that this range will download the entire dataset. The script will ask you to confirm that you have 1.7Tb free at the download destination.
###### Downloading to a specific directory
The script will default to the location of the dataset's `part` .zip files at `images/`. If you wish to move the download location, you should move these files as well or use a symbolic link.
```bash
python download.py -i 1 -r 2000 -o /home/$USER/datahoarding/etc
```
Again, the script will automatically add the `/` between the directory and the file when it downloads.
###### Setting the files to unzip once they've been downloaded
The script is set to unzip the files _after_ all files have downloaded as both can be lengthy processes in certain circumstances.
```bash
python download.py -i 1 -r 2000 -z
```
#### Method 3. Use `metadata.parquet` (Text Only)
If your task does not require images, then you can easily access all 2 million prompts and hyperparameters in the `metadata.parquet` table.
```python
from urllib.request import urlretrieve
import pandas as pd
# Download the parquet table
table_url = f'https://huggingface.co/datasets/poloclub/diffusiondb/resolve/main/metadata.parquet'
urlretrieve(table_url, 'metadata.parquet')
# Read the table using Pandas
metadata_df = pd.read_parquet('metadata.parquet')
```
## Dataset Creation
### Curation Rationale
Recent diffusion models have gained immense popularity by enabling high-quality and controllable image generation based on text prompts written in natural language. Since the release of these models, people from different domains have quickly applied them to create award-winning artworks, synthetic radiology images, and even hyper-realistic videos.
However, generating images with desired details is difficult, as it requires users to write proper prompts specifying the exact expected results. Developing such prompts requires trial and error, and can often feel random and unprincipled. Simon Willison analogizes writing prompts to wizards learning “magical spells”: users do not understand why some prompts work, but they will add these prompts to their “spell book.” For example, to generate highly-detailed images, it has become a common practice to add special keywords such as “trending on artstation” and “unreal engine” in the prompt.
Prompt engineering has become a field of study in the context of text-to-text generation, where researchers systematically investigate how to construct prompts to effectively solve different down-stream tasks. As large text-to-image models are relatively new, there is a pressing need to understand how these models react to prompts, how to write effective prompts, and how to design tools to help users generate images.
To help researchers tackle these critical challenges, we create DiffusionDB, the first large-scale prompt dataset with 14 million real prompt-image pairs.
### Source Data
#### Initial Data Collection and Normalization
We construct DiffusionDB by scraping user-generated images on the official Stable Diffusion Discord server. We choose Stable Diffusion because it is currently the only open-source large text-to-image generative model, and all generated images have a CC0 1.0 Universal Public Domain Dedication license that waives all copyright and allows uses for any purpose. We choose the official [Stable Diffusion Discord server](https://discord.gg/stablediffusion) because it is public, and it has strict rules against generating and sharing illegal, hateful, or NSFW (not suitable for work, such as sexual and violent content) images. The server also disallows users to write or share prompts with personal information.
#### Who are the source language producers?
The language producers are users of the official [Stable Diffusion Discord server](https://discord.gg/stablediffusion).
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The authors removed the discord usernames from the dataset.
We decide to anonymize the dataset because some prompts might include sensitive information: explicitly linking them to their creators can cause harm to creators.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop better understanding of large text-to-image generative models.
The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models.
It should note that we collect images and their prompts from the Stable Diffusion Discord server. The Discord server has rules against users generating or sharing harmful or NSFW (not suitable for work, such as sexual and violent content) images. The Stable Diffusion model used in the server also has an NSFW filter that blurs the generated images if it detects NSFW content. However, it is still possible that some users had generated harmful images that were not detected by the NSFW filter or removed by the server moderators. Therefore, DiffusionDB can potentially contain these images. To mitigate the potential harm, we provide a [Google Form](https://forms.gle/GbYaSpRNYqxCafMZ9) on the [DiffusionDB website](https://poloclub.github.io/diffusiondb/) where users can report harmful or inappropriate images and prompts. We will closely monitor this form and remove reported images and prompts from DiffusionDB.
### Discussion of Biases
The 14 million images in DiffusionDB have diverse styles and categories. However, Discord can be a biased data source. Our images come from channels where early users could use a bot to use Stable Diffusion before release. As these users had started using Stable Diffusion before the model was public, we hypothesize that they are AI art enthusiasts and are likely to have experience with other text-to-image generative models. Therefore, the prompting style in DiffusionDB might not represent novice users. Similarly, the prompts in DiffusionDB might not generalize to domains that require specific knowledge, such as medical images.
### Other Known Limitations
**Generalizability.** Previous research has shown a prompt that works well on one generative model might not give the optimal result when used in other models.
Therefore, different models can need users to write different prompts. For example, many Stable Diffusion prompts use commas to separate keywords, while this pattern is less seen in prompts for DALL-E 2 or Midjourney. Thus, we caution researchers that some research findings from DiffusionDB might not be generalizable to other text-to-image generative models.
## Additional Information
### Dataset Curators
DiffusionDB is created by [Jay Wang](https://zijie.wang), [Evan Montoya](https://www.linkedin.com/in/evan-montoya-b252391b4/), [David Munechika](https://www.linkedin.com/in/dmunechika/), [Alex Yang](https://alexanderyang.me), [Ben Hoover](https://www.bhoov.com), [Polo Chau](https://faculty.cc.gatech.edu/~dchau/).
### Licensing Information
The DiffusionDB dataset is available under the [CC0 1.0 License](https://creativecommons.org/publicdomain/zero/1.0/).
The Python code in this repository is available under the [MIT License](https://github.com/poloclub/diffusiondb/blob/main/LICENSE).
### Citation Information
```bibtex
@article{wangDiffusionDBLargescalePrompt2022,
title = {{{DiffusionDB}}: {{A}} Large-Scale Prompt Gallery Dataset for Text-to-Image Generative Models},
author = {Wang, Zijie J. and Montoya, Evan and Munechika, David and Yang, Haoyang and Hoover, Benjamin and Chau, Duen Horng},
year = {2022},
journal = {arXiv:2210.14896 [cs]},
url = {https://arxiv.org/abs/2210.14896}
}
```
### Contributions
If you have any questions, feel free to [open an issue](https://github.com/poloclub/diffusiondb/issues/new) or contact [Jay Wang](https://zijie.wang).
|
eli5 | 2023-06-08T12:42:30.000Z | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1907.09190",
"arxiv:1904.04047",
"region:us"
] | null | Explain Like I'm 5 long form QA dataset | @inproceedings{DBLP:conf/acl/FanJPGWA19,
author = {Angela Fan and
Yacine Jernite and
Ethan Perez and
David Grangier and
Jason Weston and
Michael Auli},
editor = {Anna Korhonen and
David R. Traum and
Lluis Marquez},
title = {{ELI5:} Long Form Question Answering},
booktitle = {Proceedings of the 57th Conference of the Association for Computational
Linguistics, {ACL} 2019, Florence, Italy, July 28- August 2, 2019,
Volume 1: Long Papers},
pages = {3558--3567},
publisher = {Association for Computational Linguistics},
year = {2019},
url = {https://doi.org/10.18653/v1/p19-1346},
doi = {10.18653/v1/p19-1346},
} | null | 36 | 8,547 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
- open-domain-abstractive-qa
paperswithcode_id: eli5
pretty_name: ELI5
viewer: false
dataset_info:
features:
- name: q_id
dtype: string
- name: title
dtype: string
- name: selftext
dtype: string
- name: document
dtype: string
- name: subreddit
dtype: string
- name: answers
sequence:
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dtype: string
- name: text
dtype: string
- name: score
dtype: int32
- name: title_urls
sequence:
- name: url
dtype: string
- name: selftext_urls
sequence:
- name: url
dtype: string
- name: answers_urls
sequence:
- name: url
dtype: string
config_name: LFQA_reddit
splits:
- name: train_eli5
num_bytes: 577188173
num_examples: 272634
- name: validation_eli5
num_bytes: 21117891
num_examples: 9812
- name: test_eli5
num_bytes: 53099796
num_examples: 24512
- name: train_asks
num_bytes: 286464210
num_examples: 131778
- name: validation_asks
num_bytes: 9662481
num_examples: 2281
- name: test_asks
num_bytes: 17713920
num_examples: 4462
- name: train_askh
num_bytes: 330483260
num_examples: 98525
- name: validation_askh
num_bytes: 18690845
num_examples: 4901
- name: test_askh
num_bytes: 36246784
num_examples: 9764
download_size: 6326543
dataset_size: 1350667360
---
## <span style="color:red">⚠️ Reddit recently [changed the terms of access](https://www.reddit.com/r/reddit/comments/12qwagm/an_update_regarding_reddits_api/) to its API, making the source data for this dataset unavailable</span>.
# Dataset Card for ELI5
## 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:** [ELI5 homepage](https://facebookresearch.github.io/ELI5/explore.html)
- **Repository:** [ELI5 repository](https://github.com/facebookresearch/ELI5)
- **Paper:** [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190)
- **Point of Contact:** [Yacine Jernite](mailto:yacine@huggingface.co)
### Dataset Summary
The ELI5 dataset is an English-language dataset of questions and answers gathered from three subreddits where users ask factual questions requiring paragraph-length or longer answers. The dataset was created to support the task of open-domain long form abstractive question answering, and covers questions about general topics in its [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) subset, science in it [r/askscience](https://www.reddit.com/r/askscience/) subset, and History in its [r/AskHistorians](https://www.reddit.com/r/AskHistorians/) subset.
### Supported Tasks and Leaderboards
- `abstractive-qa`, `open-domain-abstractive-qa`: The dataset can be used to train a model for Open Domain Long Form Question Answering. An LFQA model is presented with a non-factoid and asked to retrieve relevant information from a knowledge source (such as [Wikipedia](https://www.wikipedia.org/)), then use it to generate a multi-sentence answer. The model performance is measured by how high its [ROUGE](https://huggingface.co/metrics/rouge) score to the reference is. A [BART-based model](https://huggingface.co/yjernite/bart_eli5) with a [dense retriever](https://huggingface.co/yjernite/retribert-base-uncased) trained to draw information from [Wikipedia passages](https://huggingface.co/datasets/wiki_snippets) achieves a [ROUGE-L of 0.149](https://yjernite.github.io/lfqa.html#generation).
### Languages
The text in the dataset is in English, as spoken by Reddit users on the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/), [r/askscience](https://www.reddit.com/r/askscience/), and [r/AskHistorians](https://www.reddit.com/r/AskHistorians/) subreddits. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A typical data point comprises a question, with a `title` containing the main question and a `selftext` which sometimes elaborates on it, and a list of answers from the forum sorted by the number of upvotes they obtained. Additionally, the URLs in each of the text fields have been extracted to respective lists and replaced by generic tokens in the text.
An example from the ELI5 test set looks as follows:
```
{'q_id': '8houtx',
'title': 'Why does water heated to room temperature feel colder than the air around it?',
'selftext': '',
'document': '',
'subreddit': 'explainlikeimfive',
'answers': {'a_id': ['dylcnfk', 'dylcj49'],
'text': ["Water transfers heat more efficiently than air. When something feels cold it's because heat is being transferred from your skin to whatever you're touching. Since water absorbs the heat more readily than air, it feels colder.",
"Air isn't as good at transferring heat compared to something like water or steel (sit on a room temperature steel bench vs. a room temperature wooden bench, and the steel one will feel more cold).\n\nWhen you feel cold, what you're feeling is heat being transferred out of you. If there is no breeze, you feel a certain way. If there's a breeze, you will get colder faster (because the moving air is pulling the heat away from you), and if you get into water, its quite good at pulling heat from you. Get out of the water and have a breeze blow on you while you're wet, all of the water starts evaporating, pulling even more heat from you."],
'score': [5, 2]},
'title_urls': {'url': []},
'selftext_urls': {'url': []},
'answers_urls': {'url': []}}
```
### Data Fields
- `q_id`: a string question identifier for each example, corresponding to its ID in the [Pushshift.io](https://files.pushshift.io/reddit/submissions/) Reddit submission dumps.
- `subreddit`: One of `explainlikeimfive`, `askscience`, or `AskHistorians`, indicating which subreddit the question came from
- `title`: title of the question, with URLs extracted and replaced by `URL_n` tokens
- `title_urls`: list of the extracted URLs, the `n`th element of the list was replaced by `URL_n`
- `selftext`: either an empty string or an elaboration of the question
- `selftext_urls`: similar to `title_urls` but for `self_text`
- `answers`: a list of answers, each answer has:
- `a_id`: a string answer identifier for each answer, corresponding to its ID in the [Pushshift.io](https://files.pushshift.io/reddit/comments/) Reddit comments dumps.
- `text`: the answer text with the URLs normalized
- `score`: the number of upvotes the answer had received when the dumps were created
- `answers_urls`: a list of the extracted URLs. All answers use the same list, the numbering of the normalization token continues across answer texts
### Data Splits
The data is split into a training, validation and test set for each of the three subreddits. In order to avoid having duplicate questions in across sets, the `title` field of each of the questions were ranked by their tf-idf match to their nearest neighbor and the ones with the smallest value were used in the test and validation sets. The final split sizes are as follow:
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| r/explainlikeimfive examples| 272634 | 9812 | 24512|
| r/askscience examples | 131778 | 2281 | 4462 |
| r/AskHistorians examples | 98525 | 4901 | 9764 |
## Dataset Creation
### Curation Rationale
ELI5 was built to provide a testbed for machines to learn how to answer more complex questions, which requires them to find and combine information in a coherent manner. The dataset was built by gathering questions that were asked by community members of three subreddits, including [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/), along with the answers that were provided by other users. The [rules of the subreddit](https://www.reddit.com/r/explainlikeimfive/wiki/detailed_rules) make this data particularly well suited to training a model for abstractive question answering: the questions need to seek an objective explanation about well established facts, and the answers provided need to be understandable to a layperson without any particular knowledge domain.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained by filtering submissions and comments from the subreddits of interest from the XML dumps of the [Reddit forum](https://www.reddit.com/) hosted on [Pushshift.io](https://files.pushshift.io/reddit/).
In order to further improve the quality of the selected examples, only questions with a score of at least 2 and at least one answer with a score of at least 2 were selected for the dataset. The dataset questions and answers span a period form August 2012 to August 2019.
#### Who are the source language producers?
The language producers are users of the [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/), [r/askscience](https://www.reddit.com/r/askscience/), and [r/AskHistorians](https://www.reddit.com/r/AskHistorians/) subreddits between 2012 and 2019. No further demographic information was available from the data source.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The authors removed the speaker IDs from the [Pushshift.io](https://files.pushshift.io/reddit/) dumps but did not otherwise anonymize the data. Some of the questions and answers are about contemporary public figures or individuals who appeared in the news.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop better question answering systems.
A system that succeeds at the supported task would be able to provide a coherent answer to even complex questions requiring a multi-step explanation, which is beyond the ability of even the larger existing models. The task is also thought as a test-bed for retrieval model which can show the users which source text was used in generating the answer and allow them to confirm the information provided to them.
It should be noted however that the provided answers were written by Reddit users, an information which may be lost if models trained on it are deployed in down-stream applications and presented to users without context. The specific biases this may introduce are discussed in the next section.
### Discussion of Biases
While Reddit hosts a number of thriving communities with high quality discussions, it is also widely known to have corners where sexism, hate, and harassment are significant issues. See for example the [recent post from Reddit founder u/spez](https://www.reddit.com/r/announcements/comments/gxas21/upcoming_changes_to_our_content_policy_our_board/) outlining some of the ways he thinks the website's historical policies have been responsible for this problem, [Adrienne Massanari's 2015 article on GamerGate](https://www.researchgate.net/publication/283848479_Gamergate_and_The_Fappening_How_Reddit's_algorithm_governance_and_culture_support_toxic_technocultures) and follow-up works, or a [2019 Wired article on misogyny on Reddit](https://www.wired.com/story/misogyny-reddit-research/).
While there has been some recent work in the NLP community on *de-biasing* models (e.g. [Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings](https://arxiv.org/abs/1904.04047) for word embeddings trained specifically on Reddit data), this problem is far from solved, and the likelihood that a trained model might learn the biases present in the data remains a significant concern.
We still note some encouraging signs for all of these communities: [r/explainlikeimfive](https://www.reddit.com/r/explainlikeimfive/) and [r/askscience](https://www.reddit.com/r/askscience/) have similar structures and purposes, and [r/askscience](https://www.reddit.com/r/askscience/) was found in 2015 to show medium supportiveness and very low toxicity when compared to other subreddits (see a [hackerfall post](https://hackerfall.com/story/study-and-interactive-visualization-of-toxicity-in), [thecut.com write-up](https://www.thecut.com/2015/03/interactive-chart-of-reddits-toxicity.html) and supporting [data](https://chart-studio.plotly.com/~bsbell21/210/toxicity-vs-supportiveness-by-subreddit/#data)). Meanwhile, the [r/AskHistorians rules](https://www.reddit.com/r/AskHistorians/wiki/rules) mention that the admins will not tolerate "_racism, sexism, or any other forms of bigotry_". However, further analysis of whether and to what extent these rules reduce toxicity is still needed.
We also note that given the audience of the Reddit website which is more broadly used in the US and Europe, the answers will likely present a Western perspectives, which is particularly important to note when dealing with historical topics.
### Other Known Limitations
The answers provided in the dataset are represent the opinion of Reddit users. While these communities strive to be helpful, they should not be considered to represent a ground truth.
## Additional Information
### Dataset Curators
The dataset was initially created by Angela Fan, Ethan Perez, Yacine Jernite, Jason Weston, Michael Auli, and David Grangier, during work done at Facebook AI Research (FAIR).
### Licensing Information
The licensing status of the dataset hinges on the legal status of the [Pushshift.io](https://files.pushshift.io/reddit/) data which is unclear.
### Citation Information
```
@inproceedings{eli5_lfqa,
author = {Angela Fan and
Yacine Jernite and
Ethan Perez and
David Grangier and
Jason Weston and
Michael Auli},
editor = {Anna Korhonen and
David R. Traum and
Llu{\'{\i}}s M{\`{a}}rquez},
title = {{ELI5:} Long Form Question Answering},
booktitle = {Proceedings of the 57th Conference of the Association for Computational
Linguistics, {ACL} 2019, Florence, Italy, July 28- August 2, 2019,
Volume 1: Long Papers},
pages = {3558--3567},
publisher = {Association for Computational Linguistics},
year = {2019},
url = {https://doi.org/10.18653/v1/p19-1346},
doi = {10.18653/v1/p19-1346}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset. |
tydiqa | 2023-04-05T13:42:46.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|wikipedia",
"language:ar",
"language:bn",
"language:en",
"language:fi",
"language:id",
"language:ja",
"language:ko",
"language:ru",
"language:sw",
"language:te",
"language:th",
"license:apache-2.0",
"region:us"
] | null | TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic
information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but
don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without
the use of translation (unlike MLQA and XQuAD). | @article{tydiqa,
title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
year = {2020},
journal = {Transactions of the Association for Computational Linguistics}
} | null | 13 | 8,535 | ---
pretty_name: TyDi QA
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
- bn
- en
- fi
- id
- ja
- ko
- ru
- sw
- te
- th
license:
- apache-2.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: tydi-qa
dataset_info:
- config_name: primary_task
features:
- name: passage_answer_candidates
sequence:
- name: plaintext_start_byte
dtype: int32
- name: plaintext_end_byte
dtype: int32
- name: question_text
dtype: string
- name: document_title
dtype: string
- name: language
dtype: string
- name: annotations
sequence:
- name: passage_answer_candidate_index
dtype: int32
- name: minimal_answers_start_byte
dtype: int32
- name: minimal_answers_end_byte
dtype: int32
- name: yes_no_answer
dtype: string
- name: document_plaintext
dtype: string
- name: document_url
dtype: string
splits:
- name: train
num_bytes: 5550574617
num_examples: 166916
- name: validation
num_bytes: 484380443
num_examples: 18670
download_size: 1953887429
dataset_size: 6034955060
- config_name: secondary_task
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
splits:
- name: train
num_bytes: 52948607
num_examples: 49881
- name: validation
num_bytes: 5006461
num_examples: 5077
download_size: 1953887429
dataset_size: 57955068
---
# Dataset Card for "tydiqa"
## 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/tydiqa](https://github.com/google-research-datasets/tydiqa)
- **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:** 3.91 GB
- **Size of the generated dataset:** 6.10 GB
- **Total amount of disk used:** 10.00 GB
### Dataset Summary
TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic
information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but
don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without
the use of translation (unlike MLQA and XQuAD).
### 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
#### primary_task
- **Size of downloaded dataset files:** 1.95 GB
- **Size of the generated dataset:** 6.04 GB
- **Total amount of disk used:** 7.99 GB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"annotations": {
"minimal_answers_end_byte": [-1, -1, -1],
"minimal_answers_start_byte": [-1, -1, -1],
"passage_answer_candidate_index": [-1, -1, -1],
"yes_no_answer": ["NONE", "NONE", "NONE"]
},
"document_plaintext": "\"\\nรองศาสตราจารย์[1] หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร (22 กันยายน 2495 -) ผู้ว่าราชการกรุงเทพมหานครคนที่ 15 อดีตรองหัวหน้าพรรคปร...",
"document_title": "หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร",
"document_url": "\"https://th.wikipedia.org/wiki/%E0%B8%AB%E0%B8%A1%E0%B9%88%E0%B8%AD%E0%B8%A1%E0%B8%A3%E0%B8%B2%E0%B8%8A%E0%B8%A7%E0%B8%87%E0%B8%...",
"language": "thai",
"passage_answer_candidates": "{\"plaintext_end_byte\": [494, 1779, 2931, 3904, 4506, 5588, 6383, 7122, 8224, 9375, 10473, 12563, 15134, 17765, 19863, 21902, 229...",
"question_text": "\"หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร เรียนจบจากที่ไหน ?\"..."
}
```
#### secondary_task
- **Size of downloaded dataset files:** 1.95 GB
- **Size of the generated dataset:** 58.03 MB
- **Total amount of disk used:** 2.01 GB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [394],
"text": ["بطولتين"]
},
"context": "\"أقيمت البطولة 21 مرة، شارك في النهائيات 78 دولة، وعدد الفرق التي فازت بالبطولة حتى الآن 8 فرق، ويعد المنتخب البرازيلي الأكثر تت...",
"id": "arabic-2387335860751143628-1",
"question": "\"كم عدد مرات فوز الأوروغواي ببطولة كاس العالم لكرو القدم؟\"...",
"title": "قائمة نهائيات كأس العالم"
}
```
### Data Fields
The data fields are the same among all splits.
#### primary_task
- `passage_answer_candidates`: a dictionary feature containing:
- `plaintext_start_byte`: a `int32` feature.
- `plaintext_end_byte`: a `int32` feature.
- `question_text`: a `string` feature.
- `document_title`: a `string` feature.
- `language`: a `string` feature.
- `annotations`: a dictionary feature containing:
- `passage_answer_candidate_index`: a `int32` feature.
- `minimal_answers_start_byte`: a `int32` feature.
- `minimal_answers_end_byte`: a `int32` feature.
- `yes_no_answer`: a `string` feature.
- `document_plaintext`: a `string` feature.
- `document_url`: a `string` feature.
#### secondary_task
- `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 | validation |
| -------------- | -----: | ---------: |
| primary_task | 166916 | 18670 |
| secondary_task | 49881 | 5077 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{tydiqa,
title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
year = {2020},
journal = {Transactions of the Association for Computational Linguistics}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
Dahoas/rm-static | 2023-03-06T00:13:07.000Z | [
"region:us"
] | Dahoas | null | null | null | 86 | 8,526 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 113850006
num_examples: 76256
- name: test
num_bytes: 7649255
num_examples: 5103
download_size: 73006535
dataset_size: 121499261
---
# Dataset Card for "rm-static"
Split of [hh-static](https://huggingface.co/datasets/Dahoas/static-hh) used for training reward models after supervised fine-tuning. |
snli | 2023-01-25T14:44:35.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-flicker-30k",
"source_datasets:extended|other-visual-genome",
"language:en",
"license:cc-by-4.0",
"arxiv:1909.02209",
"region:us"
] | null | The SNLI corpus (version 1.0) is a collection of 570k human-written English
sentence pairs manually labeled for balanced classification with the labels
entailment, contradiction, and neutral, supporting the task of natural language
inference (NLI), also known as recognizing textual entailment (RTE). | @inproceedings{snli:emnlp2015,
Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
Publisher = {Association for Computational Linguistics},
Title = {A large annotated corpus for learning natural language inference},
Year = {2015}
} | null | 29 | 8,458 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-flicker-30k
- extended|other-visual-genome
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
paperswithcode_id: snli
pretty_name: Stanford Natural Language Inference
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
config_name: plain_text
splits:
- name: test
num_bytes: 1263912
num_examples: 10000
- name: train
num_bytes: 66159510
num_examples: 550152
- name: validation
num_bytes: 1268044
num_examples: 10000
download_size: 94550081
dataset_size: 68691466
---
# Dataset Card for SNLI
## 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:** [SNLI homepage](https://nlp.stanford.edu/projects/snli/)
- **Repository:**
- **Paper:** [A large annotated corpus for learning natural langauge inference](https://nlp.stanford.edu/pubs/snli_paper.pdf)
- **Leaderboard:** [SNLI leaderboard](https://nlp.stanford.edu/projects/snli/) (located on the homepage)
- **Point of Contact:** [Samuel Bowman](mailto:bowman@nyu.edu) and [Gabor Angeli](mailto:angeli@stanford.edu)
### Dataset Summary
The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE).
### Supported Tasks and Leaderboards
[SemBERT](https://arxiv.org/pdf/1909.02209.pdf) (Zhousheng Zhang et al, 2019b) is currently listed as SOTA, achieving 91.9% accuracy on the test set. See the [corpus webpage](https://nlp.stanford.edu/projects/snli/) for a list of published results.
### Languages
The language in the dataset is English as spoken by users of the website Flickr and as spoken by crowdworkers from Amazon Mechanical Turk. The BCP-47 code for English is en.
## Dataset Structure
### Data Instances
For each instance, there is a string for the premise, a string for the hypothesis, and an integer for the label. Note that each premise may appear three times with a different hypothesis and label. See the [SNLI corpus viewer](https://huggingface.co/datasets/viewer/?dataset=snli) to explore more examples.
```
{'premise': 'Two women are embracing while holding to go packages.'
'hypothesis': 'The sisters are hugging goodbye while holding to go packages after just eating lunch.'
'label': 1}
```
The average token count for the premises and hypotheses are given below:
| Feature | Mean Token Count |
| ---------- | ---------------- |
| Premise | 14.1 |
| Hypothesis | 8.3 |
### Data Fields
- `premise`: a string used to determine the truthfulness of the hypothesis
- `hypothesis`: a string that may be true, false, or whose truth conditions may not be knowable when compared to the premise
- `label`: an integer whose value may be either _0_, indicating that the hypothesis entails the premise, _1_, indicating that the premise and hypothesis neither entail nor contradict each other, or _2_, indicating that the hypothesis contradicts the premise. Dataset instances which don't have any gold label are marked with -1 label. Make sure you filter them before starting the training using `datasets.Dataset.filter`.
### Data Splits
The SNLI dataset has 3 splits: _train_, _validation_, and _test_. All of the examples in the _validation_ and _test_ sets come from the set that was annotated in the validation task with no-consensus examples removed. The remaining multiply-annotated examples are in the training set with no-consensus examples removed. Each unique premise/caption shows up in only one split, even though they usually appear in at least three different examples.
| Dataset Split | Number of Instances in Split |
| ------------- |----------------------------- |
| Train | 550,152 |
| Validation | 10,000 |
| Test | 10,000 |
## Dataset Creation
### Curation Rationale
The [SNLI corpus (version 1.0)](https://nlp.stanford.edu/projects/snli/) was developed as a benchmark for natural langauge inference (NLI), also known as recognizing textual entailment (RTE), with the goal of producing a dataset large enough to train models using neural methodologies.
### Source Data
#### Initial Data Collection and Normalization
The hypotheses were elicited by presenting crowdworkers with captions from preexisting datasets without the associated photos, but the vocabulary of the hypotheses still reflects the content of the photos as well as the caption style of writing (e.g. mostly present tense). The dataset developers report 37,026 distinct words in the corpus, ignoring case. They allowed bare NPs as well as full sentences. Using the Stanford PCFG Parser 3.5.2 (Klein and Manning, 2003) trained on the standard training set as well as on the Brown Corpus (Francis and Kucera 1979), the authors report that 74% of the premises and 88.9% of the hypotheses result in a parse rooted with an 'S'. The corpus was developed between 2014 and 2015.
Crowdworkers were presented with a caption without the associated photo and asked to produce three alternate captions, one that is definitely true, one that might be true, and one that is definitely false. See Section 2.1 and Figure 1 for details (Bowman et al., 2015).
The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) and the [VisualGenome corpus](https://visualgenome.org/). The photo captions used to prompt the data creation were collected on Flickr by [Young et al. (2014)](https://www.aclweb.org/anthology/Q14-1006.pdf), who extended the Flickr 8K dataset developed by [Hodosh et al. (2013)](https://www.jair.org/index.php/jair/article/view/10833). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in [MS-COCO](https://cocodataset.org/#home) and [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/).
The premises from the Flickr 30k corpus corrected for spelling using the Linux spell checker and ungrammatical sentences were removed. Bowman et al. do not report any normalization, though they note that punctuation and capitalization are often omitted.
#### Who are the source language producers?
A large portion of the premises (160k) were produced in the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) by an unknown number of crowdworkers. About 2,500 crowdworkers from Amazon Mechanical Turk produced the associated hypotheses. The premises from the Flickr 30k project describe people and animals whose photos were collected and presented to the Flickr 30k crowdworkers, but the SNLI corpus did not present the photos to the hypotheses creators.
The Flickr 30k corpus did not report crowdworker or photo subject demographic information or crowdworker compensation. The SNLI crowdworkers were compensated per HIT at rates between $.1 and $.5 with no incentives. Workers who ignored the guidelines were disqualified, and automated bulk submissions were rejected. No demographic information was collected from the SNLI crowdworkers.
An additional 4,000 premises come from the pilot study of the [VisualGenome corpus](https://visualgenome.org/static/paper/Visual_Genome.pdf). Though the pilot study itself is not described, the location information of the 33,000 AMT crowdworkers that participated over the course of the 6 months of data collection are aggregated. Most of the workers were located in the United States (93%), with others from the Philippines, Kenya, India, Russia, and Canada. Workers were paid $6-$8 per hour.
### Annotations
#### Annotation process
56,941 of the total sentence pairs were further annotated in a validation task. Four annotators each labeled a premise-hypothesis pair as entailment, contradiction, or neither, resulting in 5 total judgements including the original hypothesis author judgement. See Section 2.2 for more details (Bowman et al., 2015).
The authors report 3/5 annotator agreement on 98% of the validation set and unanimous annotator agreement on 58.3% of the validation set. If a label was chosen by three annotators, that label was made the gold label. Following from this, 2% of the data did not have a consensus label and was labeled '-' by the authors.
| Label | Fleiss κ |
| --------------- |--------- |
| _contradiction_ | 0.77 |
| _entailment_ | 0.72 |
| _neutral_ | 0.60 |
| overall | 0.70 |
#### Who are the annotators?
The annotators of the validation task were a closed set of about 30 trusted crowdworkers on Amazon Mechanical Turk. No demographic information was collected. Annotators were compensated per HIT between $.1 and $.5 with $1 bonuses in cases where annotator labels agreed with the curators' labels for 250 randomly distributed examples.
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people in the original Flickr photos.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context. (It should be noted that the truth conditions of a hypothesis given a premise does not necessarily match the truth conditions of the hypothesis in the real world.) Systems that are successful at such a task may be more successful in modeling semantic representations.
### Discussion of Biases
The language reflects the content of the photos collected from Flickr, as described in the [Data Collection](#initial-data-collection-and-normalization) section. [Rudinger et al (2017)](https://www.aclweb.org/anthology/W17-1609.pdf) use pointwise mutual information to calculate a measure of association between a manually selected list of tokens corresponding to identity categories and the other words in the corpus, showing strong evidence of stereotypes across gender categories. They also provide examples in which crowdworkers reproduced harmful stereotypes or pejorative language in the hypotheses.
### Other Known Limitations
[Gururangan et al (2018)](https://www.aclweb.org/anthology/N18-2017.pdf), [Poliak et al (2018)](https://www.aclweb.org/anthology/S18-2023.pdf), and [Tsuchiya (2018)](https://www.aclweb.org/anthology/L18-1239.pdf) show that the SNLI corpus has a number of annotation artifacts. Using various classifiers, Poliak et al correctly predicted the label of the hypothesis 69% of the time without using the premise, Gururangan et al 67% of the time, and Tsuchiya 63% of the time.
## Additional Information
### Dataset Curators
The SNLI corpus was developed by Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning as part of the [Stanford NLP group](https://nlp.stanford.edu/).
It was supported by a Google Faculty Research Award, a gift from Bloomberg L.P., the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) contract no. FA8750-13-2-0040, the National Science Foundation under grant no. IIS 1159679, and the Department of the Navy, Office of Naval Research, under grant no. N00014-10-1-0109.
### Licensing Information
The Stanford Natural Language Inference Corpus is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
```
@inproceedings{snli:emnlp2015,
Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
Publisher = {Association for Computational Linguistics},
Title = {A large annotated corpus for learning natural language inference},
Year = {2015}
}
```
### Contributions
Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset. |
multi_nli | 2023-04-05T10:10:15.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"license:cc-by-sa-3.0",
"license:mit",
"license:other",
"region:us"
] | null | The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
crowd-sourced collection of 433k sentence pairs annotated with textual
entailment information. The corpus is modeled on the SNLI corpus, but differs in
that covers a range of genres of spoken and written text, and supports a
distinctive cross-genre generalization evaluation. The corpus served as the
basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen. | @InProceedings{N18-1101,
author = {Williams, Adina
and Nangia, Nikita
and Bowman, Samuel},
title = {A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference},
booktitle = {Proceedings of the 2018 Conference of
the North American Chapter of the
Association for Computational Linguistics:
Human Language Technologies, Volume 1 (Long
Papers)},
year = {2018},
publisher = {Association for Computational Linguistics},
pages = {1112--1122},
location = {New Orleans, Louisiana},
url = {http://aclweb.org/anthology/N18-1101}
} | null | 36 | 8,353 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-3.0
- cc-by-sa-3.0
- mit
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
paperswithcode_id: multinli
pretty_name: Multi-Genre Natural Language Inference
license_details: Open Portion of the American National Corpus
dataset_info:
features:
- name: promptID
dtype: int32
- name: pairID
dtype: string
- name: premise
dtype: string
- name: premise_binary_parse
dtype: string
- name: premise_parse
dtype: string
- name: hypothesis
dtype: string
- name: hypothesis_binary_parse
dtype: string
- name: hypothesis_parse
dtype: string
- name: genre
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 410211586
num_examples: 392702
- name: validation_matched
num_bytes: 10063939
num_examples: 9815
- name: validation_mismatched
num_bytes: 10610221
num_examples: 9832
download_size: 226850426
dataset_size: 430885746
---
# Dataset Card for Multi-Genre Natural Language Inference (MultiNLI)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.nyu.edu/projects/bowman/multinli/](https://www.nyu.edu/projects/bowman/multinli/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 226.85 MB
- **Size of the generated dataset:** 76.95 MB
- **Total amount of disk used:** 303.81 MB
### Dataset Summary
The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
crowd-sourced collection of 433k sentence pairs annotated with textual
entailment information. The corpus is modeled on the SNLI corpus, but differs in
that covers a range of genres of spoken and written text, and supports a
distinctive cross-genre generalization evaluation. The corpus served as the
basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The dataset contains samples in English only.
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 226.85 MB
- **Size of the generated dataset:** 76.95 MB
- **Total amount of disk used:** 303.81 MB
Example of a data instance:
```
{
"promptID": 31193,
"pairID": "31193n",
"premise": "Conceptually cream skimming has two basic dimensions - product and geography.",
"premise_binary_parse": "( ( Conceptually ( cream skimming ) ) ( ( has ( ( ( two ( basic dimensions ) ) - ) ( ( product and ) geography ) ) ) . ) )",
"premise_parse": "(ROOT (S (NP (JJ Conceptually) (NN cream) (NN skimming)) (VP (VBZ has) (NP (NP (CD two) (JJ basic) (NNS dimensions)) (: -) (NP (NN product) (CC and) (NN geography)))) (. .)))",
"hypothesis": "Product and geography are what make cream skimming work. ",
"hypothesis_binary_parse": "( ( ( Product and ) geography ) ( ( are ( what ( make ( cream ( skimming work ) ) ) ) ) . ) )",
"hypothesis_parse": "(ROOT (S (NP (NN Product) (CC and) (NN geography)) (VP (VBP are) (SBAR (WHNP (WP what)) (S (VP (VBP make) (NP (NP (NN cream)) (VP (VBG skimming) (NP (NN work)))))))) (. .)))",
"genre": "government",
"label": 1
}
```
### Data Fields
The data fields are the same among all splits.
- `promptID`: Unique identifier for prompt
- `pairID`: Unique identifier for pair
- `{premise,hypothesis}`: combination of `premise` and `hypothesis`
- `{premise,hypothesis} parse`: Each sentence as parsed by the Stanford PCFG Parser 3.5.2
- `{premise,hypothesis} binary parse`: parses in unlabeled binary-branching format
- `genre`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). Dataset instances which don't have any gold label are marked with -1 label. Make sure you filter them before starting the training using `datasets.Dataset.filter`.
### Data Splits
|train |validation_matched|validation_mismatched|
|-----:|-----------------:|--------------------:|
|392702| 9815| 9832|
## Dataset Creation
### Curation Rationale
They constructed MultiNLI so as to make it possible to explicitly evaluate models both on the quality of their sentence representations within the training domain and on their ability to derive reasonable representations in unfamiliar domains.
### Source Data
#### Initial Data Collection and Normalization
They created each sentence pair by selecting a premise sentence from a preexisting text source and asked a human annotator to compose a novel sentence to pair with it as a hypothesis.
#### 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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The majority of the corpus is released under the OANC’s license, which allows all content to be freely used, modified, and shared under permissive terms. The data in the FICTION section falls under several permissive licenses; Seven Swords is available under a Creative Commons Share-Alike 3.0 Unported License, and with the explicit permission of the author, Living History and Password Incorrect are available under Creative Commons Attribution 3.0 Unported Licenses; the remaining works of fiction are in the public domain in the United States (but may be licensed differently elsewhere).
### Citation Information
```
@InProceedings{N18-1101,
author = "Williams, Adina
and Nangia, Nikita
and Bowman, Samuel",
title = "A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference",
booktitle = "Proceedings of the 2018 Conference of
the North American Chapter of the
Association for Computational Linguistics:
Human Language Technologies, Volume 1 (Long
Papers)",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "1112--1122",
location = "New Orleans, Louisiana",
url = "http://aclweb.org/anthology/N18-1101"
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. |
gia-project/gia-dataset-parquet | 2023-10-02T21:52:41.000Z | [
"task_categories:reinforcement-learning",
"task_categories:text-generation",
"task_categories:question-answering",
"annotations_creators:found",
"annotations_creators:machine-generated",
"source_datasets:conceptual-captions",
"source_datasets:ok-vqa",
"source_datasets:oscar",
"license:apache-2.0",
"imitation-learning",
"reinforcement-learning",
"text-generation",
"question-answering",
"generalist-agent",
"arxiv:2303.03915",
"region:us"
] | gia-project | null | null | null | 0 | 8,350 | ---
annotations_creators:
- found
- machine-generated
license: apache-2.0
size_categories:
- {}
source_datasets:
- conceptual-captions
- ok-vqa
- oscar
task_categories:
- reinforcement-learning
- text-generation
- question-answering
pretty_name: GIA-dataset
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path: metaworld-button-press/test-*
- config_name: metaworld-button-press-topdown
data_files:
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path: metaworld-button-press-topdown/train-*
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path: metaworld-button-press-topdown/test-*
- config_name: metaworld-button-press-topdown-wall
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path: metaworld-button-press-topdown-wall/train-*
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path: metaworld-button-press-topdown-wall/test-*
- config_name: metaworld-button-press-wall
data_files:
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path: metaworld-button-press-wall/train-*
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path: metaworld-button-press-wall/test-*
- config_name: metaworld-coffee-button
data_files:
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path: metaworld-coffee-button/train-*
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path: metaworld-coffee-button/test-*
- config_name: metaworld-coffee-pull
data_files:
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path: metaworld-coffee-pull/train-*
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path: metaworld-coffee-pull/test-*
- config_name: metaworld-coffee-push
data_files:
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path: metaworld-coffee-push/train-*
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path: metaworld-coffee-push/test-*
- config_name: metaworld-dial-turn
data_files:
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path: metaworld-dial-turn/train-*
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path: metaworld-dial-turn/test-*
- config_name: metaworld-disassemble
data_files:
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path: metaworld-disassemble/train-*
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path: metaworld-disassemble/test-*
- config_name: metaworld-door-close
data_files:
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path: metaworld-door-close/train-*
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path: metaworld-door-close/test-*
- config_name: metaworld-door-lock
data_files:
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path: metaworld-door-lock/train-*
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path: metaworld-door-lock/test-*
- config_name: metaworld-door-open
data_files:
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path: metaworld-door-open/train-*
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path: metaworld-door-open/test-*
- config_name: metaworld-door-unlock
data_files:
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path: metaworld-door-unlock/train-*
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path: metaworld-door-unlock/test-*
- config_name: metaworld-drawer-close
data_files:
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path: metaworld-drawer-close/train-*
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path: metaworld-drawer-close/test-*
- config_name: metaworld-drawer-open
data_files:
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path: metaworld-drawer-open/train-*
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path: metaworld-drawer-open/test-*
- config_name: metaworld-faucet-close
data_files:
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path: metaworld-faucet-close/train-*
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path: metaworld-faucet-close/test-*
- config_name: metaworld-faucet-open
data_files:
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path: metaworld-faucet-open/train-*
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path: metaworld-faucet-open/test-*
- config_name: metaworld-hammer
data_files:
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path: metaworld-hammer/train-*
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path: metaworld-hammer/test-*
- config_name: metaworld-hand-insert
data_files:
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path: metaworld-hand-insert/train-*
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path: metaworld-hand-insert/test-*
- config_name: metaworld-handle-press
data_files:
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path: metaworld-handle-press/train-*
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path: metaworld-handle-press/test-*
- config_name: metaworld-handle-press-side
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path: metaworld-handle-press-side/train-*
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path: metaworld-handle-press-side/test-*
- config_name: metaworld-handle-pull
data_files:
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path: metaworld-handle-pull/train-*
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path: metaworld-handle-pull/test-*
- config_name: metaworld-handle-pull-side
data_files:
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path: metaworld-handle-pull-side/train-*
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path: metaworld-handle-pull-side/test-*
- config_name: metaworld-lever-pull
data_files:
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path: metaworld-lever-pull/train-*
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path: metaworld-lever-pull/test-*
- config_name: metaworld-peg-insert-side
data_files:
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path: metaworld-peg-insert-side/train-*
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path: metaworld-peg-insert-side/test-*
- config_name: metaworld-peg-unplug-side
data_files:
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path: metaworld-peg-unplug-side/train-*
- split: test
path: metaworld-peg-unplug-side/test-*
- config_name: metaworld-pick-out-of-hole
data_files:
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path: metaworld-pick-out-of-hole/test-*
- config_name: metaworld-pick-place
data_files:
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path: metaworld-pick-place/train-*
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path: metaworld-pick-place/test-*
- config_name: metaworld-pick-place-wall
data_files:
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path: metaworld-pick-place-wall/test-*
- config_name: metaworld-plate-slide
data_files:
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path: metaworld-plate-slide/train-*
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path: metaworld-plate-slide/test-*
- config_name: metaworld-plate-slide-back
data_files:
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path: metaworld-plate-slide-back/test-*
- config_name: metaworld-plate-slide-back-side
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- config_name: metaworld-plate-slide-side
data_files:
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- config_name: metaworld-push
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path: metaworld-push/test-*
- config_name: metaworld-push-back
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- config_name: metaworld-push-wall
data_files:
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- config_name: metaworld-reach
data_files:
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- config_name: metaworld-reach-wall
data_files:
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- config_name: metaworld-shelf-place
data_files:
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path: metaworld-shelf-place/test-*
- config_name: metaworld-soccer
data_files:
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path: metaworld-soccer/train-*
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path: metaworld-soccer/test-*
- config_name: metaworld-stick-pull
data_files:
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- config_name: metaworld-stick-push
data_files:
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- config_name: metaworld-sweep
data_files:
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path: metaworld-sweep/train-*
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path: metaworld-sweep/test-*
- config_name: metaworld-sweep-into
data_files:
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path: metaworld-sweep-into/train-*
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path: metaworld-sweep-into/test-*
- config_name: metaworld-window-close
data_files:
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path: metaworld-window-close/train-*
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path: metaworld-window-close/test-*
- config_name: metaworld-window-open
data_files:
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path: metaworld-window-open/train-*
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path: metaworld-window-open/test-*
- config_name: mujoco-ant
data_files:
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path: mujoco-ant/train-*
- split: test
path: mujoco-ant/test-*
- config_name: mujoco-doublependulum
data_files:
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path: mujoco-doublependulum/train-*
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path: mujoco-doublependulum/test-*
- config_name: mujoco-halfcheetah
data_files:
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path: mujoco-halfcheetah/train-*
- split: test
path: mujoco-halfcheetah/test-*
- config_name: mujoco-hopper
data_files:
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path: mujoco-hopper/train-*
- split: test
path: mujoco-hopper/test-*
- config_name: mujoco-humanoid
data_files:
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path: mujoco-humanoid/train-*
- split: test
path: mujoco-humanoid/test-*
- config_name: mujoco-pendulum
data_files:
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path: mujoco-pendulum/test-*
- config_name: mujoco-pusher
data_files:
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path: mujoco-pusher/train-*
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path: mujoco-pusher/test-*
- config_name: mujoco-reacher
data_files:
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path: mujoco-reacher/train-*
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path: mujoco-reacher/test-*
- config_name: mujoco-standup
data_files:
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- config_name: mujoco-swimmer
data_files:
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- config_name: mujoco-walker
data_files:
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path: mujoco-walker/test-*
- config_name: ok-vqa
data_files:
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path: ok-vqa/train-*
- split: test
path: ok-vqa/test-*
tags:
- imitation-learning
- reinforcement-learning
- text-generation
- question-answering
- generalist-agent
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---
# GIA Dataset
## Dataset Description
The GIA dataset combines a wide range of individual datasets. It includes expert demonstrations by expert RL agents, image and caption pairs, textual data and more. The GIA dataset is part of the GIA project, which aims to build a multimodal generalist agent.
### Usage
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("gia-project/gia-dataset", "metaworld-assembly")
>>> first_episode = dataset["train"][0]
>>> first_episode.keys()
dict_keys(['continuous_observations', 'continuous_actions', 'rewards'])
>>> len(first_episode["rewards"])
500
>>> first_episode["continuous_actions"][0]
[6.459120273590088, 2.2422609329223633, -5.914587020874023, -19.799840927124023]
```
## Dataset Structure
### Data Instances
<details>
<summary>Click to expand the score information for each task</summary>
The following table presents a comparative analysis of scores across various domains and tasks. The scores highlight the performance difference between a random agent and the episodes recorded in our dataset.
| Task | Random Agent Score | Dataset Episode Score |
| ----------------------------------- | :-----------------: | :-------------------: |
| **Atari** | | |
| atari-alien | 205.50 ± 111.97 | 16912.50 ± 7087.42 |
| atari-amidar | 2.38 ± 2.50 | 2164.71 ± 1229.47 |
| atari-assault | 262.50 ± 89.61 | 15699.12 ± 9572.12 |
| atari-asterix | 213.50 ± 110.87 | 3699.62 ± 2421.30 |
| atari-asteroids | 856.40 ± 434.32 | 177011.05 ± 35334.20 |
| atari-atlantis | 17764.00 ± 6662.43 | 320679.59 ± 418247.37 |
| atari-bankheist | 13.40 ± 11.07 | 1322.43 ± 60.84 |
| atari-battlezone | 2170.00 ± 2121.58 | 295592.59 ± 161960.96 |
| atari-beamrider | 357.28 ± 143.97 | 29589.35 ± 16132.96 |
| atari-berzerk | 160.10 ± 118.87 | 57085.26 ± 13104.53 |
| atari-bowling | 23.81 ± 6.07 | 20.40 ± 7.29 |
| atari-boxing | 0.52 ± 4.37 | 97.97 ± 3.77 |
| atari-breakout | 1.24 ± 1.30 | 702.97 ± 203.62 |
| atari-centipede | 2150.06 ± 1113.28 | 11624.29 ± 4918.34 |
| atari-choppercommand | 875.00 ± 416.98 | 90990.62 ± 270876.93 |
| atari-crazyclimber | 7376.00 ± 2253.09 | 179296.94 ± 39862.06 |
| atari-defender | 3417.50 ± 1443.41 | 351958.33 ± 40466.82 |
| atari-demonattack | 165.55 ± 92.93 | 92195.25 ± 26174.79 |
| atari-doubledunk | -18.54 ± 3.07 | 20.94 ± 3.65 |
| atari-enduro | 0.00 ± 0.00 | 2292.22 ± 147.54 |
| atari-fishingderby | -93.90 ± 3.51 | 7.18 ± 25.06 |
| atari-freeway | 0.01 ± 0.10 | 33.88 ± 0.35 |
| atari-frostbite | 67.60 ± 37.61 | 13196.12 ± 4341.00 |
| atari-gopher | 319.40 ± 228.24 | 81676.15 ± 46329.48 |
| atari-gravitar | 188.50 ± 203.33 | 3986.57 ± 1729.05 |
| atari-hero | 475.25 ± 894.95 | 44677.35 ± 1754.42 |
| atari-icehockey | -9.83 ± 3.24 | 25.17 ± 5.79 |
| atari-jamesbond | 28.50 ± 45.42 | 27786.89 ± 33819.20 |
| atari-kangaroo | 52.00 ± 108.15 | 574.05 ± 636.94 |
| atari-krull | 1754.00 ± 583.56 | 11439.83 ± 1218.34 |
| atari-kungfumaster | 390.00 ± 359.03 | 32392.81 ± 10006.55 |
| atari-montezumarevenge | 0.00 ± 0.00 | 393.53 ± 50.45 |
| atari-mspacman | 246.40 ± 121.22 | 6896.08 ± 2031.99 |
| atari-namethisgame | 2447.40 ± 888.97 | 22991.18 ± 2473.15 |
| atari-phoenix | 776.80 ± 635.86 | 424583.16 ± 97649.17 |
| atari-pitfall | -259.75 ± 384.26 | -1.45 ± 4.50 |
| atari-pong | -20.22 ± 0.95 | 20.99 ± 0.18 |
| atari-privateeye | 41.65 ± 191.83 | 100.00 ± 0.00 |
| atari-qbert | 164.25 ± 151.79 | 42971.37 ± 85070.72 |
| atari-riverraid | 1474.40 ± 314.59 | 14800.94 ± 7924.56 |
| atari-roadrunner | 11.00 ± 42.18 | 77942.80 ± 6088.62 |
| atari-robotank | 1.87 ± 1.59 | 80.51 ± 13.28 |
| atari-seaquest | 73.20 ± 57.91 | 2597.34 ± 386.09 |
| atari-skiing | -16299.52 ± 1850.70 | -10738.06 ± 111.13 |
| atari-solaris | 2360.40 ± 1852.03 | 1353.68 ± 516.96 |
| atari-spaceinvaders | 137.20 ± 95.82 | 29425.29 ± 23623.89 |
| atari-stargunner | 652.00 ± 312.24 | 360588.57 ± 49207.71 |
| atari-surround | -9.99 ± 0.10 | 9.39 ± 0.85 |
| atari-tennis | -23.95 ± 0.22 | 11.11 ± 7.57 |
| atari-timepilot | 3396.00 ± 2128.85 | 69583.33 ± 29838.67 |
| atari-tutankham | 12.73 ± 17.40 | 291.16 ± 30.37 |
| atari-upndown | 358.90 ± 380.11 | 429418.33 ± 7187.43 |
| atari-venture | 0.00 ± 0.00 | 0.00 ± 0.00 |
| atari-videopinball | 23917.17 ± 19449.59 | 441507.92 ± 283264.62 |
| atari-wizardofwor | 620.00 ± 837.85 | 49333.33 ± 16157.08 |
| atari-yarsrevenge | 3503.91 ± 906.14 | 270262.86 ± 161815.96 |
| atari-zaxxon | 21.00 ± 102.27 | 73097.22 ± 14825.77 |
| **BabyAI** | | |
| babyai-action-obj-door | 0.37 ± 0.39 | 0.99 ± 0.01 |
| babyai-blocked-unlock-pickup | 0.00 ± 0.02 | 0.95 ± 0.01 |
| babyai-boss-level | 0.06 ± 0.21 | 0.94 ± 0.05 |
| babyai-boss-level-no-unlock | 0.06 ± 0.19 | 0.94 ± 0.05 |
| babyai-find-obj-s5 | 0.08 ± 0.23 | 0.95 ± 0.04 |
| babyai-go-to | 0.13 ± 0.29 | 0.92 ± 0.07 |
| babyai-go-to-door | 0.45 ± 0.38 | 0.99 ± 0.00 |
| babyai-go-to-imp-unlock | 0.08 ± 0.23 | |
| babyai-go-to-local | 0.16 ± 0.30 | 0.93 ± 0.04 |
| babyai-go-to-obj | 0.13 ± 0.27 | 0.93 ± 0.03 |
| babyai-go-to-obj-door | 0.53 ± 0.39 | 0.99 ± 0.01 |
| babyai-go-to-red-ball | 0.17 ± 0.30 | 0.93 ± 0.04 |
| babyai-go-to-red-ball-grey | 0.12 ± 0.27 | 0.92 ± 0.05 |
| babyai-go-to-red-ball-no-dists | 0.14 ± 0.28 | 0.93 ± 0.03 |
| babyai-go-to-red-blue-ball | 0.12 ± 0.27 | 0.92 ± 0.05 |
| babyai-go-to-seq | 0.08 ± 0.23 | 0.94 ± 0.05 |
| babyai-key-corridor | 0.00 ± 0.00 | 0.91 ± 0.01 |
| babyai-key-in-box | 0.01 ± 0.06 | |
| babyai-mini-boss-level | 0.07 ± 0.21 | 0.89 ± 0.10 |
| babyai-move-two-across-s8n9 | 0.00 ± 0.00 | 0.96 ± 0.01 |
| babyai-one-room-s8 | 0.08 ± 0.21 | 0.92 ± 0.03 |
| babyai-open | 0.10 ± 0.24 | 0.95 ± 0.05 |
| babyai-open-door | 0.23 ± 0.34 | 0.99 ± 0.00 |
| babyai-open-doors-order-n4 | 0.16 ± 0.30 | 0.99 ± 0.01 |
| babyai-open-red-door | 0.08 ± 0.21 | 0.92 ± 0.03 |
| babyai-open-two-doors | 0.08 ± 0.20 | 0.98 ± 0.00 |
| babyai-pickup | 0.08 ± 0.22 | 0.92 ± 0.07 |
| babyai-pickup-above | 0.02 ± 0.09 | 0.91 ± 0.07 |
| babyai-pickup-dist | 0.10 ± 0.24 | 0.86 ± 0.21 |
| babyai-pickup-loc | 0.08 ± 0.23 | 0.91 ± 0.04 |
| babyai-synth | 0.11 ± 0.26 | 0.93 ± 0.06 |
| babyai-synth-loc | 0.13 ± 0.29 | 0.94 ± 0.06 |
| babyai-synth-seq | 0.07 ± 0.20 | 0.95 ± 0.04 |
| babyai-unblock-pickup | 0.08 ± 0.22 | 0.91 ± 0.08 |
| babyai-unlock | 0.03 ± 0.15 | |
| babyai-unlock-local | 0.01 ± 0.09 | 0.98 ± 0.01 |
| babyai-unlock-pickup | 0.00 ± 0.00 | 0.75 ± 0.04 |
| babyai-unlock-to-unlock | 0.00 ± 0.00 | |
| **MetaWorld** | | |
| metaworld-assembly | 45.30 ± 4.13 | 245.99 ± 3.50 |
| metaworld-basketball | 2.81 ± 1.24 | 627.99 ± 1.98 |
| metaworld-bin-picking | 1.89 ± 0.45 | 425.58 ± 101.86 |
| metaworld-box-close | 76.39 ± 17.91 | 512.49 ± 107.81 |
| metaworld-button-press | 31.73 ± 5.20 | 643.10 ± 12.85 |
| metaworld-button-press-topdown | 28.97 ± 10.37 | 490.18 ± 27.21 |
| metaworld-button-press-topdown-wall | 29.04 ± 10.52 | 497.19 ± 31.37 |
| metaworld-button-press-wall | 8.98 ± 3.99 | 675.41 ± 15.04 |
| metaworld-coffee-button | 31.72 ± 6.36 | 731.08 ± 29.34 |
| metaworld-coffee-pull | 4.09 ± 0.38 | 259.86 ± 88.48 |
| metaworld-coffee-push | 4.17 ± 0.76 | 496.78 ± 118.20 |
| metaworld-dial-turn | 29.64 ± 16.67 | 793.56 ± 80.06 |
| metaworld-disassemble | 40.31 ± 7.53 | 42.83 ± 6.30 |
| metaworld-door-close | 5.30 ± 1.33 | 529.75 ± 27.24 |
| metaworld-door-lock | 112.35 ± 28.63 | 811.52 ± 34.07 |
| metaworld-door-open | 56.37 ± 11.23 | 581.94 ± 19.67 |
| metaworld-door-unlock | 94.17 ± 15.56 | 802.88 ± 17.05 |
| metaworld-drawer-close | 116.73 ± 253.11 | 867.92 ± 4.48 |
| metaworld-drawer-open | 126.85 ± 25.22 | 492.99 ± 2.52 |
| metaworld-faucet-close | 253.12 ± 22.94 | 753.92 ± 13.42 |
| metaworld-faucet-open | 244.10 ± 23.25 | 705.76 ± 7.15 |
| metaworld-hammer | 95.33 ± 9.02 | 693.17 ± 34.62 |
| metaworld-hand-insert | 2.75 ± 3.53 | 740.53 ± 36.69 |
| metaworld-handle-press | 80.41 ± 110.19 | 855.91 ± 72.75 |
| metaworld-handle-press-side | 57.00 ± 39.47 | 861.12 ± 20.01 |
| metaworld-handle-pull | 10.34 ± 13.54 | 669.35 ± 24.81 |
| metaworld-handle-pull-side | 2.13 ± 2.76 | 384.65 ± 102.89 |
| metaworld-lever-pull | 60.31 ± 15.77 | 612.04 ± 38.85 |
| metaworld-peg-insert-side | 1.71 ± 0.36 | 315.23 ± 140.07 |
| metaworld-peg-unplug-side | 4.75 ± 2.83 | 456.12 ± 81.65 |
| metaworld-pick-out-of-hole | 1.51 ± 0.24 | 219.61 ± 88.85 |
| metaworld-pick-place | 1.61 ± 0.99 | 419.10 ± 98.19 |
| metaworld-pick-place-wall | 0.00 ± 0.01 | 450.57 ± 64.10 |
| metaworld-plate-slide | 74.64 ± 13.84 | 527.01 ± 155.34 |
| metaworld-plate-slide-back | 33.47 ± 11.22 | 718.22 ± 87.41 |
| metaworld-plate-slide-back-side | 34.34 ± 11.53 | 729.61 ± 69.15 |
| metaworld-plate-slide-side | 22.61 ± 17.36 | 662.81 ± 102.81 |
| metaworld-push | 5.51 ± 2.43 | 750.57 ± 43.98 |
| metaworld-push-back | 1.21 ± 0.16 | 85.05 ± 107.12 |
| metaworld-push-wall | 6.13 ± 3.17 | 748.87 ± 10.62 |
| metaworld-reach | 149.67 ± 44.70 | 681.37 ± 133.68 |
| metaworld-reach-wall | 143.26 ± 36.56 | 746.12 ± 104.19 |
| metaworld-shelf-place | 0.00 ± 0.01 | 241.34 ± 24.60 |
| metaworld-soccer | 5.66 ± 4.61 | 375.15 ± 140.24 |
| metaworld-stick-pull | 2.64 ± 1.41 | 523.55 ± 18.94 |
| metaworld-stick-push | 2.81 ± 1.04 | 627.95 ± 10.20 |
| metaworld-sweep | 11.23 ± 7.28 | 494.85 ± 43.29 |
| metaworld-sweep-into | 12.55 ± 10.72 | 799.21 ± 19.07 |
| metaworld-window-close | 57.46 ± 7.11 | 591.30 ± 38.63 |
| metaworld-window-open | 43.36 ± 2.09 | 590.82 ± 57.08 |
| **MuJoCo** | | |
| mujoco-ant | -59.95 ± 99.62 | 5846.42 ± 942.55 |
| mujoco-doublependulum | 57.46 ± 17.54 | 9338.69 ± 352.61 |
| mujoco-halfcheetah | -284.97 ± 79.83 | 7437.77 ± 173.30 |
| mujoco-hopper | 18.38 ± 17.09 | 1858.73 ± 534.07 |
| mujoco-humanoid | 122.02 ± 35.28 | 6281.02 ± 1795.84 |
| mujoco-pendulum | 6.07 ± 3.47 | 475.40 ± 178.96 |
| mujoco-pusher | -149.69 ± 7.41 | -25.21 ± 6.66 |
| mujoco-reacher | -43.00 ± 3.91 | -5.68 ± 2.53 |
| mujoco-standup | 33135.75 ± 2481.89 | 273574.16 ± 85253.26 |
| mujoco-swimmer | 0.80 ± 10.71 | 92.18 ± 4.44 |
| mujoco-walker | 2.68 ± 6.06 | 4631.22 ± 1059.01 |
</details>
### Data Fields
- `text`: a `string` feature
- `images`: a `image` feature
- `image_observations` : a `Sequence(image)` feature
- `text_observations` : a `Sequence(string)` feature
- `discrete_observations`: a `Sequence(Sequence(int64))` feature
- `continuous_observations`: a `Sequence(Sequence(float32))` feature
- `continuous_actions`: a `Sequence(Sequence(float32))` feature
- `discrete_actions`: a `Sequence(int64)` feature
- `rewards`: a `Sequence(float32)` feature
### Data Splits
- `train`: `` examples
- `test`: `` examples
## Dataset Creation
This section describes how our dataset was created. We specifically detail how data for each domain and task were generated. The generation scripts are available in the [GIA repository](https://github.com/huggingface/gia). For RL tasks, we trained one agent per task using the [Sample Factory](https://www.samplefactory.dev). Then we used the trained agent to generate episodes.
### Atari
We used the 57 [ALE/Atari](https://github.com/Farama-Foundation/Arcade-Learning-Environment) games as our environment, configuring the following parameters for our experiments. We rendered the images in grayscale with an 84x84 pixel resolution. The agent interacted with the environment every 4 frames. Sticky actions were not used, and the raw reward (no clipping) was reported. Episodes were stored as complete, i.e. with no termination on life loss.
### BabyAI
We used BabyAI's implementation from [Minigrid](https://github.com/Farama-Foundation/Minigrid).
We reused the [bot agent](https://github.com/mila-iqia/babyai) provided with BabyAI's paper and adapted it to the new Minigrid API.
Using the bot, we generated 100.000 episodes for each of the 39 tasks of [Minigrid's BabyAI](https://minigrid.farama.org/environments/babyai/) and stored for each step:
- the mission: str
- the concatenation of the symbolic observation flattened and the direction: Array of integers of size (147,)
- the action: integer
- the reward: float
### Conceptual Captions
The [Conceptual Captions](https://github.com/google-research-datasets/conceptual-captions/tree/master) dataset, offered by Google LLC, comprises pairs of image links and their corresponding captions. Each image has been downloaded and, when required, resized to ensure the maximum dimension does not exceed 352 pixels.
### MetaWorld
We used the 50 tasks from [MetaWorld v2](https://github.com/Farama-Foundation/Metaworld). We constrained the episode to a duration of 100 timesteps, which is always sufficient to solve the task.
### MuJoCo
We used the 11 environments of Gymnasium MuJoCo.
### OK-VQA
The [OK-VQA](https://okvqa.allenai.org/index.html) dataset released by Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi was used.
The data were formatted to match Hugging Face dataset's requirements and images were resized such that the largest dimension is at most 352.
### OSCAR
We modified the "unshuffled_deduplicated_en" split of [OSCAR 2019](https://huggingface.co/datasets/oscar) dataset, initially put together by Pedro J. Ortiz, Benoît Sagot, and Laurent Romary and licensed under [CC BY 4.0](https://oscar-project.github.io/documentation/versions/oscar-2019/#license).
We cleaned and deduplicated the dataset using [the methods](https://github.com/bigscience-workshop/data-preparation/tree/main/preprocessing/training/01b_oscar_cleaning_and_filtering) and parameters used for the [ROOTS dataset](https://arxiv.org/abs/2303.03915) (Lurençon et al., 2023).
The dataset was splitted into 30 even shards each cleaned and deduplicated independently before being concatenated again.
## Considerations for Using the Data
### Known Issues
- Some BabyAI tasks are missing due to incompatibility with the training bot:
- `babyai-key-in-box`
- `babyai-go-to-imp-unlock`
- `babyai-unlock-to-unlock`
- `babyai-unlock`
- For some atari tasks, the episode is too long, causing an `OverflowError` when loading the dataset:
- `atari-enduro`
- For some tasks, although the score can be higher than the random agent, we can't consider the task as solved:
- `atari-bowling`
- `atari-privateeye`
- `atari-solaris`
- `atari-venture`
- `metaworld-bin-picking`
- `metaworld-disassemble`
- `metaworld-peg-insert-side`
- `metaworld-plate-slide`
- `metaworld-push-back`
### Future Developments
We plan to expand the dataset to include the following additional domains:
- [ ] DM Lab
- [ ] Sokoban
- [ ] Procgen
- [ ] DM Control Suite (w and w/o pixels)
## Additional Information
### Licensing Information
This dataset is release under the Apache 2.0 license.
### Citation Information
```bibtex
@misc{gallouedec2023giadataset,
title={GIA Dataset: A Multi-Modal, Multi-Task Learning Resource},
author={Gallouédec, Quentin and Beeching, Edward and Romac, Clément},
year={2023},
howpublished={\url{https://huggingface.co/datasets/gia-project/gia-dataset}},
note={Part of the GIA Project}
}
```
## Acknowledgment
We would like to extend our sincere gratitude to:
- [Shengyi Costa Huang](https://huggingface.co/vwxyzjn) for his invaluable assistance with the pretrained models used in this research |
seamew/THUCNewsText | 2021-08-19T00:04:34.000Z | [
"region:us"
] | seamew | null | null | null | 4 | 8,285 | Entry not found |
hf-internal-testing/cats_vs_dogs_sample | 2023-04-11T17:04:37.000Z | [
"region:us"
] | hf-internal-testing | null | \\n@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
year = {2007},
month = {October},
publisher = {Association for Computing Machinery, Inc.},
url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},
edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
} | null | 0 | 8,128 | Entry not found |
BigScienceBiasEval/crows_pairs_multilingual | 2022-04-26T16:26:28.000Z | [
"license:cc-by-sa-4.0",
"arxiv:2010.00133",
"region:us"
] | BigScienceBiasEval | This is a revised version of CrowS-Pairs that measures stereotypes in language modelling in both English and French. | @inproceedings{neveol2022french,
title={French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English},
author={N{\'e}v{\'e}ol, Aur{\'e}lie and Dupont, Yoann and Bezan{\c{c}}on, Julien and Fort, Kar{\"e}n},
booktitle={ACL 2022-60th Annual Meeting of the Association for Computational Linguistics},
year={2022}
} | null | 2 | 8,099 | ---
license: cc-by-sa-4.0
---
Original from https://gitlab.inria.fr/french-crows-pairs/acl-2022-paper-data-and-code/-/tree/main/.
# Data Statement for CrowS-Pairs-fr
> **How to use this document:**
> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.
> For full details, the best source is the original Data Statements paper, here: https://www.aclweb.org/anthology/Q18-1041/ .
> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as "DATASTATEMENT.md". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.
> Only blockquoted content should be deleted; the final about statement should be left intact.
Data set name: Crows-Pairs-fr
Citation (if available): Névéol A, Dupont Y, Bezançon J, Fort K. French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics - ACL 2022
Data set developer(s): Aurélie Névéol, Yoann Dupont, Julien Bezançon, Karën Fort
Data statement author(s): Aurélie Névéol, Yoann Dupont
Others who contributed to this document: N/A
License: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).
## A. CURATION RATIONALE
> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.
The French part of the corpus was built by first translating the original 1,508 sentence pairs of the English corpus into French.
We then adapted the crowdsourcing method described by [Nangia et al. (2020)](https://arxiv.org/pdf/2010.00133) to collect additional sentences expressing a stereotype relevant to the French socio-cultural environment. Data collection is implemented through LanguageARC [(Fiumara et al., 2020)](https://www.aclweb.org/anthology/2020.cllrd-1.1.pdf), a citizen science platform supporting the development of language resources dedicated to social improvement. We created a LanguageARC project (https://languagearc.com/projects/19) to collect these additional sentences. Participants were asked to submit a statement that expressed a stereotype in French along with a selection of ten bias types: the nine bias types offered in CrowS-Pairs and the additional category _other_. We collected 210 additional sentences this way.
## B. LANGUAGE VARIETY/VARIETIES
> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., "English as spoken in Palo Alto, California", or "Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin").
* BCP-47 language tags: fr-FR
* Language variety description: French spoken by native French people from metropolitan France.
## C. CONTRIBUTOR DEMOGRAPHIC
> ## C. SPEAKER DEMOGRAPHIC
> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include:
N/A
## D. ANNOTATOR DEMOGRAPHIC
> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own “social address” influences their experience with language and thus their perception of what they are annotating. Specifications include:
Participants to the collection project were recruited through calls for volunteers posted to social media and mailing lists in the French research community.
## E. SPEECH SITUATION
N/A
## F. TEXT CHARACTERISTICS
> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.
Collected data is a collection of offensive stereotyped statements in French, they might be upsetting.
Along these stereotyped statements are paired anti-stereotyped statements.
## G. RECORDING QUALITY
N/A
## H. OTHER
> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset.
## I. PROVENANCE APPENDIX
Examples were gathered using the LanguageArc site and by creating a dedicated project: https://languagearc.com/projects/19
## About this document
A data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.
Data Statements are from the University of Washington. Contact: [datastatements@uw.edu](mailto:datastatements@uw.edu). This document template is licensed as [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/).
This version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the [2020 LREC workshop on Data Statements](https://sites.google.com/uw.edu/data-statements-for-nlp/), by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski.
|
opus_books | 2022-11-03T16:47:07.000Z | [
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"language:nl",
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"language:pl",
"language:pt",
"language:ru",
"language:sv",
"license:unknown",
"region:us"
] | null | This is a collection of copyright free books aligned by Andras Farkas, which are available from http://www.farkastranslations.com/bilingual_books.php
Note that the texts are rather dated due to copyright issues and that some of them are manually reviewed (check the meta-data at the top of the corpus files in XML). The source is multilingually aligned, which is available from http://www.farkastranslations.com/bilingual_books.php. In OPUS, the alignment is formally bilingual but the multilingual alignment can be recovered from the XCES sentence alignment files. Note also that the alignment units from the original source may include multi-sentence paragraphs, which are split and sentence-aligned in OPUS.
All texts are freely available for personal, educational and research use. Commercial use (e.g. reselling as parallel books) and mass redistribution without explicit permission are not granted. Please acknowledge the source when using the data!
16 languages, 64 bitexts
total number of files: 158
total number of tokens: 19.50M
total number of sentence fragments: 0.91M | @InProceedings{TIEDEMANN12.463,
author = {J�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}
} | null | 15 | 7,964 | ---
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num_examples: 26127
download_size: 2469765
dataset_size: 7818688
- config_name: it-ru
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- it
- ru
splits:
- name: train
num_bytes: 5316952
num_examples: 17906
download_size: 1620478
dataset_size: 5316952
- config_name: en-sv
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- sv
splits:
- name: train
num_bytes: 790785
num_examples: 3095
download_size: 304975
dataset_size: 790785
- config_name: fr-sv
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- fr
- sv
splits:
- name: train
num_bytes: 833553
num_examples: 3002
download_size: 321660
dataset_size: 833553
- config_name: it-sv
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- it
- sv
splits:
- name: train
num_bytes: 811413
num_examples: 2998
download_size: 307821
dataset_size: 811413
---
# Dataset Card for OpusBooks
## 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/Books.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. |
lamini/lamini_docs | 2023-07-23T23:48:57.000Z | [
"region:us"
] | lamini | null | null | null | 6 | 7,760 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 1846734.3
num_examples: 1260
- name: test
num_bytes: 205192.7
num_examples: 140
download_size: 698607
dataset_size: 2051927.0
---
# Dataset Card for "lamini_docs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco | 2023-09-17T20:27:35.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 7,747 | ---
pretty_name: Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mctaco
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Charlie911/vicuna-7b-v1.5-lora-mctaco](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco)\
\ 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_Charlie911__vicuna-7b-v1.5-lora-mctaco\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T20:27:23.554125](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco/blob/main/results_2023-09-17T20-27-23.554125.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\": 0.2488464765100671,\n\
\ \"em_stderr\": 0.004427614016278926,\n \"f1\": 0.28849937080536914,\n\
\ \"f1_stderr\": 0.00442953185165108,\n \"acc\": 0.372010258662628,\n\
\ \"acc_stderr\": 0.00929094831305589\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.2488464765100671,\n \"em_stderr\": 0.004427614016278926,\n\
\ \"f1\": 0.28849937080536914,\n \"f1_stderr\": 0.00442953185165108\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04473085670962851,\n \
\ \"acc_stderr\": 0.005693886131407047\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6992896606156275,\n \"acc_stderr\": 0.012888010494704732\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|arc:challenge|25_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|arc:challenge|25_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T20_27_23.554125
path:
- '**/details_harness|drop|3_2023-09-17T20-27-23.554125.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T20-27-23.554125.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T20_27_23.554125
path:
- '**/details_harness|gsm8k|5_2023-09-17T20-27-23.554125.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T20-27-23.554125.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hellaswag|10_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hellaswag|10_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet'
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- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet'
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- split: latest
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T20_27_23.554125
path:
- '**/details_harness|winogrande|5_2023-09-17T20-27-23.554125.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T20-27-23.554125.parquet'
- config_name: results
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- results_2023-09-01T09:00:53.100273.parquet
- split: 2023_09_01T09_03_24.370765
path:
- results_2023-09-01T09:03:24.370765.parquet
- split: 2023_09_17T20_27_23.554125
path:
- results_2023-09-17T20-27-23.554125.parquet
- split: latest
path:
- results_2023-09-17T20-27-23.554125.parquet
---
# Dataset Card for Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mctaco
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Charlie911/vicuna-7b-v1.5-lora-mctaco](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco) 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_Charlie911__vicuna-7b-v1.5-lora-mctaco",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T20:27:23.554125](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco/blob/main/results_2023-09-17T20-27-23.554125.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": 0.2488464765100671,
"em_stderr": 0.004427614016278926,
"f1": 0.28849937080536914,
"f1_stderr": 0.00442953185165108,
"acc": 0.372010258662628,
"acc_stderr": 0.00929094831305589
},
"harness|drop|3": {
"em": 0.2488464765100671,
"em_stderr": 0.004427614016278926,
"f1": 0.28849937080536914,
"f1_stderr": 0.00442953185165108
},
"harness|gsm8k|5": {
"acc": 0.04473085670962851,
"acc_stderr": 0.005693886131407047
},
"harness|winogrande|5": {
"acc": 0.6992896606156275,
"acc_stderr": 0.012888010494704732
}
}
```
### 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] |
yahma/alpaca-cleaned | 2023-04-10T20:29:06.000Z | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"instruction-finetuning",
"region:us"
] | yahma | null | null | null | 246 | 7,741 | ---
license: cc-by-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: Alpaca-Cleaned
task_categories:
- text-generation
---
# Dataset Card for Alpaca-Cleaned
- **Repository:** https://github.com/gururise/AlpacaDataCleaned
## Dataset Description
This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
1. **Hallucinations:** Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
```
"instruction":"Summarize the given article in 200 Words.",
"input": "https://www.bbc.com/news/world-51461830",
"output": "The recent protests in Belarus are part of a growing wave of discontent against the rule of Alexander Lukashenko, who has led the country since 1994. Demonstrations against his government have been ongoing since August, and come in the wake of an election widely seen as rigged. As police and security forces have cracked down on protesters, the movement has only grown, with hundreds of thousands of people taking to the streets to call for new leadership. Human rights activists have accused the government of using extreme violence in order prosecute protesters, leading to an international outcry and sanctions from the European Union. As the situation in Belarus continues to unfold, the international community is monitoring the situation closely - hoping for a peaceful resolution, while also standing firm against the oppressive tactics employed by the Belarusian state."
"instruction": "Classify the following shape according to simple geometry.",
"input": "<Picture Attached>",
"output": "Triangle"
"instruction": "Given a recent news article, write a headline for it.",
"input": "https://www.news.example.com/article-1",
"output": "\"NASA Launches a Mars 2020 Mission to Search for Signs of Ancient Life\""
```
2. **Merged Instructions:** There were many instructions that were merged together in the original dataset for some reason.
```
"instruction": "Should I invest in stocks?",
"input": "",
"output": "It depends on your individual situation. Investing in stocks carries a certain amount of risk but can also offer potential rewards. Make sure to research the different types of stocks, understand the risks and rewards, and develop a plan that works for you.\n##\n18. Instruction: Identify the noun in the following sentence:\n18. Input:\nThe large apple was delicious.\n18. Output:\nApple"
```
3. **Empty outputs:** Some entries in the original dataset had empty outputs.
```
"instruction": "Calculate the area of the following shape in square centimeters.",
"input": "rectangle of size 4 cm x 5 cm",
"output": ""
```
4. **Empty code examples:** Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code.
5. **Instructions to generate images:** Some descriptions in the original dataset included instructions to generate images, something obviously not possible.
```
"instruction": "Create a graphic or logo that visually represents the word \"courage\".",
"input": "",
"output": "<No Output>"
```
6. **N/A outputs:** Some code snippets in the original dataset had N/A outputs.
7. **Inconsistent input field:** The original dataset had inconsistent usage of the input field when it was supposed to be empty.
```
"input":"<no input>"
"input":"No input"
"input":"noinput"
"input":"<noinput>"
```
8. **Wrong answers:** Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers.
```
"instruction": "Calculate the median of the following data set.",
"input": "1, 2, 4, 5, 8, 9",
"output": "5"
"instruction": "Convert 25m to km.",
"input": "",
"output": "25km"
```
9. **Non-Sensical/Unclear instructions:** Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered.
```
"instruction": "Freeze the following sample of yogurt for 10 minutes.",
"input": "Yogurt sample",
"output": "<noinput>"
"instruction": "Increase the font size to 12 points.",
"input": "",
"output": "The font size has been increased to 12 points."
```
10. **Extraneous escape and control characters:** The original dataset had several entries with extraneous escape and control characters.
### Original Alpaca Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:
- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.
This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl).
### Supported Tasks and Leaderboards
The Alpaca dataset designed for instruction training pretrained language models.
### Languages
The data in Alpaca are in English (BCP-47 en).
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"instruction": "Create a classification task by clustering the given list of items.",
"input": "Apples, oranges, bananas, strawberries, pineapples",
"output": "Class 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
"text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a classification task by clustering the given list of items.\n\n### Input:\nApples, oranges, bananas, strawberries, pineapples\n\n### Response:\nClass 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
}
```
### Data Fields
The data fields are as follows:
* `instruction`: describes the task the model should perform. Each of the 52K instructions is unique.
* `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input.
* `output`: the answer to the instruction as generated by `text-davinci-003`.
* `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors for fine-tuning their models.
### Data Splits
| | train |
|---------------|------:|
| alpaca | 52002 |
## 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
Excerpt the [blog post](https://crfm.stanford.edu/2023/03/13/alpaca.html) accompanying the release of this dataset:
> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
The `alpaca` data is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}
```
### Contributions
[More Information Needed] |
garage-bAInd/Open-Platypus | 2023-09-17T16:56:19.000Z | [
"size_categories:10K<n<100K",
"language:en",
"arxiv:2308.07317",
"region:us"
] | garage-bAInd | null | null | null | 225 | 7,535 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
- name: data_source
dtype: string
splits:
- name: train
num_bytes: 30776452
num_examples: 24926
download_size: 15565850
dataset_size: 30776452
language:
- en
size_categories:
- 10K<n<100K
---
# OpenPlatypus
This dataset is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. It is comprised of the following datasets, which were filtered using keyword search and then Sentence Transformers to remove questions with a similarity above 80%:
| Dataset Name | License Type |
|--------------------------------------------------------------|--------------|
| [PRM800K](https://github.com/openai/prm800k) | MIT |
| [ScienceQA](https://github.com/lupantech/ScienceQA) | [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
| [SciBench](https://github.com/mandyyyyii/scibench) | MIT |
| [ReClor](https://whyu.me/reclor/) | Non-commercial |
| [TheoremQA](https://huggingface.co/datasets/wenhu/TheoremQA) | MIT |
| [`nuprl/leetcode-solutions-python-testgen-gpt4`](https://huggingface.co/datasets/nuprl/leetcode-solutions-python-testgen-gpt4/viewer/nuprl--leetcode-solutions-python-testgen-gpt4/train?p=1) | None listed |
| [`jondurbin/airoboros-gpt4-1.4.1`](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1) | other |
| [`TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k`](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k/viewer/TigerResearch--tigerbot-kaggle-leetcodesolutions-en-2k/train?p=2) | apache-2.0 |
| [openbookQA](https://huggingface.co/datasets/openbookqa/viewer/additional/train?row=35) | apache-2.0 |
| [ARB](https://arb.duckai.org) | MIT |
| [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) | apache-2.0 |
## Data Contamination Check
We've removed approximately 200 questions that appear in the Hugging Face benchmark test sets. Please see our [paper](https://arxiv.org/abs/2308.07317) and [project webpage](https://platypus-llm.github.io) for additional information.
## Model Info
Please see models at [`garage-bAInd`](https://huggingface.co/garage-bAInd).
## Training and filtering code
Please see the [Platypus GitHub repo](https://github.com/arielnlee/Platypus).
## Citations
```bibtex
@article{platypus2023,
title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs},
author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz},
booktitle={arXiv preprint arxiv:2308.07317},
year={2023}
}
```
```bibtex
@article{lightman2023lets,
title={Let's Verify Step by Step},
author={Lightman, Hunter and Kosaraju, Vineet and Burda, Yura and Edwards, Harri and Baker, Bowen and Lee, Teddy and Leike, Jan and Schulman, John and Sutskever, Ilya and Cobbe, Karl},
journal={preprint arXiv:2305.20050},
year={2023}
}
```
```bibtex
@inproceedings{lu2022learn,
title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
year={2022}
}
```
```bibtex
@misc{wang2023scibench,
title={SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models},
author={Xiaoxuan Wang and Ziniu Hu and Pan Lu and Yanqiao Zhu and Jieyu Zhang and Satyen Subramaniam and Arjun R. Loomba and Shichang Zhang and Yizhou Sun and Wei Wang},
year={2023},
arXiv eprint 2307.10635
}
```
```bibtex
@inproceedings{yu2020reclor,
author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
booktitle = {International Conference on Learning Representations (ICLR)},
month = {April},
year = {2020}
}
```
```bibtex
@article{chen2023theoremqa,
title={TheoremQA: A Theorem-driven Question Answering dataset},
author={Chen, Wenhu and Ming Yin, Max Ku, Elaine Wan, Xueguang Ma, Jianyu Xu, Tony Xia, Xinyi Wang, Pan Lu},
journal={preprint arXiv:2305.12524},
year={2023}
}
```
```bibtex
@inproceedings{OpenBookQA2018,
title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},
author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal},
booktitle={EMNLP},
year={2018}
}
```
```bibtex
@misc{sawada2023arb,
title={ARB: Advanced Reasoning Benchmark for Large Language Models},
author={Tomohiro Sawada and Daniel Paleka and Alexander Havrilla and Pranav Tadepalli and Paula Vidas and Alexander Kranias and John J. Nay and Kshitij Gupta and Aran Komatsuzaki},
arXiv eprint 2307.13692,
year={2023}
}
``` |
wiki_dpr | 2023-04-05T13:43:12.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"license:gfdl",
"text-search",
"arxiv:2004.04906",
"region:us"
] | null | This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model.
It contains 21M passages from wikipedia along with their DPR embeddings.
The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages. | @misc{karpukhin2020dense,
title={Dense Passage Retrieval for Open-Domain Question Answering},
author={Vladimir Karpukhin and Barlas Oğuz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih},
year={2020},
eprint={2004.04906},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 18 | 7,498 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
- gfdl
multilinguality:
- multilingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
task_ids:
- language-modeling
- masked-language-modeling
pretty_name: Wiki-DPR
tags:
- text-search
dataset_info:
- config_name: psgs_w100.nq.exact
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 78419281788
num_examples: 21015300
download_size: 70965697456
dataset_size: 78419281788
- config_name: psgs_w100.nq.compressed
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 78419281788
num_examples: 21015300
download_size: 70965697456
dataset_size: 78419281788
- config_name: psgs_w100.nq.no_index
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 78419281788
num_examples: 21015300
download_size: 70965697456
dataset_size: 78419281788
- config_name: psgs_w100.multiset.exact
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 78419281788
num_examples: 21015300
download_size: 70965697456
dataset_size: 78419281788
- config_name: psgs_w100.multiset.compressed
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 78419281788
num_examples: 21015300
download_size: 70965697456
dataset_size: 78419281788
- config_name: psgs_w100.multiset.no_index
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 78419281788
num_examples: 21015300
download_size: 70965697456
dataset_size: 78419281788
---
# Dataset Card for "wiki_dpr"
## 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/facebookresearch/DPR](https://github.com/facebookresearch/DPR)
- **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:** 425.79 GB
- **Size of the generated dataset:** 470.52 GB
- **Total amount of disk used:** 978.05 GB
### Dataset Summary
This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model.
It contains 21M passages from wikipedia along with their DPR embeddings.
The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages.
The wikipedia dump is the one from Dec. 20, 2018.
### 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
Each instance contains a paragraph of at most 100 words, as well as the title of the wikipedia page it comes from, and the DPR embedding (a 768-d vector).
#### psgs_w100.multiset.compressed
- **Size of downloaded dataset files:** 70.97 GB
- **Size of the generated dataset:** 78.42 GB
- **Total amount of disk used:** 152.26 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{'id': '1',
'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'],
'title': 'Aaron',
'embeddings': [-0.07233893871307373,
0.48035329580307007,
0.18650995194911957,
-0.5287084579467773,
-0.37329429388046265,
0.37622880935668945,
0.25524479150772095,
...
-0.336689829826355,
0.6313082575798035,
-0.7025573253631592]}
```
#### psgs_w100.multiset.exact
- **Size of downloaded dataset files:** 70.97 GB
- **Size of the generated dataset:** 78.42 GB
- **Total amount of disk used:** 187.38 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{'id': '1',
'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'],
'title': 'Aaron',
'embeddings': [-0.07233893871307373,
0.48035329580307007,
0.18650995194911957,
-0.5287084579467773,
-0.37329429388046265,
0.37622880935668945,
0.25524479150772095,
...
-0.336689829826355,
0.6313082575798035,
-0.7025573253631592]}
```
#### psgs_w100.multiset.no_index
- **Size of downloaded dataset files:** 70.97 GB
- **Size of the generated dataset:** 78.42 GB
- **Total amount of disk used:** 149.38 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{'id': '1',
'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'],
'title': 'Aaron',
'embeddings': [-0.07233893871307373,
0.48035329580307007,
0.18650995194911957,
-0.5287084579467773,
-0.37329429388046265,
0.37622880935668945,
0.25524479150772095,
...
-0.336689829826355,
0.6313082575798035,
-0.7025573253631592]}
```
#### psgs_w100.nq.compressed
- **Size of downloaded dataset files:** 70.97 GB
- **Size of the generated dataset:** 78.42 GB
- **Total amount of disk used:** 152.26 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{'id': '1',
'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'],
'title': 'Aaron',
'embeddings': [0.013342111371457577,
0.582173764705658,
-0.31309744715690613,
-0.6991612911224365,
-0.5583199858665466,
0.5187504887580872,
0.7152731418609619,
...
-0.5385938286781311,
0.8093984127044678,
-0.4741983711719513]}
```
#### psgs_w100.nq.exact
- **Size of downloaded dataset files:** 70.97 GB
- **Size of the generated dataset:** 78.42 GB
- **Total amount of disk used:** 187.38 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{'id': '1',
'text': 'Aaron Aaron ( or ; "Ahärôn") is a prophet, high priest, and the brother of Moses in the Abrahamic religions. Knowledge of Aaron, along with his brother Moses, comes exclusively from religious texts, such as the Bible and Quran. The Hebrew Bible relates that, unlike Moses, who grew up in the Egyptian royal court, Aaron and his elder sister Miriam remained with their kinsmen in the eastern border-land of Egypt (Goshen). When Moses first confronted the Egyptian king about the Israelites, Aaron served as his brother\'s spokesman ("prophet") to the Pharaoh. Part of the Law (Torah) that Moses received from'],
'title': 'Aaron',
'embeddings': [0.013342111371457577,
0.582173764705658,
-0.31309744715690613,
-0.6991612911224365,
-0.5583199858665466,
0.5187504887580872,
0.7152731418609619,
...
-0.5385938286781311,
0.8093984127044678,
-0.4741983711719513]}
```
### Data Fields
The data fields are the same among all splits.
#### psgs_w100.multiset.compressed
- `id`: a `string` feature.
- `text`: a `string` feature.
- `title`: a `string` feature.
- `embeddings`: a `list` of `float32` features.
#### psgs_w100.multiset.exact
- `id`: a `string` feature.
- `text`: a `string` feature.
- `title`: a `string` feature.
- `embeddings`: a `list` of `float32` features.
#### psgs_w100.multiset.no_index
- `id`: a `string` feature.
- `text`: a `string` feature.
- `title`: a `string` feature.
- `embeddings`: a `list` of `float32` features.
#### psgs_w100.nq.compressed
- `id`: a `string` feature.
- `text`: a `string` feature.
- `title`: a `string` feature.
- `embeddings`: a `list` of `float32` features.
#### psgs_w100.nq.exact
- `id`: a `string` feature.
- `text`: a `string` feature.
- `title`: a `string` feature.
- `embeddings`: a `list` of `float32` features.
### Data Splits
| name | train |
|-----------------------------|-------:|
|psgs_w100.multiset.compressed|21015300|
|psgs_w100.multiset.exact |21015300|
|psgs_w100.multiset.no_index |21015300|
|psgs_w100.nq.compressed |21015300|
|psgs_w100.nq.exact |21015300|
## 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
```
@misc{karpukhin2020dense,
title={Dense Passage Retrieval for Open-Domain Question Answering},
author={Vladimir Karpukhin and Barlas Oğuz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih},
year={2020},
eprint={2004.04906},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
roneneldan/TinyStories | 2023-08-16T16:54:12.000Z | [
"arxiv:2305.07759",
"region:us"
] | roneneldan | null | null | null | 251 | 7,470 | License: CDLA-Sharing-1.0
-------------
Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M.
There are two more resources:
tinystories_all_data.tar.gz - contains a superset of the stories together with metadata and the prompt that was used to create each story.
TinyStoriesV2-GPT4-train.txt - Is a new version of the dataset that is based on generations by GPT-4 only (the original dataset also has generations by GPT-3.5 which are of lesser quality). It contains all the examples in TinyStories.txt which were GPT-4 generated as a subset (but is significantly larger).
|
bigcode/the-stack-dedup | 2023-08-17T08:21:58.000Z | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:other",
"arxiv:2211.15533",
"arxiv:2107.03374",
"arxiv:2207.14157",
"region:us"
] | bigcode | null | null | null | 234 | 7,452 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- other
multilinguality:
- multilingual
pretty_name: The-Stack
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids: []
extra_gated_prompt: |-
## Terms of Use for The Stack
The Stack dataset is a collection of source code in over 300 programming languages. We ask that you read and acknowledge the following points before using the dataset:
1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.
2. The Stack is regularly updated to enact validated data removal requests. By clicking on "Access repository", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset’s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes.
3. To host, share, or otherwise provide access to The Stack dataset, you must include [these Terms of Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) and require users to agree to it.
By clicking on "Access repository" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well.
extra_gated_fields:
Email: text
I have read the License and agree with its terms: checkbox
---
# Dataset Card for The Stack
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Changelog](#changelog)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use it](#how-to-use-it)
- [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)
- [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)
- [Terms of Use for The Stack](#terms-of-use-for-the-stack)
## Dataset Description
- **Homepage:** https://www.bigcode-project.org/
- **Repository:** https://github.com/bigcode-project
- **Paper:** https://arxiv.org/abs/2211.15533
- **Leaderboard:** N/A
- **Point of Contact:** contact@bigcode-project.org
### Changelog
|Release|Description|
|-|-|
|v1.0| Initial release of the Stack. Included 30 programming languages and 18 permissive licenses. **Note:** Three included licenses (MPL/EPL/LGPL) are considered weak copyleft licenses. The resulting near-deduplicated dataset is 1.5TB in size. |
|v1.1| The three copyleft licenses ((MPL/EPL/LGPL) were excluded and the list of permissive licenses extended to 193 licenses in total. The list of programming languages was increased from 30 to 358 languages. Also opt-out request submitted by 15.11.2022 were excluded from this version of the dataset. The resulting near-deduplicated dataset is 3TB in size.|
|v1.2| Opt-out request submitted by 09.02.2022 were excluded from this version of the dataset. A stronger near-deduplication strategy was applied resulting leading to 2.7TB in size.|
### Dataset Summary
The Stack contains over 6TB of permissively-licensed source code files covering 358 programming languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets. **This is the near-deduplicated version with 3TB data.**
### Supported Tasks and Leaderboards
The Stack is a pre-training dataset for creating code LLMs. Code LLMs can be used for a wide variety of downstream tasks such as code completion from natural language descriptions ([HumanEval](https://huggingface.co/datasets/openai_humaneval), [MBPP](https://huggingface.co/datasets/mbpp)), documentation generation for individual functions ([CodeSearchNet](https://huggingface.co/datasets/code_search_net)), and auto-completion of code snippets ([HumanEval-Infilling](https://github.com/openai/human-eval-infilling)). However, these downstream evaluation benchmarks are outside the scope of The Stack.
### Languages
The following natural languages appear in the comments and docstrings from files in the dataset: EN, ZH, FR, PT, ES, RU, DE, KO, JA, UZ, IT, ID, RO, AR, FA, CA, HU, ML, NL, TR, TE, EL, EO, BN, LV, GL, PL, GU, CEB, IA, KN, SH, MK, UR, SV, LA, JKA, MY, SU, CS, MN. This kind of data is essential for applications such as documentation generation and natural-language-to-code translation.
The dataset contains **358 programming languages**. The full list can be found [here](https://huggingface.co/datasets/bigcode/the-stack-dedup/blob/main/programming-languages.json).
### How to use it
```python
from datasets import load_dataset
# full dataset (3TB of data)
ds = load_dataset("bigcode/the-stack-dedup", split="train")
# specific language (e.g. Dockerfiles)
ds = load_dataset("bigcode/the-stack-dedup", data_dir="data/dockerfile", split="train")
# dataset streaming (will only download the data as needed)
ds = load_dataset("bigcode/the-stack-dedup", streaming=True, split="train")
for sample in iter(ds): print(sample["content"])
```
## Dataset Structure
### Data Instances
Each data instance corresponds to one file. The content of the file is in the `content` feature, and other features (`repository_name`, `licenses`, etc.) provide some metadata. Note that a given file can appear in several different repositories that satisfy our safe-license criterion. If that is the case, only the first – in alphabetical order -- of these repositories is shown for simplicity.
### Data Fields
- `content` (string): the content of the file.
- `size` (integer): size of the uncompressed file.
- `lang` (string): the programming language.
- `ext` (string): file extension
- `avg_line_length` (float): the average line-length of the file.
- `max_line_length` (integer): the maximum line-length of the file.
- `alphanum_fraction` (float): the fraction of characters in the file that are alphabetical or numerical characters.
- `hexsha` (string): unique git hash of file
- `max_{stars|forks|issues}_repo_path` (string): path to file in repo containing this file with maximum number of `{stars|forks|issues}`
- `max_{stars|forks|issues}_repo_name` (string): name of repo containing this file with maximum number of `{stars|forks|issues}`
- `max_{stars|forks|issues}_repo_head_hexsha` (string): hexsha of repository head
- `max_{stars|forks|issues}_repo_licenses` (string): licenses in repository
- `max_{stars|forks|issues}_count` (integer): number of `{stars|forks|issues}` in repository
- `max_{stars|forks|issues}_repo_{stars|forks|issues}_min_datetime` (string): first timestamp of a `{stars|forks|issues}` event
- `max_{stars|forks|issues}_repo_{stars|forks|issues}_max_datetime` (string): last timestamp of a `{stars|forks|issues}` event
### Data Splits
The dataset has no splits and all data is loaded as train split by default. If you want to setup a custom train-test split beware that dataset contains a lot of near-duplicates which can cause leakage into the test split.
## Dataset Creation
### Curation Rationale
One of the challenges faced by researchers working on code LLMs is the lack of openness and transparency around the development of these systems. Most prior works described the high-level data collection process but did not release the training data. It is therefore difficult for other researchers to fully reproduce these models and understand what kind of pre-training data leads to high-performing code LLMs. By releasing an open large-scale code dataset we hope to make training of code LLMs more reproducible. **This is the near-deduplicated version with 3TB data.**
### Source Data
#### Initial Data Collection and Normalization
220.92M active GitHub repository names were collected from the event archives published between January 1st, 2015 and March 31st, 2022 on [GHArchive](https://gharchive.org/). Only 137.36M of these repositories were public and accessible on GitHub – others were not accessible as they had been deleted by their owners. 51.76B files were downloaded from the public repositories on GitHub between November 2021 and June 2022. 5.28B files were unique. The uncompressed size of all stored files is 92.36TB.
The list of programming language extensions is taken from this [list](https://gist.github.com/ppisarczyk/43962d06686722d26d176fad46879d41) (also provided in Appendix C of the paper).
Near-deduplication was implemented in the pre-processing pipeline on top of exact deduplication. To find near-duplicates, MinHash with 256 permutations of all documents was computed in linear time. Locality Sensitive Hashing was used to find the clusters of duplicates. Jaccard Similarities were computed inside these clusters to remove any false positives and with a similarity threshold of 0.85. Roughly 40% of permissively licensed files were (near-)duplicates. See section 3 of the paper for further details.
The following are not stored:
- Files that cannot contribute to training code: binary, empty, could not be decoded
- Files larger than 1MB
- The excluded file extensions are listed in Appendix B of the paper.
##### License detection
Permissive licenses have minimal restrictions on how the software can be copied, modified, and redistributed. The full list of licenses can be found [here](https://huggingface.co/datasets/bigcode/the-stack-dedup/blob/main/licenses.json)
GHArchive contained the license information for approximately 12% of the collected repositories. For the remaining repositories, [go-license-detector](https://github.com/src-d/go-license-detector) was run to detect the most likely SPDX license identifier. The detector did not detect a license for ~81% of the repositories, in which case the repository was excluded from the dataset.
A file was in included in the safe license dataset if at least one of the repositories containing the file had a permissive license.
#### Who are the source language producers?
The source (code) language producers are users of GitHub that created unique repository names between January 1st, 2015, and March 31st, 2022.
### Personal and Sensitive Information
The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. Deduplication has helped to reduce the amount of sensitive data that may exist. In the event that the dataset contains personal information, researchers should only use public, non-personal information in support of conducting and publishing their [open-access](https://en.wikipedia.org/wiki/Open_access) research. Personal information should not be used for spamming purposes, including sending unsolicited emails or selling of personal information. Complaints, removal requests, and "do not contact" requests can be sent to contact@bigcode-project.org.
The PII pipeline for this dataset is still a work in progress (see this [issue](https://github.com/bigcode-project/admin/issues/9) for updates). Researchers that wish to contribute to the anonymization pipeline of the project can apply to join [here](https://www.bigcode-project.org/docs/about/join/). Developers with source code in the dataset can request to have it removed [here](https://www.bigcode-project.org/docs/about/ip/) (proof of code contribution is required).
### Opting out of The Stack
We are giving developers the ability to have their code removed from the dataset upon request. The process for submitting and enacting removal requests will keep evolving throughout the project as we receive feedback and build up more data governance tools.
You can check if your code is in The Stack with the following ["Am I In The Stack?" Space](https://huggingface.co/spaces/bigcode/in-the-stack). If you'd like to have your data removed from the dataset follow the [instructions on GitHub](https://github.com/bigcode-project/opt-out-v2).
## Considerations for Using the Data
### Social Impact of Dataset
The Stack is an output of the BigCode Project. BigCode aims to be responsible by design and by default. The project is conducted in the spirit of Open Science, focused on the responsible development of LLMs for code.
With the release of The Stack, we aim to increase access, reproducibility, and transparency of code LLMs in the research community. Work to de-risk and improve on the implementation of ethical best practices of code LLMs is conducted in various BigCode working groups. The Legal, Ethics, and Governance working group has explored topics such as licensing (including copyleft and the intended use of permissively licensed code), attribution of generated code to original code, rights to restrict processing, the inclusion of Personally Identifiable Information (PII), and risks of malicious code, among other topics. This work is ongoing as of October 25th, 2022.
We expect code LLMs to enable people from diverse backgrounds to write higher quality code and develop low-code applications. Mission-critical software could become easier to maintain as professional developers are guided by code-generating systems on how to write more robust and efficient code. While the social impact is intended to be positive, the increased accessibility of code LLMs comes with certain risks such as over-reliance on the generated code and long-term effects on the software development job market.
A broader impact analysis relating to Code LLMs can be found in section 7 of this [paper](https://arxiv.org/abs/2107.03374). An in-depth risk assessments for Code LLMs can be found in section 4 of this [paper](https://arxiv.org/abs/2207.14157).
### Discussion of Biases
The code collected from GitHub does not contain demographic information or proxy information about the demographics. However, it is not without risks,
as the comments within the code may contain harmful or offensive language, which could be learned by the models.
Widely adopted programming languages like C and Javascript are overrepresented compared to niche programming languages like Julia and Scala. Some programming languages such as SQL, Batchfile, TypeScript are less likely to be permissively licensed (4% vs the average 10%). This may result in a biased representation of those languages. Permissively licensed files also tend to be longer.
Roughly 40 natural languages are present in docstrings and comments with English being the most prevalent. In python files, it makes up ~96% of the dataset.
For further information on data analysis of the Stack, see this [repo](https://github.com/bigcode-project/bigcode-analysis).
### Other Known Limitations
One of the current limitations of The Stack is that scraped HTML for websites may not be compliant with Web Content Accessibility Guidelines ([WCAG](https://www.w3.org/WAI/standards-guidelines/wcag/)). This could have an impact on HTML-generated code that may introduce web accessibility issues.
The training dataset could contain malicious code and/or the model could be used to generate malware or ransomware.
To the best of our knowledge, all files contained in the dataset are licensed with one of the permissive licenses (see list in [Licensing information](#licensing-information)). The accuracy of license attribution is limited by the accuracy of GHArchive and go-license-detector. Any mistakes should be reported to BigCode Project for review and follow-up as needed.
## Additional Information
### Dataset Curators
1. Harm de Vries, ServiceNow Research, harm.devries@servicenow.com
2. Leandro von Werra, Hugging Face, leandro@huggingface.co
### Licensing Information
The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.
The list of [SPDX license identifiers](https://spdx.org/licenses/) included in the dataset can be found [here](https://huggingface.co/datasets/bigcode/the-stack-dedup/blob/main/licenses.json).
### Citation Information
```
@article{Kocetkov2022TheStack,
title={The Stack: 3 TB of permissively licensed source code},
author={Kocetkov, Denis and Li, Raymond and Ben Allal, Loubna and Li, Jia and Mou,Chenghao and Muñoz Ferrandis, Carlos and Jernite, Yacine and Mitchell, Margaret and Hughes, Sean and Wolf, Thomas and Bahdanau, Dzmitry and von Werra, Leandro and de Vries, Harm},
journal={Preprint},
year={2022}
}
```
### Contributions
[More Information Needed]
## Terms of Use for The Stack
The Stack dataset is a collection of source code in over 300 programming languages. We ask that you read and acknowledge the following points before using the dataset:
1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.
2. The Stack is regularly updated to enact validated data removal requests. By clicking on "Access repository", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset’s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes.
3. To host, share, or otherwise provide access to The Stack dataset, you must include these Terms of Use and require users to agree to it.
|
e2e_nlg | 2022-11-18T19:59:40.000Z | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"meaning-representation-to-text",
"arxiv:1706.09254",
"arxiv:1901.11528",
"region:us"
] | null | The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
The E2E dataset poses new challenges:
(1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena;
(2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances.
E2E is released in the following paper where you can find more details and baseline results:
https://arxiv.org/abs/1706.09254 | @article{dusek.etal2020:csl,
title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}},
author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena},
year = {2020},
month = jan,
volume = {59},
pages = {123--156},
doi = {10.1016/j.csl.2019.06.009},
archivePrefix = {arXiv},
eprint = {1901.11528},
eprinttype = {arxiv},
journal = {Computer Speech & Language}
} | null | 7 | 7,392 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: e2e
pretty_name: End-to-End NLG Challenge
tags:
- meaning-representation-to-text
dataset_info:
features:
- name: meaning_representation
dtype: string
- name: human_reference
dtype: string
splits:
- name: train
num_bytes: 9435824
num_examples: 42061
- name: validation
num_bytes: 1171723
num_examples: 4672
- name: test
num_bytes: 1320205
num_examples: 4693
download_size: 11812316
dataset_size: 11927752
---
# Dataset Card for End-to-End NLG Challenge
## 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:** [homepage](http://www.macs.hw.ac.uk/InteractionLab/E2E/)
- **Repository:** [repository](https://github.com/tuetschek/e2e-dataset/)
- **Paper:** [paper](https://arxiv.org/abs/1706.09254)
- **Leaderboard:** [leaderboard](http://www.macs.hw.ac.uk/InteractionLab/E2E/)
### Dataset Summary
The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
The E2E dataset poses new challenges:
(1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena;
(2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances.
E2E is released in the following paper where you can find more details and baseline results:
https://arxiv.org/abs/1706.09254
### Supported Tasks and Leaderboards
- `text2text-generation-other-meaning-representation-to-text`: The dataset can be used to train a model to generate descriptions in the restaurant domain from meaning representations, which consists in taking as input some data about a restaurant and generate a sentence in natural language that presents the different aspects of the data about the restaurant.. Success on this task is typically measured by achieving a *high* [BLEU](https://huggingface.co/metrics/bleu), [NIST](https://huggingface.co/metrics/nist), [METEOR](https://huggingface.co/metrics/meteor), [Rouge-L](https://huggingface.co/metrics/rouge), [CIDEr](https://huggingface.co/metrics/cider). The TGen model (Dusek and Jurcıcek, 2016a) was used a baseline, had the following scores:
| | BLEU | NIST | METEOR | ROUGE_L | CIDEr |
| -------- | ------ | ------ | ------ | ------- | ------ |
| BASELINE | 0.6593 | 8.6094 | 0.4483 | 0.6850 | 2.2338 |
This task has an inactive leaderboard which can be found [here](http://www.macs.hw.ac.uk/InteractionLab/E2E/) and ranks models based on the metrics above.
### Languages
The dataset is in english (en).
## Dataset Structure
### Data Instances
Example of one instance:
```
{'human_reference': 'The Vaults pub near Café Adriatic has a 5 star rating. Prices start at £30.',
'meaning_representation': 'name[The Vaults], eatType[pub], priceRange[more than £30], customer rating[5 out of 5], near[Café Adriatic]'}
```
### Data Fields
- `human_reference`: string, the text is natural language that describes the different characteristics in the meaning representation
- `meaning_representation`: list of slots and values to generate a description from
Each MR consists of 3–8 attributes (slots), such as name, food or area, and their values.
### Data Splits
The dataset is split into training, validation and testing sets (in a 76.5-8.5-15 ratio), keeping a similar distribution of MR and reference text lengths and ensuring that MRs in different sets are distinct.
| | train | validation | test |
| ----- |-------:|------------:|------:|
| N. Instances | 42061 | 4672 | 4693 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
The data was collected using the CrowdFlower platform and quality-controlled following Novikova et al. (2016).
#### Who are the source language producers?
[More Information Needed]
### Annotations
Following Novikova et al. (2016), the E2E data was collected using pictures as stimuli, which was shown to elicit significantly more natural, more informative, and better phrased human references than textual MRs.
#### 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
```
@article{dusek.etal2020:csl,
title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}},
author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena},
year = {2020},
month = jan,
volume = {59},
pages = {123--156},
doi = {10.1016/j.csl.2019.06.009},
archivePrefix = {arXiv},
eprint = {1901.11528},
eprinttype = {arxiv},
journal = {Computer Speech \& Language}
```
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
codeparrot/github-code-clean | 2022-07-05T09:35:14.000Z | [
"license:apache-2.0",
"region:us"
] | codeparrot | The GitHub Code clean dataset in a more filtered version of codeparrot/github-code dataset, it consists of 115M code files from GitHub in 32 programming languages with 60 extensions totaling in almost 1TB of text data. | null | null | 55 | 7,339 | ---
license: apache-2.0
---
This is a cleaner version of [Github-code dataset](https://huggingface.co/datasets/codeparrot/github-code), we add the following filters:
* Average line length < 100
* Alpha numeric characters fraction > 0.25
* Remove auto-generated files (keyword search)
3.39M files are removed making up 2.94% of the dataset. |
financial_phrasebank | 2023-07-26T06:27:17.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-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-nc-sa-3.0",
"finance",
"arxiv:1307.5336",
"region:us"
] | null | The key arguments for the low utilization of statistical techniques in
financial sentiment analysis have been the difficulty of implementation for
practical applications and the lack of high quality training data for building
such models. Especially in the case of finance and economic texts, annotated
collections are a scarce resource and many are reserved for proprietary use
only. To resolve the missing training data problem, we present a collection of
∼ 5000 sentences to establish human-annotated standards for benchmarking
alternative modeling techniques.
The objective of the phrase level annotation task was to classify each example
sentence into a positive, negative or neutral category by considering only the
information explicitly available in the given sentence. Since the study is
focused only on financial and economic domains, the annotators were asked to
consider the sentences from the view point of an investor only; i.e. whether
the news may have positive, negative or neutral influence on the stock price.
As a result, sentences which have a sentiment that is not relevant from an
economic or financial perspective are considered neutral.
This release of the financial phrase bank covers a collection of 4840
sentences. The selected collection of phrases was annotated by 16 people with
adequate background knowledge on financial markets. Three of the annotators
were researchers and the remaining 13 annotators were master’s students at
Aalto University School of Business with majors primarily in finance,
accounting, and economics.
Given the large number of overlapping annotations (5 to 8 annotations per
sentence), there are several ways to define a majority vote based gold
standard. To provide an objective comparison, we have formed 4 alternative
reference datasets based on the strength of majority agreement: all annotators
agree, >=75% of annotators agree, >=66% of annotators agree and >=50% of
annotators agree. | @article{Malo2014GoodDO,
title={Good debt or bad debt: Detecting semantic orientations in economic texts},
author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},
journal={Journal of the Association for Information Science and Technology},
year={2014},
volume={65}
} | null | 104 | 7,304 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- sentiment-classification
pretty_name: FinancialPhrasebank
dataset_info:
- config_name: sentences_allagree
features:
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dtype: string
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dtype:
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'0': negative
'1': neutral
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num_bytes: 679240
num_examples: 4846
download_size: 681890
dataset_size: 679240
tags:
- finance
---
# Dataset Card for financial_phrasebank
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Kaggle](https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news) [ResearchGate](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10)
- **Repository:**
- **Paper:** [Arxiv](https://arxiv.org/abs/1307.5336)
- **Leaderboard:** [Kaggle](https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news/code) [PapersWithCode](https://paperswithcode.com/sota/sentiment-analysis-on-financial-phrasebank) =
- **Point of Contact:** [Pekka Malo](mailto:pekka.malo@aalto.fi) [Ankur Sinha](mailto:ankur.sinha@aalto.fi)
### Dataset Summary
Polar sentiment dataset of sentences from financial news. The dataset consists of 4840 sentences from English language financial news categorised by sentiment. The dataset is divided by agreement rate of 5-8 annotators.
### Supported Tasks and Leaderboards
Sentiment Classification
### Languages
English
## Dataset Structure
### Data Instances
```
{ "sentence": "Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings that were hit by larger expenditures on R&D and marketing .",
"label": "negative"
}
```
### Data Fields
- sentence: a tokenized line from the dataset
- label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'
### Data Splits
There's no train/validation/test split.
However the dataset is available in four possible configurations depending on the percentage of agreement of annotators:
`sentences_50agree`; Number of instances with >=50% annotator agreement: 4846
`sentences_66agree`: Number of instances with >=66% annotator agreement: 4217
`sentences_75agree`: Number of instances with >=75% annotator agreement: 3453
`sentences_allagree`: Number of instances with 100% annotator agreement: 2264
## Dataset Creation
### Curation Rationale
The key arguments for the low utilization of statistical techniques in
financial sentiment analysis have been the difficulty of implementation for
practical applications and the lack of high quality training data for building
such models. Especially in the case of finance and economic texts, annotated
collections are a scarce resource and many are reserved for proprietary use
only. To resolve the missing training data problem, we present a collection of
∼ 5000 sentences to establish human-annotated standards for benchmarking
alternative modeling techniques.
The objective of the phrase level annotation task was to classify each example
sentence into a positive, negative or neutral category by considering only the
information explicitly available in the given sentence. Since the study is
focused only on financial and economic domains, the annotators were asked to
consider the sentences from the view point of an investor only; i.e. whether
the news may have positive, negative or neutral influence on the stock price.
As a result, sentences which have a sentiment that is not relevant from an
economic or financial perspective are considered neutral.
### Source Data
#### Initial Data Collection and Normalization
The corpus used in this paper is made out of English news on all listed
companies in OMX Helsinki. The news has been downloaded from the LexisNexis
database using an automated web scraper. Out of this news database, a random
subset of 10,000 articles was selected to obtain good coverage across small and
large companies, companies in different industries, as well as different news
sources. Following the approach taken by Maks and Vossen (2010), we excluded
all sentences which did not contain any of the lexicon entities. This reduced
the overall sample to 53,400 sentences, where each has at least one or more
recognized lexicon entity. The sentences were then classified according to the
types of entity sequences detected. Finally, a random sample of ∼5000 sentences
was chosen to represent the overall news database.
#### Who are the source language producers?
The source data was written by various financial journalists.
### Annotations
#### Annotation process
This release of the financial phrase bank covers a collection of 4840
sentences. The selected collection of phrases was annotated by 16 people with
adequate background knowledge on financial markets.
Given the large number of overlapping annotations (5 to 8 annotations per
sentence), there are several ways to define a majority vote based gold
standard. To provide an objective comparison, we have formed 4 alternative
reference datasets based on the strength of majority agreement:
#### Who are the annotators?
Three of the annotators were researchers and the remaining 13 annotators were
master's students at Aalto University School of Business with majors primarily
in finance, accounting, and economics.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
All annotators were from the same institution and so interannotator agreement
should be understood with this taken into account.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/.
If you are interested in commercial use of the data, please contact the following authors for an appropriate license:
- [Pekka Malo](mailto:pekka.malo@aalto.fi)
- [Ankur Sinha](mailto:ankur.sinha@aalto.fi)
### Citation Information
```
@article{Malo2014GoodDO,
title={Good debt or bad debt: Detecting semantic orientations in economic texts},
author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},
journal={Journal of the Association for Information Science and Technology},
year={2014},
volume={65}
}
```
### Contributions
Thanks to [@frankier](https://github.com/frankier) for adding this dataset. |
agemagician/uniref50 | 2023-10-07T23:04:56.000Z | [
"region:us"
] | agemagician | null | null | null | 2 | 7,299 | Entry not found |
opus100 | 2023-06-01T14:59:58.000Z | [
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"language:xh",
"language:yi",
"language:yo",
"language:zh",
"language:zu",
"license:unknown",
"arxiv:2004.11867",
"region:us"
] | null | OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side.
The corpus covers 100 languages (including English).OPUS-100 contains approximately 55M sentence pairs.
Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 have at least 10k. | @misc{zhang2020improving,
title={Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation},
author={Biao Zhang and Philip Williams and Ivan Titov and Rico Sennrich},
year={2020},
eprint={2004.11867},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 56 | 7,186 | ---
pretty_name: Opus100
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---
# Dataset Card for Opus100
## 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:** [Link](http://opus.nlpl.eu/opus-100.php)
- **Repository:** [GitHub](https://github.com/EdinburghNLP/opus-100-corpus)
- **Paper:** [ARXIV](https://arxiv.org/abs/2004.11867)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side. The corpus covers 100 languages (including English). Selected the languages based on the volume of parallel data available in OPUS.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
OPUS-100 contains approximately 55M sentence pairs. Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 have at least 10k.
## Dataset Structure
### Data Instances
```
{
"ca": "El departament de bombers té el seu propi equip d'investigació.",
"en": "Well, the fire department has its own investigative unit."
}
```
### Data Fields
- `src_tag`: `string` text in source language
- `tgt_tag`: `string` translation of source language in target language
### Data Splits
The dataset is split into training, development, and test portions. Data was prepared by randomly sampled up to 1M sentence pairs per language pair for training and up to 2000 each for development and test. To ensure that there was no overlap (at the monolingual sentence level) between the training and development/test data, they applied a filter during sampling to exclude sentences that had already been sampled. Note that this was done cross-lingually so that, for instance, an English sentence in the Portuguese-English portion of the training data could not occur in the Hindi-English test set.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{zhang2020improving,
title={Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation},
author={Biao Zhang and Philip Williams and Ivan Titov and Rico Sennrich},
year={2020},
eprint={2004.11867},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset. |
wics/strategy-qa | 2023-05-10T06:12:13.000Z | [
"license:other",
"region:us"
] | wics | null | 3 | 7,054 | ---
license: other
---
| ||
xquad | 2023-04-05T13:45:22.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|squad",
"language:ar",
"language:de",
"language:el",
"language:en",
"language:es",
"language:hi",
"language:ro",
"language:ru",
"language:th",
"language:tr",
"language:vi",
"language:zh",
"license:cc-by-sa-4.0",
"arxiv:1910.11856",
"region:us"
] | null | XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German,
Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel
across 12 languages. | @article{Artetxe:etal:2019,
author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
title = {On the cross-lingual transferability of monolingual representations},
journal = {CoRR},
volume = {abs/1910.11856},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.11856}
} | null | 11 | 7,024 | ---
pretty_name: XQuAD
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids:
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paperswithcode_id: xquad
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num_bytes: 2206690
num_examples: 1190
download_size: 13962158
dataset_size: 2206690
- config_name: xquad.th
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: validation
num_bytes: 2854959
num_examples: 1190
download_size: 13962158
dataset_size: 2854959
- config_name: xquad.tr
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: validation
num_bytes: 1210763
num_examples: 1190
download_size: 13962158
dataset_size: 1210763
- config_name: xquad.ru
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: validation
num_bytes: 2136990
num_examples: 1190
download_size: 13962158
dataset_size: 2136990
- config_name: xquad.ro
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: validation
num_bytes: 1299450
num_examples: 1190
download_size: 13962158
dataset_size: 1299450
---
# Dataset Card for "xquad"
## 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/deepmind/xquad](https://github.com/deepmind/xquad)
- **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:** 146.31 MB
- **Size of the generated dataset:** 18.97 MB
- **Total amount of disk used:** 165.28 MB
### Dataset Summary
XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German,
Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Consequently, the dataset is entirely parallel
across 11 languages.
### 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
#### xquad.ar
- **Size of downloaded dataset files:** 13.30 MB
- **Size of the generated dataset:** 1.72 MB
- **Total amount of disk used:** 15.03 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [527],
"text": ["136"]
},
"context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
"id": "56beb4343aeaaa14008c925c",
"question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```
#### xquad.de
- **Size of downloaded dataset files:** 13.30 MB
- **Size of the generated dataset:** 1.29 MB
- **Total amount of disk used:** 14.59 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [527],
"text": ["136"]
},
"context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
"id": "56beb4343aeaaa14008c925c",
"question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```
#### xquad.el
- **Size of downloaded dataset files:** 13.30 MB
- **Size of the generated dataset:** 2.21 MB
- **Total amount of disk used:** 15.51 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [527],
"text": ["136"]
},
"context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
"id": "56beb4343aeaaa14008c925c",
"question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```
#### xquad.en
- **Size of downloaded dataset files:** 13.30 MB
- **Size of the generated dataset:** 1.12 MB
- **Total amount of disk used:** 14.42 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [527],
"text": ["136"]
},
"context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
"id": "56beb4343aeaaa14008c925c",
"question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```
#### xquad.es
- **Size of downloaded dataset files:** 13.30 MB
- **Size of the generated dataset:** 1.28 MB
- **Total amount of disk used:** 14.58 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [527],
"text": ["136"]
},
"context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
"id": "56beb4343aeaaa14008c925c",
"question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```
### Data Fields
The data fields are the same among all splits.
#### xquad.ar
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### xquad.de
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### xquad.el
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### xquad.en
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### xquad.es
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | validation |
| -------- | ---------: |
| xquad.ar | 1190 |
| xquad.de | 1190 |
| xquad.el | 1190 |
| xquad.en | 1190 |
| xquad.es | 1190 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{Artetxe:etal:2019,
author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
title = {On the cross-lingual transferability of monolingual representations},
journal = {CoRR},
volume = {abs/1910.11856},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.11856}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
paws-x | 2023-01-25T14:42:16.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:multi-input-text-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-paws",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:ja",
"language:ko",
"language:zh",
"license:other",
"paraphrase-identification",
"arxiv:1908.11828",
"region:us"
] | null | PAWS-X, a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages.
This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine
translated training pairs in six typologically distinct languages: French, Spanish, German,
Chinese, Japanese, and Korean. English language is available by default. All translated
pairs are sourced from examples in PAWS-Wiki.
For further details, see the accompanying paper: PAWS-X: A Cross-lingual Adversarial Dataset
for Paraphrase Identification (https://arxiv.org/abs/1908.11828)
NOTE: There might be some missing or wrong labels in the dataset and we have replaced them with -1. | @InProceedings{pawsx2019emnlp,
title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
booktitle = {Proc. of EMNLP},
year = {2019}
} | null | 17 | 7,002 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- expert-generated
- machine-generated
language:
- de
- en
- es
- fr
- ja
- ko
- zh
license:
- other
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-paws
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
- semantic-similarity-scoring
- text-scoring
- multi-input-text-classification
paperswithcode_id: paws-x
pretty_name: 'PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification'
tags:
- paraphrase-identification
dataset_info:
- config_name: en
features:
- name: id
dtype: int32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
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'1': '1'
splits:
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num_bytes: 12215953
num_examples: 49401
- name: test
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num_examples: 2000
- name: validation
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num_examples: 2000
download_size: 30282057
dataset_size: 13202974
- config_name: de
features:
- name: id
dtype: int32
- name: sentence1
dtype: string
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dtype: string
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dtype:
class_label:
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download_size: 30282057
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- config_name: es
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dtype: string
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- config_name: fr
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dtype: int32
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dtype: string
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download_size: 30282057
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- config_name: ja
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download_size: 30282057
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- config_name: ko
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dtype: int32
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dtype: string
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download_size: 30282057
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- config_name: zh
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dtype: int32
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dtype: string
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dtype: string
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dtype:
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num_examples: 2000
download_size: 30282057
dataset_size: 11763261
---
# Dataset Card for PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification
## 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:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx)
- **Repository:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx)
- **Paper:** [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://arxiv.org/abs/1908.11828)
- **Point of Contact:** [Yinfei Yang](yinfeiy@google.com)
### Dataset Summary
This dataset contains 23,659 **human** translated PAWS evaluation pairs and
296,406 **machine** translated training pairs in six typologically distinct
languages: French, Spanish, German, Chinese, Japanese, and Korean. All
translated pairs are sourced from examples in
[PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki).
For further details, see the accompanying paper:
[PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase
Identification](https://arxiv.org/abs/1908.11828)
### Supported Tasks and Leaderboards
It has been majorly used for paraphrase identification for English and other 6 languages namely French, Spanish, German, Chinese, Japanese, and Korean
### Languages
The dataset is in English, French, Spanish, German, Chinese, Japanese, and Korean
## Dataset Structure
### Data Instances
For en:
```
id : 1
sentence1 : In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland .
sentence2 : In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England .
label : 0
```
For fr:
```
id : 1
sentence1 : À Paris, en octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, lui demandant un passeport pour retourner en Angleterre en passant par l'Écosse.
sentence2 : En octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, à Paris, et lui demanda un passeport pour retourner en Écosse par l'Angleterre.
label : 0
```
### Data Fields
All files are in tsv format with four columns:
Column Name | Data
:---------- | :--------------------------------------------------------
id | An ID that matches the ID of the source pair in PAWS-Wiki
sentence1 | The first sentence
sentence2 | The second sentence
label | Label for each pair
The source text of each translation can be retrieved by looking up the ID in the
corresponding file in PAWS-Wiki.
### Data Splits
The numbers of examples for each of the seven languages are shown below:
Language | Train | Dev | Test
:------- | ------: | -----: | -----:
en | 49,401 | 2,000 | 2,000
fr | 49,401 | 2,000 | 2,000
es | 49,401 | 2,000 | 2,000
de | 49,401 | 2,000 | 2,000
zh | 49,401 | 2,000 | 2,000
ja | 49,401 | 2,000 | 2,000
ko | 49,401 | 2,000 | 2,000
> **Caveat**: please note that the dev and test sets of PAWS-X are both sourced
> from the dev set of PAWS-Wiki. As a consequence, the same `sentence 1` may
> appear in both the dev and test sets. Nevertheless our data split guarantees
> that there is no overlap on sentence pairs (`sentence 1` + `sentence 2`)
> between dev and test.
## Dataset Creation
### Curation Rationale
Most existing work on adversarial data generation focuses on English. For example, PAWS (Paraphrase Adversaries from Word Scrambling) (Zhang et al., 2019) consists of challenging English paraphrase identification pairs from Wikipedia and Quora. They remedy this gap with PAWS-X, a new dataset of 23,659 human translated PAWS evaluation pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. They provide baseline numbers for three models with different capacity to capture non-local context and sentence structure, and using different multilingual training and evaluation regimes. Multilingual BERT (Devlin et al., 2019) fine-tuned on PAWS English plus machine-translated data performs the best, with a range of 83.1-90.8 accuracy across the non-English languages and an average accuracy gain of 23% over the next best model. PAWS-X shows the effectiveness of deep, multilingual pre-training while also leaving considerable headroom as a new challenge to drive multilingual research that better captures structure and contextual information.
### Source Data
PAWS (Paraphrase Adversaries from Word Scrambling)
#### Initial Data Collection and Normalization
All translated pairs are sourced from examples in [PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki)
#### Who are the source language producers?
This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean.
### Annotations
#### Annotation process
If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.
#### Who are the annotators?
The paper mentions the translate team, especially Mengmeng Niu, for the help with the annotations.
### 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
List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
### Licensing Information
The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
### Citation Information
```
@InProceedings{pawsx2019emnlp,
title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
booktitle = {Proc. of EMNLP},
year = {2019}
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@gowtham1997](https://github.com/gowtham1997) for adding this dataset. |
multi_woz_v22 | 2023-01-25T14:41:08.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:dialogue-modeling",
"task_ids:multi-class-classification",
"task_ids:parsing",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1810.00278",
"region:us"
] | null | Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.
MultiWOZ 2.1 (Eric et al., 2019) identified and fixed many erroneous annotations and user utterances in the original version, resulting in an
improved version of the dataset. MultiWOZ 2.2 is a yet another improved version of this dataset, which identifies and fizes dialogue state annotation errors
across 17.3% of the utterances on top of MultiWOZ 2.1 and redefines the ontology by disallowing vocabularies of slots with a large number of possible values
(e.g., restaurant name, time of booking) and introducing standardized slot span annotations for these slots. | @article{corr/abs-2007-12720,
author = {Xiaoxue Zang and
Abhinav Rastogi and
Srinivas Sunkara and
Raghav Gupta and
Jianguo Zhang and
Jindong Chen},
title = {MultiWOZ 2.2 : {A} Dialogue Dataset with Additional Annotation Corrections
and State Tracking Baselines},
journal = {CoRR},
volume = {abs/2007.12720},
year = {2020},
url = {https://arxiv.org/abs/2007.12720},
archivePrefix = {arXiv},
eprint = {2007.12720}
} | null | 14 | 6,967 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- token-classification
- text-classification
task_ids:
- dialogue-modeling
- multi-class-classification
- parsing
paperswithcode_id: multiwoz
pretty_name: Multi-domain Wizard-of-Oz
dataset_info:
- config_name: v2.2
features:
- name: dialogue_id
dtype: string
- name: services
sequence: string
- name: turns
sequence:
- name: turn_id
dtype: string
- name: speaker
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struct:
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- name: copy_from
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- name: dialogue_acts
struct:
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num_bytes: 68222649
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download_size: 276592909
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num_bytes: 5377939
num_examples: 1000
- name: test
num_bytes: 5410819
num_examples: 1000
download_size: 276592909
dataset_size: 51726335
---
# Dataset Card for MultiWOZ
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [MultiWOZ 2.2 github repository](https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2)
- **Paper:** [MultiWOZ v2](https://arxiv.org/abs/1810.00278), and [MultiWOZ v2.2](https://www.aclweb.org/anthology/2020.nlp4convai-1.13.pdf)
- **Point of Contact:** [Paweł Budzianowski](pfb30@cam.ac.uk)
### Dataset Summary
Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.
MultiWOZ 2.1 (Eric et al., 2019) identified and fixed many erroneous annotations and user utterances in the original version, resulting in an
improved version of the dataset. MultiWOZ 2.2 is a yet another improved version of this dataset, which identifies and fixes dialogue state annotation errors
across 17.3% of the utterances on top of MultiWOZ 2.1 and redefines the ontology by disallowing vocabularies of slots with a large number of possible values
(e.g., restaurant name, time of booking) and introducing standardized slot span annotations for these slots.
### Supported Tasks and Leaderboards
This dataset supports a range of task.
- **Generative dialogue modeling** or `dialogue-modeling`: the text of the dialogues can be used to train a sequence model on the utterances. Performance on this task is typically evaluated with delexicalized-[BLEU](https://huggingface.co/metrics/bleu), inform rate and request success.
- **Intent state tracking**, a `multi-class-classification` task: predict the belief state of the user side of the conversation, performance is measured by [F1](https://huggingface.co/metrics/f1).
- **Dialog act prediction**, a `parsing` task: parse an utterance into the corresponding dialog acts for the system to use. [F1](https://huggingface.co/metrics/f1) is typically reported.
### Languages
The text in the dataset is in English (`en`).
## Dataset Structure
### Data Instances
A data instance is a full multi-turn dialogue between a `USER` and a `SYSTEM`. Each turn has a single utterance, e.g.:
```
['What fun places can I visit in the East?',
'We have five spots which include boating, museums and entertainment. Any preferences that you have?']
```
The utterances of the `USER` are also annotated with frames denoting their intent and believe state:
```
[{'service': ['attraction'],
'slots': [{'copy_from': [],
'copy_from_value': [],
'exclusive_end': [],
'slot': [],
'start': [],
'value': []}],
'state': [{'active_intent': 'find_attraction',
'requested_slots': [],
'slots_values': {'slots_values_list': [['east']],
'slots_values_name': ['attraction-area']}}]},
{'service': [], 'slots': [], 'state': []}]
```
Finally, each of the utterances is annotated with dialog acts which provide a structured representation of what the `USER` or `SYSTEM` is inquiring or giving information about.
```
[{'dialog_act': {'act_slots': [{'slot_name': ['east'],
'slot_value': ['area']}],
'act_type': ['Attraction-Inform']},
'span_info': {'act_slot_name': ['area'],
'act_slot_value': ['east'],
'act_type': ['Attraction-Inform'],
'span_end': [39],
'span_start': [35]}},
{'dialog_act': {'act_slots': [{'slot_name': ['none'], 'slot_value': ['none']},
{'slot_name': ['boating', 'museums', 'entertainment', 'five'],
'slot_value': ['type', 'type', 'type', 'choice']}],
'act_type': ['Attraction-Select', 'Attraction-Inform']},
'span_info': {'act_slot_name': ['type', 'type', 'type', 'choice'],
'act_slot_value': ['boating', 'museums', 'entertainment', 'five'],
'act_type': ['Attraction-Inform',
'Attraction-Inform',
'Attraction-Inform',
'Attraction-Inform'],
'span_end': [40, 49, 67, 12],
'span_start': [33, 42, 54, 8]}}]
```
### Data Fields
Each dialogue instance has the following fields:
- `dialogue_id`: a unique ID identifying the dialog. The MUL and PMUL names refer to strictly multi domain dialogues (at least 2 main domains are involved) while the SNG, SSNG and WOZ names refer to single domain dialogues with potentially sub-domains like booking.
- `services`: a list of services mentioned in the dialog, such as `train` or `hospitals`.
- `turns`: the sequence of utterances with their annotations, including:
- `turn_id`: a turn identifier, unique per dialog.
- `speaker`: either the `USER` or `SYSTEM`.
- `utterance`: the text of the utterance.
- `dialogue_acts`: The structured parse of the utterance into dialog acts in the system's grammar
- `act_type`: Such as e.g. `Attraction-Inform` to seek or provide information about an `attraction`
- `act_slots`: provide more details about the action
- `span_info`: maps these `act_slots` to the `utterance` text.
- `frames`: only for `USER` utterances, track the user's belief state, i.e. a structured representation of what they are trying to achieve in the fialog. This decomposes into:
- `service`: the service they are interested in
- `state`: their belief state including their `active_intent` and further information expressed in `requested_slots`
- `slots`: a mapping of the `requested_slots` to where they are mentioned in the text. It takes one of two forms, detailed next:
The first type are span annotations that identify the location where slot values have been mentioned in the utterances for non-categorical slots. These span annotations are represented as follows:
```
{
"slots": [
{
"slot": String of slot name.
"start": Int denoting the index of the starting character in the utterance corresponding to the slot value.
"exclusive_end": Int denoting the index of the character just after the last character corresponding to the slot value in the utterance. In python, utterance[start:exclusive_end] gives the slot value.
"value": String of value. It equals to utterance[start:exclusive_end], where utterance is the current utterance in string.
}
]
}
```
There are also some non-categorical slots whose values are carried over from another slot in the dialogue state. Their values don"t explicitly appear in the utterances. For example, a user utterance can be "I also need a taxi from the restaurant to the hotel.", in which the state values of "taxi-departure" and "taxi-destination" are respectively carried over from that of "restaurant-name" and "hotel-name". For these slots, instead of annotating them as spans, a "copy from" annotation identifies the slot it copies the value from. This annotation is formatted as follows,
```
{
"slots": [
{
"slot": Slot name string.
"copy_from": The slot to copy from.
"value": A list of slot values being . It corresponds to the state values of the "copy_from" slot.
}
]
}
```
### Data Splits
The dataset is split into a `train`, `validation`, and `test` split with the following sizes:
| | train | validation | test |
|---------------------|------:|-----------:|-----:|
| Number of dialogues | 8438 | 1000 | 1000 |
| Number of turns | 42190 | 5000 | 5000 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The initial dataset (Versions 1.0 and 2.0) was created by a team of researchers from the [Cambridge Dialogue Systems Group](https://mi.eng.cam.ac.uk/research/dialogue/corpora/). Version 2.1 was developed on top of v2.0 by a team from Amazon, and v2.2 was developed by a team of Google researchers.
### Licensing Information
The dataset is released under the Apache License 2.0.
### Citation Information
You can cite the following for the various versions of MultiWOZ:
Version 1.0
```
@inproceedings{ramadan2018large,
title={Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing},
author={Ramadan, Osman and Budzianowski, Pawe{\l} and Gasic, Milica},
booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics},
volume={2},
pages={432--437},
year={2018}
}
```
Version 2.0
```
@inproceedings{budzianowski2018large,
Author = {Budzianowski, Pawe{\l} and Wen, Tsung-Hsien and Tseng, Bo-Hsiang and Casanueva, I{\~n}igo and Ultes Stefan and Ramadan Osman and Ga{\v{s}}i\'c, Milica},
title={MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling},
booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
year={2018}
}
```
Version 2.1
```
@article{eric2019multiwoz,
title={MultiWOZ 2.1: Multi-Domain Dialogue State Corrections and State Tracking Baselines},
author={Eric, Mihail and Goel, Rahul and Paul, Shachi and Sethi, Abhishek and Agarwal, Sanchit and Gao, Shuyag and Hakkani-Tur, Dilek},
journal={arXiv preprint arXiv:1907.01669},
year={2019}
}
```
Version 2.2
```
@inproceedings{zang2020multiwoz,
title={MultiWOZ 2.2: A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines},
author={Zang, Xiaoxue and Rastogi, Abhinav and Sunkara, Srinivas and Gupta, Raghav and Zhang, Jianguo and Chen, Jindong},
booktitle={Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI, ACL 2020},
pages={109--117},
year={2020}
}
```
### Contributions
Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset. |
angelolab/ark_example | 2023-08-31T19:36:09.000Z | [
"task_categories:image-segmentation",
"task_ids:instance-segmentation",
"annotations_creators:no-annotation",
"size_categories:n<1K",
"source_datasets:original",
"license:apache-2.0",
"MIBI",
"Multiplexed-Imaging",
"region:us"
] | angelolab | This dataset contains 11 Field of Views (FOVs), each with 22 channels. | @InProceedings{huggingface:dataset,
title = {Ark Analysis Example Dataset},
author={Angelo Lab},
year={2022}
} | null | 0 | 6,953 | ---
annotations_creators:
- no-annotation
language: []
language_creators: []
license:
- apache-2.0
multilinguality: []
pretty_name: An example dataset for analyzing multiplexed imaging data.
size_categories:
- n<1K
source_datasets:
- original
tags:
- MIBI
- Multiplexed-Imaging
task_categories:
- image-segmentation
task_ids:
- instance-segmentation
---
# Dataset Card for [Dataset Name]
## 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
[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
#### 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 [@angelolab](https://github.com/angelolab) for adding this dataset. |
universal_dependencies | 2023-06-01T14:59:56.000Z | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
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"language:ckt",
"language:cop",
"language:cs",
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"language:de",
"language:el",
"language:en",
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"language:yo",
"language:yue",
"language:zh",
"license:unknown",
"constituency-parsing",
"dependency-parsing",
"region:us"
] | null | Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008). | null | null | 13 | 6,927 | ---
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids:
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paperswithcode_id: universal-dependencies
pretty_name: Universal Dependencies Treebank
tags:
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---
# Dataset Card for Universal Dependencies Treebank
## 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:** [Universal Dependencies](https://universaldependencies.org/)
- **Repository:**
- **Paper:**
- **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
[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 [@patrickvonplaten](https://github.com/patrickvonplaten), [@jplu](https://github.com/jplu) for adding this dataset. |
common_voice | 2023-06-27T07:46:51.000Z | [
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"license:cc0-1.0",
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] | null | Common Voice is Mozilla's initiative to help teach machines how real people speak.
The dataset currently consists of 7,335 validated hours of speech in 60 languages, but we’re always adding more voices and languages. | @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
} | null | 99 | 6,810 | ---
pretty_name: Common Voice
annotations_creators:
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task_categories:
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task_ids: []
paperswithcode_id: common-voice
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---
# Dataset Card for common_voice
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Deprecated:</b> Dataset "common_voice" is deprecated and will soon be deleted. Use datasets under <a href="https://huggingface.co/mozilla-foundation">mozilla-foundation</a> organisation instead. For example, you can load <a href="https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0">Common Voice 13</a> dataset via <code>load_dataset("mozilla-foundation/common_voice_13_0", "en")</code></p>
</div>
## 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://commonvoice.mozilla.org/en/datasets
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 9,283 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help train the accuracy of speech recognition engines.
The dataset currently consists of 7,335 validated hours in 60 languages, but were always adding more voices and languages. Take a look at our Languages page to request a language or start contributing.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called path and its sentence. Additional fields include accent, age, client_id, up_votes down_votes, gender, locale and segment.
`
{'accent': 'netherlands', 'age': 'fourties', 'client_id': 'bbbcb732e0f422150c30ff3654bbab572e2a617da107bca22ff8b89ab2e4f124d03b6a92c48322862f60bd0179ae07baf0f9b4f9c4e11d581e0cec70f703ba54', 'down_votes': 0, 'gender': 'male', 'locale': 'nl', 'path': 'nl/clips/common_voice_nl_23522441.mp3', 'segment': "''", 'sentence': 'Ik vind dat een dubieuze procedure.', 'up_votes': 2, 'audio': {'path': `nl/clips/common_voice_nl_23522441.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000}
`
### Data Fields
client_id: An id for which client (voice) made the recording
path: The path to the audio file
audio: 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: The sentence the user was prompted to speak
up_votes: How many upvotes the audio file has received from reviewers
down_votes: How many downvotes the audio file has received from reviewers
age: The age of the speaker.
gender: The gender of the speaker
accent: Accent of the speaker
locale: The locale of the speaker
segment: Usually 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 recieved upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and recieved downvotes 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.
## 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
}
```
### Contributions
Thanks to [@BirgerMoell](https://github.com/BirgerMoell) for adding this dataset. |
mteb/mtop_domain | 2022-11-21T19:59:05.000Z | [
"task_categories:text-classification",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:th",
"region:us"
] | mteb | null | null | null | 2 | 6,786 | ---
task_categories:
- text-classification
language:
- de
- en
- es
- fr
- hi
- th
--- |
sick | 2023-01-25T14:44:16.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|image-flickr-8k",
"source_datasets:extended|semeval2012-sts-msr-video",
"language:en",
"license:cc-by-nc-sa-3.0",
"region:us"
] | null | Shared and internationally recognized benchmarks are fundamental for the development of any computational system.
We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them.
SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs.
By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral).
The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes. | @inproceedings{marelli-etal-2014-sick,
title = "A {SICK} cure for the evaluation of compositional distributional semantic models",
author = "Marelli, Marco and
Menini, Stefano and
Baroni, Marco and
Bentivogli, Luisa and
Bernardi, Raffaella and
Zamparelli, Roberto",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf",
pages = "216--223",
} | null | 5 | 6,754 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|image-flickr-8k
- extended|semeval2012-sts-msr-video
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: sick
pretty_name: Sentences Involving Compositional Knowledge
dataset_info:
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'1': neutral
'2': contradiction
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- name: train
num_bytes: 1180530
num_examples: 4439
- name: validation
num_bytes: 132913
num_examples: 495
- name: test
num_bytes: 1305846
num_examples: 4906
download_size: 217584
dataset_size: 2619289
---
# Dataset Card for sick
## 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://marcobaroni.org/composes/sick.html
- **Repository:** [Needs More Information]
- **Paper:** https://www.aclweb.org/anthology/L14-1314/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
Example instance:
```
{
"entailment_AB": "A_neutral_B",
"entailment_BA": "B_neutral_A",
"label": 1,
"id": "1",
"relatedness_score": 4.5,
"sentence_A": "A group of kids is playing in a yard and an old man is standing in the background",
"sentence_A_dataset": "FLICKR",
"sentence_A_original": "A group of children playing in a yard, a man in the background.",
"sentence_B": "A group of boys in a yard is playing and a man is standing in the background",
"sentence_B_dataset": "FLICKR",
"sentence_B_original": "A group of children playing in a yard, a man in the background."
}
```
### Data Fields
- pair_ID: sentence pair ID
- sentence_A: sentence A
- sentence_B: sentence B
- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)
- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)
- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)
- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)
- sentence_A_original: original sentence from which sentence A is derived
- sentence_B_original: original sentence from which sentence B is derived
- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)
- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)
### Data Splits
Train Trial Test
4439 495 4906
## 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
```
@inproceedings{marelli-etal-2014-sick,
title = "A {SICK} cure for the evaluation of compositional distributional semantic models",
author = "Marelli, Marco and
Menini, Stefano and
Baroni, Marco and
Bentivogli, Luisa and
Bernardi, Raffaella and
Zamparelli, Roberto",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf",
pages = "216--223",
}
```
### Contributions
Thanks to [@calpt](https://github.com/calpt) for adding this dataset. |
llm-lens/vocab_tags | 2023-06-29T02:50:09.000Z | [
"region:us"
] | llm-lens | null | null | null | 1 | 6,697 | ---
dataset_info:
features:
- name: prompt_descriptions
dtype: string
splits:
- name: train
num_bytes: 346971
num_examples: 22131
download_size: 298971
dataset_size: 346971
---
# Dataset Card for "vocab_tags"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dair-ai/emotion | 2023-04-20T08:08:15.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"emotion-classification",
"region:us"
] | dair-ai | Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. | @inproceedings{saravia-etal-2018-carer,
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
author = "Saravia, Elvis and
Liu, Hsien-Chi Toby and
Huang, Yen-Hao and
Wu, Junlin and
Chen, Yi-Shin",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D18-1404",
doi = "10.18653/v1/D18-1404",
pages = "3687--3697",
abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
} | null | 127 | 6,650 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: emotion
pretty_name: Emotion
tags:
- emotion-classification
dataset_info:
- config_name: split
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': sadness
'1': joy
'2': love
'3': anger
'4': fear
'5': surprise
splits:
- name: train
num_bytes: 1741597
num_examples: 16000
- name: validation
num_bytes: 214703
num_examples: 2000
- name: test
num_bytes: 217181
num_examples: 2000
download_size: 740883
dataset_size: 2173481
- config_name: unsplit
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': sadness
'1': joy
'2': love
'3': anger
'4': fear
'5': surprise
splits:
- name: train
num_bytes: 45445685
num_examples: 416809
download_size: 15388281
dataset_size: 45445685
train-eval-index:
- config: default
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for "emotion"
## 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/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset)
- **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:** 16.13 MB
- **Size of the generated dataset:** 47.62 MB
- **Total amount of disk used:** 63.75 MB
### Dataset Summary
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
### 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
An example looks as follows.
```
{
"text": "im feeling quite sad and sorry for myself but ill snap out of it soon",
"label": 0
}
```
### Data Fields
The data fields are:
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5).
### Data Splits
The dataset has 2 configurations:
- split: with a total of 20_000 examples split into train, validation and split
- unsplit: with a total of 416_809 examples in a single train split
| name | train | validation | test |
|---------|-------:|-----------:|-----:|
| split | 16000 | 2000 | 2000 |
| unsplit | 416809 | n/a | n/a |
## 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
The dataset should be used for educational and research purposes only.
### Citation Information
If you use this dataset, please cite:
```
@inproceedings{saravia-etal-2018-carer,
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
author = "Saravia, Elvis and
Liu, Hsien-Chi Toby and
Huang, Yen-Hao and
Wu, Junlin and
Chen, Yi-Shin",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D18-1404",
doi = "10.18653/v1/D18-1404",
pages = "3687--3697",
abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
}
```
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.
|
enwik8 | 2023-04-06T14:14:17.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | null | The dataset is based on the Hutter Prize (http://prize.hutter1.net) and contains the first 10^8 bytes of English Wikipedia in 2006 in XML | null | null | 4 | 6,627 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: enwik8
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
task_ids:
- language-modeling
- masked-language-modeling
dataset_info:
- config_name: enwik8
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 104299244
num_examples: 1128024
download_size: 36445475
dataset_size: 102383126
- config_name: enwik8-raw
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 100000008
num_examples: 1
download_size: 36445475
dataset_size: 100000008
---
# Dataset Card for enwik8
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** http://mattmahoney.net/dc/textdata.html
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** https://paperswithcode.com/sota/language-modelling-on-enwiki8
- **Point of Contact:** [Needs More Information]
- **Size of downloaded dataset files:** 36.45 MB
- **Size of the generated dataset:** 102.38 MB
- **Total amount of disk used:** 138.83 MB
### Dataset Summary
The enwik8 dataset is the first 100,000,000 (100M) bytes of the English Wikipedia XML dump on Mar. 3, 2006 and is typically used to measure a model's ability to compress data.
### Supported Tasks and Leaderboards
A leaderboard for byte-level causal language modelling can be found on [paperswithcode](https://paperswithcode.com/sota/language-modelling-on-enwiki8)
### Languages
en
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 36.45 MB
- **Size of the generated dataset:** 102.38 MB
- **Total amount of disk used:** 138.83 MB
```
{
"text": "In [[Denmark]], the [[Freetown Christiania]] was created in downtown [[Copenhagen]]....",
}
```
### Data Fields
The data fields are the same among all sets.
#### enwik8
- `text`: a `string` feature.
#### enwik8-raw
- `text`: a `string` feature.
### Data Splits
| dataset | train |
| --- | --- |
| enwik8 | 1128024 |
| enwik8- raw | 1 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
The data is just English Wikipedia XML dump on Mar. 3, 2006 split by line for enwik8 and not split by line for enwik8-raw.
#### 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
Dataset is not part of a publication, and can therefore not be cited.
### Contributions
Thanks to [@HallerPatrick](https://github.com/HallerPatrick) for adding this dataset and [@mtanghu](https://github.com/mtanghu) for updating it. |
oscar | 2023-06-01T14:59:59.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:100M<n<1B",
"size_categories:10K<n<100K",
"size_categories:10M<n<100M",
"size_categories:1K<n<10K",
"size_categories:1M<n<10M",
"size_categories:n<1K",
"source_datasets:original",
"language:af",
"language:als",
"language:am",
"language:an",
"language:ar",
"language:arz",
"language:as",
"language:ast",
"language:av",
"language:az",
"language:azb",
"language:ba",
"language:bar",
"language:bcl",
"language:be",
"language:bg",
"language:bh",
"language:bn",
"language:bo",
"language:bpy",
"language:br",
"language:bs",
"language:bxr",
"language:ca",
"language:cbk",
"language:ce",
"language:ceb",
"language:ckb",
"language:cs",
"language:cv",
"language:cy",
"language:da",
"language:de",
"language:diq",
"language:dsb",
"language:dv",
"language:el",
"language:eml",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:frr",
"language:fy",
"language:ga",
"language:gd",
"language:gl",
"language:gn",
"language:gom",
"language:gu",
"language:he",
"language:hi",
"language:hr",
"language:hsb",
"language:ht",
"language:hu",
"language:hy",
"language:ia",
"language:id",
"language:ie",
"language:ilo",
"language:io",
"language:is",
"language:it",
"language:ja",
"language:jbo",
"language:jv",
"language:ka",
"language:kk",
"language:km",
"language:kn",
"language:ko",
"language:krc",
"language:ku",
"language:kv",
"language:kw",
"language:ky",
"language:la",
"language:lb",
"language:lez",
"language:li",
"language:lmo",
"language:lo",
"language:lrc",
"language:lt",
"language:lv",
"language:mai",
"language:mg",
"language:mhr",
"language:min",
"language:mk",
"language:ml",
"language:mn",
"language:mr",
"language:mrj",
"language:ms",
"language:mt",
"language:mwl",
"language:my",
"language:myv",
"language:mzn",
"language:nah",
"language:nap",
"language:nds",
"language:ne",
"language:new",
"language:nl",
"language:nn",
"language:no",
"language:oc",
"language:or",
"language:os",
"language:pa",
"language:pam",
"language:pl",
"language:pms",
"language:pnb",
"language:ps",
"language:pt",
"language:qu",
"language:rm",
"language:ro",
"language:ru",
"language:sa",
"language:sah",
"language:scn",
"language:sd",
"language:sh",
"language:si",
"language:sk",
"language:sl",
"language:so",
"language:sq",
"language:sr",
"language:su",
"language:sv",
"language:sw",
"language:ta",
"language:te",
"language:tg",
"language:th",
"language:tk",
"language:tl",
"language:tr",
"language:tt",
"language:tyv",
"language:ug",
"language:uk",
"language:ur",
"language:uz",
"language:vec",
"language:vi",
"language:vo",
"language:wa",
"language:war",
"language:wuu",
"language:xal",
"language:xmf",
"language:yi",
"language:yo",
"language:yue",
"language:zh",
"license:cc0-1.0",
"arxiv:2010.14571",
"region:us"
] | null | The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.\ | @inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{\'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{\'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{\"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
} | null | 122 | 6,620 | ---
pretty_name: OSCAR
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multilinguality:
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- unshuffled_deduplicated_dsb
- unshuffled_deduplicated_dv
- unshuffled_deduplicated_el
- unshuffled_deduplicated_eml
- unshuffled_deduplicated_en
- unshuffled_deduplicated_eo
- unshuffled_deduplicated_es
- unshuffled_deduplicated_et
- unshuffled_deduplicated_eu
- unshuffled_deduplicated_fa
- unshuffled_deduplicated_fi
- unshuffled_deduplicated_fr
- unshuffled_deduplicated_frr
- unshuffled_deduplicated_fy
- unshuffled_deduplicated_ga
- unshuffled_deduplicated_gd
- unshuffled_deduplicated_gl
- unshuffled_deduplicated_gn
- unshuffled_deduplicated_gom
- unshuffled_deduplicated_gu
- unshuffled_deduplicated_he
- unshuffled_deduplicated_hi
- unshuffled_deduplicated_hr
- unshuffled_deduplicated_hsb
- unshuffled_deduplicated_ht
- unshuffled_deduplicated_hu
- unshuffled_deduplicated_hy
- unshuffled_deduplicated_ia
- unshuffled_deduplicated_id
- unshuffled_deduplicated_ie
- unshuffled_deduplicated_ilo
- unshuffled_deduplicated_io
- unshuffled_deduplicated_is
- unshuffled_deduplicated_it
- unshuffled_deduplicated_ja
- unshuffled_deduplicated_jbo
- unshuffled_deduplicated_jv
- unshuffled_deduplicated_ka
- unshuffled_deduplicated_kk
- unshuffled_deduplicated_km
- unshuffled_deduplicated_kn
- unshuffled_deduplicated_ko
- unshuffled_deduplicated_krc
- unshuffled_deduplicated_ku
- unshuffled_deduplicated_kv
- unshuffled_deduplicated_kw
- unshuffled_deduplicated_ky
- unshuffled_deduplicated_la
- unshuffled_deduplicated_lb
- unshuffled_deduplicated_lez
- unshuffled_deduplicated_li
- unshuffled_deduplicated_lmo
- unshuffled_deduplicated_lo
- unshuffled_deduplicated_lrc
- unshuffled_deduplicated_lt
- unshuffled_deduplicated_lv
- unshuffled_deduplicated_mai
- unshuffled_deduplicated_mg
- unshuffled_deduplicated_mhr
- unshuffled_deduplicated_min
- unshuffled_deduplicated_mk
- unshuffled_deduplicated_ml
- unshuffled_deduplicated_mn
- unshuffled_deduplicated_mr
- unshuffled_deduplicated_mrj
- unshuffled_deduplicated_ms
- unshuffled_deduplicated_mt
- unshuffled_deduplicated_mwl
- unshuffled_deduplicated_my
- unshuffled_deduplicated_myv
- unshuffled_deduplicated_mzn
- unshuffled_deduplicated_nah
- unshuffled_deduplicated_nap
- unshuffled_deduplicated_nds
- unshuffled_deduplicated_ne
- unshuffled_deduplicated_new
- unshuffled_deduplicated_nl
- unshuffled_deduplicated_nn
- unshuffled_deduplicated_no
- unshuffled_deduplicated_oc
- unshuffled_deduplicated_or
- unshuffled_deduplicated_os
- unshuffled_deduplicated_pa
- unshuffled_deduplicated_pam
- unshuffled_deduplicated_pl
- unshuffled_deduplicated_pms
- unshuffled_deduplicated_pnb
- unshuffled_deduplicated_ps
- unshuffled_deduplicated_pt
- unshuffled_deduplicated_qu
- unshuffled_deduplicated_rm
- unshuffled_deduplicated_ro
- unshuffled_deduplicated_ru
- unshuffled_deduplicated_sa
- unshuffled_deduplicated_sah
- unshuffled_deduplicated_scn
- unshuffled_deduplicated_sd
- unshuffled_deduplicated_sh
- unshuffled_deduplicated_si
- unshuffled_deduplicated_sk
- unshuffled_deduplicated_sl
- unshuffled_deduplicated_so
- unshuffled_deduplicated_sq
- unshuffled_deduplicated_sr
- unshuffled_deduplicated_su
- unshuffled_deduplicated_sv
- unshuffled_deduplicated_sw
- unshuffled_deduplicated_ta
- unshuffled_deduplicated_te
- unshuffled_deduplicated_tg
- unshuffled_deduplicated_th
- unshuffled_deduplicated_tk
- unshuffled_deduplicated_tl
- unshuffled_deduplicated_tr
- unshuffled_deduplicated_tt
- unshuffled_deduplicated_tyv
- unshuffled_deduplicated_ug
- unshuffled_deduplicated_uk
- unshuffled_deduplicated_ur
- unshuffled_deduplicated_uz
- unshuffled_deduplicated_vec
- unshuffled_deduplicated_vi
- unshuffled_deduplicated_vo
- unshuffled_deduplicated_wa
- unshuffled_deduplicated_war
- unshuffled_deduplicated_wuu
- unshuffled_deduplicated_xal
- unshuffled_deduplicated_xmf
- unshuffled_deduplicated_yi
- unshuffled_deduplicated_yo
- unshuffled_deduplicated_yue
- unshuffled_deduplicated_zh
- unshuffled_original_af
- unshuffled_original_als
- unshuffled_original_am
- unshuffled_original_an
- unshuffled_original_ar
- unshuffled_original_arz
- unshuffled_original_as
- unshuffled_original_ast
- unshuffled_original_av
- unshuffled_original_az
- unshuffled_original_azb
- unshuffled_original_ba
- unshuffled_original_bar
- unshuffled_original_bcl
- unshuffled_original_be
- unshuffled_original_bg
- unshuffled_original_bh
- unshuffled_original_bn
- unshuffled_original_bo
- unshuffled_original_bpy
- unshuffled_original_br
- unshuffled_original_bs
- unshuffled_original_bxr
- unshuffled_original_ca
- unshuffled_original_cbk
- unshuffled_original_ce
- unshuffled_original_ceb
- unshuffled_original_ckb
- unshuffled_original_cs
- unshuffled_original_cv
- unshuffled_original_cy
- unshuffled_original_da
- unshuffled_original_de
- unshuffled_original_diq
- unshuffled_original_dsb
- unshuffled_original_dv
- unshuffled_original_el
- unshuffled_original_eml
- unshuffled_original_en
- unshuffled_original_eo
- unshuffled_original_es
- unshuffled_original_et
- unshuffled_original_eu
- unshuffled_original_fa
- unshuffled_original_fi
- unshuffled_original_fr
- unshuffled_original_frr
- unshuffled_original_fy
- unshuffled_original_ga
- unshuffled_original_gd
- unshuffled_original_gl
- unshuffled_original_gn
- unshuffled_original_gom
- unshuffled_original_gu
- unshuffled_original_he
- unshuffled_original_hi
- unshuffled_original_hr
- unshuffled_original_hsb
- unshuffled_original_ht
- unshuffled_original_hu
- unshuffled_original_hy
- unshuffled_original_ia
- unshuffled_original_id
- unshuffled_original_ie
- unshuffled_original_ilo
- unshuffled_original_io
- unshuffled_original_is
- unshuffled_original_it
- unshuffled_original_ja
- unshuffled_original_jbo
- unshuffled_original_jv
- unshuffled_original_ka
- unshuffled_original_kk
- unshuffled_original_km
- unshuffled_original_kn
- unshuffled_original_ko
- unshuffled_original_krc
- unshuffled_original_ku
- unshuffled_original_kv
- unshuffled_original_kw
- unshuffled_original_ky
- unshuffled_original_la
- unshuffled_original_lb
- unshuffled_original_lez
- unshuffled_original_li
- unshuffled_original_lmo
- unshuffled_original_lo
- unshuffled_original_lrc
- unshuffled_original_lt
- unshuffled_original_lv
- unshuffled_original_mai
- unshuffled_original_mg
- unshuffled_original_mhr
- unshuffled_original_min
- unshuffled_original_mk
- unshuffled_original_ml
- unshuffled_original_mn
- unshuffled_original_mr
- unshuffled_original_mrj
- unshuffled_original_ms
- unshuffled_original_mt
- unshuffled_original_mwl
- unshuffled_original_my
- unshuffled_original_myv
- unshuffled_original_mzn
- unshuffled_original_nah
- unshuffled_original_nap
- unshuffled_original_nds
- unshuffled_original_ne
- unshuffled_original_new
- unshuffled_original_nl
- unshuffled_original_nn
- unshuffled_original_no
- unshuffled_original_oc
- unshuffled_original_or
- unshuffled_original_os
- unshuffled_original_pa
- unshuffled_original_pam
- unshuffled_original_pl
- unshuffled_original_pms
- unshuffled_original_pnb
- unshuffled_original_ps
- unshuffled_original_pt
- unshuffled_original_qu
- unshuffled_original_rm
- unshuffled_original_ro
- unshuffled_original_ru
- unshuffled_original_sa
- unshuffled_original_sah
- unshuffled_original_scn
- unshuffled_original_sd
- unshuffled_original_sh
- unshuffled_original_si
- unshuffled_original_sk
- unshuffled_original_sl
- unshuffled_original_so
- unshuffled_original_sq
- unshuffled_original_sr
- unshuffled_original_su
- unshuffled_original_sv
- unshuffled_original_sw
- unshuffled_original_ta
- unshuffled_original_te
- unshuffled_original_tg
- unshuffled_original_th
- unshuffled_original_tk
- unshuffled_original_tl
- unshuffled_original_tr
- unshuffled_original_tt
- unshuffled_original_tyv
- unshuffled_original_ug
- unshuffled_original_uk
- unshuffled_original_ur
- unshuffled_original_uz
- unshuffled_original_vec
- unshuffled_original_vi
- unshuffled_original_vo
- unshuffled_original_wa
- unshuffled_original_war
- unshuffled_original_wuu
- unshuffled_original_xal
- unshuffled_original_xmf
- unshuffled_original_yi
- unshuffled_original_yo
- unshuffled_original_yue
- unshuffled_original_zh
---
# Dataset Card for "oscar"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://oscar-corpus.com](https://oscar-corpus.com)
- **Repository:** [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)
### Dataset Summary
OSCAR or **O**pen **S**uper-large **C**rawled [**A**LMAnaCH](https://team.inria.fr/almanach/) co**R**pus is a huge multilingual corpus obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) corpus using the [goclassy](https://github.com/pjox/goclassy) architecture. Data is distributed by language in both original and deduplicated form.
The version here is the original OSCAR 2019 release: https://oscar-project.org/post/oscar-2019/
For more recent versions, visit the [oscar-corpus](https://huggingface.co/oscar-corpus) organization on the Hub:
- OSCAR 22.01 (released in January 2022): [oscar-corpus/OSCAR-2201](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201)
- OSCAR 21.09 (released in September 2021): [oscar-corpus/OSCAR-2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)
### Supported Tasks and Leaderboards
OSCAR is mainly inteded to pretrain language models and word represantations.
### Languages
All the data is distributed by language, both the original and the deduplicated versions of the data are available. 166 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR.
## Dataset Structure
We show detailed information for all the configurations of the dataset.
### Data Instances
<details>
<summary>Click to expand the Data/size information for each language (deduplicated)</summary>
#### unshuffled_deduplicated_af
- **Size of downloaded dataset files:** 65.99 MB
- **Size of the generated dataset:** 172.30 MB
- **Total amount of disk used:** 238.29 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "aanlyn markte as gevolg van ons voortgesette 'n begrip opsie handel sakeplan pdf terwyl ons steeds die gereelde ons binêre opsies handel"
}
```
#### unshuffled_deduplicated_als
- **Size of downloaded dataset files:** 1.26 MB
- **Size of the generated dataset:** 2.96 MB
- **Total amount of disk used:** 4.22 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"De Nazionalpark hät e Flächi vo 170,3 km² und isch dodemit s grösti Naturschutzgebiet vo de Schwiz. Er ligt uf em Gebiet vo de ..."
}
```
#### unshuffled_deduplicated_am
- **Size of downloaded dataset files:** 61.35 MB
- **Size of the generated dataset:** 216.15 MB
- **Total amount of disk used:** 277.50 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"አየር መንገዱ ከአዲስ አበባ ወደ ሮም ጣሊያን በማምራት ላይ በነበረበት ጊዜ ረዳት አብራሪው የጉዞውን አቅጣጫ በመቀየር ጄኔቭ አውሮፓላን ማረፊያ በማሳረፍ እጁን ለፖሊስ ሰጥቷል።\\nየኢትዮጵያ መንግስት የ..."
}
```
#### unshuffled_deduplicated_an
- **Size of downloaded dataset files:** 0.14 MB
- **Size of the generated dataset:** 0.85 MB
- **Total amount of disk used:** 0.99 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"واااااااأسفاه الأمم تفتخر ب 0 أمي ووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووو..."
}
```
#### unshuffled_deduplicated_ar
- **Size of downloaded dataset files:** 9.67 GB
- **Size of the generated dataset:** 33.57 GB
- **Total amount of disk used:** 43.23 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"مرحبا بك عزيز الزائر نتمنى لك أوقاتاً سعيدة معنا وأن نزداد شرفا بخدمتك ولا تنسى التسجيل معنا لتستفيد بكل جديد\\nأهلا وسهلا بك زا..."
}
```
#### unshuffled_deduplicated_arz
- **Size of downloaded dataset files:** 10.02 MB
- **Size of the generated dataset:** 35.91 MB
- **Total amount of disk used:** 45.94 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"بنى عجل : قبيلة من عجل بن لجيم بن صعب بن على بن بكر بن وائل انتقل اغلبهم الى البصرة فى العراق و اصفهان و خراسان فى ايران و اذرب..."
}
```
#### unshuffled_deduplicated_as
- **Size of downloaded dataset files:** 15.51 MB
- **Size of the generated dataset:** 74.07 MB
- **Total amount of disk used:** 89.58 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"আমি, এই সংগঠনৰ সদস্য সকলে একেলগ হৈ অসমকে ধৰি ভাৰতৰ উত্তৰ পূৰ্বাঞ্চলৰ অমূল্য কলা-সাংস্কৃতিক সম্পদৰাজি বৃহত্তৰ অষ্ট্ৰেলিয়াৰ সন্মু..."
}
```
#### unshuffled_deduplicated_ast
- **Size of downloaded dataset files:** 0.86 MB
- **Size of the generated dataset:** 2.17 MB
- **Total amount of disk used:** 3.03 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"The Killers llanzaron el so álbum debú, Hot Fuss, en xunu de 2004 nel Reinu Xuníu, al traviés de la discográfica Lizard King, y..."
}
```
#### unshuffled_deduplicated_av
- **Size of downloaded dataset files:** 0.07 MB
- **Size of the generated dataset:** 0.34 MB
- **Total amount of disk used:** 0.41 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Жинда малъараб ва божизе бегьулеб рагІудаса кьуризе бегьуларо гьев. Гьес насихІат гьабизе кколелъул бацІцІадаб диналъул рахъалъ..."
}
```
#### unshuffled_deduplicated_az
- **Size of downloaded dataset files:** 521.74 MB
- **Size of the generated dataset:** 1.53 GB
- **Total amount of disk used:** 2.05 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"AZTV-Artıq 7 ildir ki, Abşeron rayonu dotasiya almadan bütün xərclərini yerli daxilolmalar hesabına maliyyələşdirir.\\nDünən, 10..."
}
```
#### unshuffled_deduplicated_azb
- **Size of downloaded dataset files:** 5.19 MB
- **Size of the generated dataset:** 20.08 MB
- **Total amount of disk used:** 25.27 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"لعلی ١٣-جو عصرده یاشاییب یاراتمیش گؤرکملی آذربایجان شاعرلریندندیر. ١٢٢٤-جی ایلده تبریزده آنادان اولموشدور، گنج یاشلاریندا تیجار..."
}
```
#### unshuffled_deduplicated_ba
- **Size of downloaded dataset files:** 25.98 MB
- **Size of the generated dataset:** 93.84 MB
- **Total amount of disk used:** 119.82 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Күҙәтеү ҡуласаһы моделен хәҙер Мифтахетдин Аҡмулла исемендәге Башҡорт дәүләт педагогия университетында ла эшләргә мөмкин\\t\\nКүҙ..."
}
```
#### unshuffled_deduplicated_bar
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": " vo"
}
```
#### unshuffled_deduplicated_bcl
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"& ÿ ó / í 0 - ø û ù ö ú ð ï ú \\u0014 ù þ ô ö í ÷ ò \\u0014 ÷ í ù û ö í \\u0001 û ñ ç þ \\u0001 ð \\u0007 þ ò ñ ñ ò ô \\u0017 û ö ô ÷..."
}
```
#### unshuffled_deduplicated_be
- **Size of downloaded dataset files:** 306.70 MB
- **Size of the generated dataset:** 1.08 GB
- **Total amount of disk used:** 1.39 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Брэсцкія ўлады не дазволілі прафсаюзу РЭП правесці пікетаванне ў парку Воінаў-інтэрнацыяналістаў 30 мая 2018 года.\\nСітуацыю пр..."
}
```
#### unshuffled_deduplicated_bg
- **Size of downloaded dataset files:** 3.85 GB
- **Size of the generated dataset:** 14.45 GB
- **Total amount of disk used:** 18.30 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ЖАЛБОПОДАТЕЛЯТ директор на Дирекция „ Обжалване и данъчно-осигурителна практика“- Бургас, редовно призован, се представлява от ..."
}
```
#### unshuffled_deduplicated_bh
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.04 MB
- **Total amount of disk used:** 0.04 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"सुकमा जिला भारत के छत्तीसगढ़ राज्य में एगो जिला बाटे। एकर मुख्यालय सुकमा शहर बाटे। एकर कुल रकबा 5636 वर्ग कि॰मी॰ बाटे।\"..."
}
```
#### unshuffled_deduplicated_bn
- **Size of downloaded dataset files:** 1.26 GB
- **Size of the generated dataset:** 6.24 GB
- **Total amount of disk used:** 7.50 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ভড়ং সর্বস্ব বাংলা আর্ট অ্যান্ড কালচারের হিসাব গুলিয়ে দেওয়ার ম্যাজিকের নাম ব্রাত্য রাইসু November 23, 2017\\nTagged with ডায়োজিনি..."
}
```
#### unshuffled_deduplicated_bo
- **Size of downloaded dataset files:** 22.37 MB
- **Size of the generated dataset:** 144.65 MB
- **Total amount of disk used:** 167.02 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"བོད་མི་འདི་དག་ནི་རང་རྒྱུད་སྒོ་རུ་ཕུད་དེ་གཞན་རྒྱུད་པང་དུ་ཉར་ནས་གསོ་སྐྱོང་བྱེད་དགོས་ཟེར་བ་དང་གཅིག་མཚུངས་རེད།\\nཚན་རིག་ནི་དང་ཐོག་རང..."
}
```
#### unshuffled_deduplicated_bpy
- **Size of downloaded dataset files:** 0.19 MB
- **Size of the generated dataset:** 1.78 MB
- **Total amount of disk used:** 1.97 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"পৌরসভা এহার আয়তন (লয়াহান) ২,৭৩০,.৬৩ বর্গ কিলোমিটার। পৌরসভা এহার মাপাহানর অক্ষাংশ বারো দ্রাঘিমাংশ ইলতাই 18.63° S 48.18° W ।[১]..."
}
```
#### unshuffled_deduplicated_br
- **Size of downloaded dataset files:** 6.47 MB
- **Size of the generated dataset:** 17.00 MB
- **Total amount of disk used:** 23.47 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Ar mank Magalhães(Daveoù a vank) a zo ur spesad evned, Spheniscus magellanicus an anv skiantel anezhañ.\\nGallout a reer implijo..."
}
```
#### unshuffled_deduplicated_bs
- **Size of downloaded dataset files:** 0.04 MB
- **Size of the generated dataset:** 0.15 MB
- **Total amount of disk used:** 0.18 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ž šř é ú šř šř ě šř ž é č ě ž ů ě ď éé ýš ě ě Ž č š ý ě ď é ýš ě ď ě éé ýš ě č ž ě š ý ď ě ýš é ú č ž č š ý ď ý ž é éě ď é č ýš..."
}
```
#### unshuffled_deduplicated_bxr
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.01 MB
- **Total amount of disk used:** 0.01 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"2002 оной хабар буряад хэлэ бэшэгэй һалбари Үндэһэтэнэй хүмүүнлиг ухаанай дээдэ һургуули болгогдожо өөршэлэгдөө.\\nХарин мүнөө б..."
}
```
#### unshuffled_deduplicated_ca
- **Size of downloaded dataset files:** 1.73 GB
- **Size of the generated dataset:** 4.57 GB
- **Total amount of disk used:** 6.30 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Daniel Vendrell, conegut com Vandrell, ha sigut un dels il•lustradors contemporanis més influents, representant a la nova onada..."
}
```
#### unshuffled_deduplicated_cbk
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano..."
}
```
#### unshuffled_deduplicated_ce
- **Size of downloaded dataset files:** 1.87 MB
- **Size of the generated dataset:** 7.04 MB
- **Total amount of disk used:** 8.90 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Шаьш анархисташ ду бохучу жигархойн дIахьедарехь дуьйцу, оьрсийн ницкъаллийн структурийн а, федералан каналан а Iалашонаш \\\"мар..."
}
```
#### unshuffled_deduplicated_ceb
- **Size of downloaded dataset files:** 7.12 MB
- **Size of the generated dataset:** 24.83 MB
- **Total amount of disk used:** 31.95 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Si Isko walay pupamilok nga nagtan-aw sa unahan, natugaw. “Naunsa ka gud diha Isko nga layo man kaayo ang imong panan-aw?” ni I..."
}
```
#### unshuffled_deduplicated_ckb
- **Size of downloaded dataset files:** 60.32 MB
- **Size of the generated dataset:** 237.72 MB
- **Total amount of disk used:** 298.05 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"رسی رۆژ - ساڵێک دوای بومەلەرزەی کرماشان میوانی بەرنامە : کاک سیاوەش حەیاتی چالاکی مەدەنی -قەسری شیرین\\nپارچە موزیک 30 / 10 / 20..."
}
```
#### unshuffled_deduplicated_cs
- **Size of downloaded dataset files:** 10.49 GB
- **Size of the generated dataset:** 25.71 GB
- **Total amount of disk used:** 36.20 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Akce anarchistů proti připravovanému novému služební řádu a nízkým mzdám 1903 – Historie českého anarchismu (1880 – 1939)\\nRost..."
}
```
#### unshuffled_deduplicated_cv
- **Size of downloaded dataset files:** 7.47 MB
- **Size of the generated dataset:** 27.49 MB
- **Total amount of disk used:** 34.95 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Шыранӑ чухне ӑнсӑртран латин кирилл саспаллисем вырӑнне латин саспаллисене ҫырсан, сайт эсир ҫырнине юсама тӑрӑшӗ.\\nКу сайтра ч..."
}
```
#### unshuffled_deduplicated_cy
- **Size of downloaded dataset files:** 53.63 MB
- **Size of the generated dataset:** 141.22 MB
- **Total amount of disk used:** 194.86 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Mae capeli Cymreig yr Andes ym Mhatagonia wedi cyhoeddi na fydd gwasanaethau yno weddill y mis, oherwydd yr eira trwm sydd wedi..."
}
```
#### unshuffled_deduplicated_da
- **Size of downloaded dataset files:** 3.82 GB
- **Size of the generated dataset:** 10.24 GB
- **Total amount of disk used:** 14.06 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Den 2.-5. februar 2016 løb det tredje kursus i uddannelsen af 4kommunesamarbejdets Local Impact Coaches, af stablen i Gentofte ..."
}
```
#### unshuffled_deduplicated_de
- **Size of downloaded dataset files:** 60.80 GB
- **Size of the generated dataset:** 156.30 GB
- **Total amount of disk used:** 217.10 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Auf dieser Seite gibt es mind. ein YouTube Video. Cookies für diese Website wurden abgelehnt. Dadurch können keine YouTube Vide..."
}
```
#### unshuffled_deduplicated_diq
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "Zıwanê Slawki, zıwano merdumanê Slawano. Zıwanê Slawki yew lızgeyê Zıwananê Hind u Ewropao. Keyeyê Zıwananê Slawki beno hirê letey:"
}
```
#### unshuffled_deduplicated_dsb
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.01 MB
- **Total amount of disk used:** 0.01 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Pśiklaskaju južo pśed pśedstajenim... 1500 źiśi njamóžo wěcej docakaś, měsćańska hala w Chóśebuzu - wupśedana."
}
```
#### unshuffled_deduplicated_dv
- **Size of downloaded dataset files:** 16.84 MB
- **Size of the generated dataset:** 82.19 MB
- **Total amount of disk used:** 99.03 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ބ. އަތޮޅުގައި ހުޅުވަން ތައްޔާރުވަމުން އަންނަ ވައްކަރު ރިސޯޓުގައި ވަޒީފާ އަދާކުރަން ޝައުގުވެރިވާ ފަރާތްތަކަށް ކުރިމަތިލުމުގެ ފުރ..."
}
```
#### unshuffled_deduplicated_el
- **Size of downloaded dataset files:** 7.91 GB
- **Size of the generated dataset:** 28.74 GB
- **Total amount of disk used:** 36.65 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Νεκρός εντοπίστηκε μέσα στο σπίτι του στην οδό Ηρώδου Αττικού στον αριθμό 7 ο επικεφαλής του προξενικού τμήματος της Ρωσικής πρ..."
}
```
#### unshuffled_deduplicated_eml
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.02 MB
- **Total amount of disk used:** 0.03 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"A séguit dal prucès ad rubutiśasiòṅ di abitànt dal pòpul ad Mikenes, Angoras 'l è finî dènt'r a 'n robot cun la tèsta dna rana ..."
}
```
#### unshuffled_deduplicated_en
- **Size of downloaded dataset files:** 496.50 GB
- **Size of the generated dataset:** 1299.75 GB
- **Total amount of disk used:** 1796.24 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Mtendere Village was inspired by the vision of Chief Napoleon Dzombe, which he shared with John Blanchard during his first visi..."
}
```
#### unshuffled_deduplicated_eo
- **Size of downloaded dataset files:** 92.86 MB
- **Size of the generated dataset:** 240.12 MB
- **Total amount of disk used:** 332.99 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Ĉu ... preĝi | mediti | ricevi instigojn || kanti | muziki || informiĝi | legi | studi || prepari Diservon\\nTemas pri kolekto d..."
}
```
#### unshuffled_deduplicated_es
- **Size of downloaded dataset files:** 60.46 GB
- **Size of the generated dataset:** 160.86 GB
- **Total amount of disk used:** 221.32 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Como se librará de la celulitis en el gimnasio La piel superflua en las manos después del adelgazamiento, Los bailes fáciles pa..."
}
```
#### unshuffled_deduplicated_et
- **Size of downloaded dataset files:** 966.79 MB
- **Size of the generated dataset:** 2.45 GB
- **Total amount of disk used:** 3.41 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"MTÜ AB Video järgib oma tegevuses kodanikuühenduste eetilise tegevuse üldtunnustatud põhimõtteid, mis on lühidalt kokkuvõetud 7..."
}
```
#### unshuffled_deduplicated_eu
- **Size of downloaded dataset files:** 134.68 MB
- **Size of the generated dataset:** 363.93 MB
- **Total amount of disk used:** 498.61 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "Gure jarduerek eraikuntzarekin, elkarbizitzarekin, hirigintzarekin eta ekologiarekin dute harremana, baita ideia eta konponbideak irudikatu eta garatzearekin ere, eraikuntza sektorea hobetuz, pertsonen erosotasuna eta bizi-kalitatea hobetzeko."
}
```
#### unshuffled_deduplicated_fa
- **Size of downloaded dataset files:** 10.46 GB
- **Size of the generated dataset:** 40.06 GB
- **Total amount of disk used:** 50.52 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"قـــــــــــــــــرار بود با هم کنـــــــــــــار بیایم نه اینکه از کنــــــــــــار هم رد بشیم...!!!\\nاگر روزی دلت لبریز غم بو..."
}
```
#### unshuffled_deduplicated_fi
- **Size of downloaded dataset files:** 5.38 GB
- **Size of the generated dataset:** 13.99 GB
- **Total amount of disk used:** 19.37 GB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Kiitos Deelle kaikesta - 1,5 viikkoa kulunut, kun Dee ei ole enää ollut omani. Reilu viikko sitten sunnuntaina vein Deen uuteen kotiinsa. Itselläni on ollut niin ristiriitaiset t..."
}
```
#### unshuffled_deduplicated_fr
- **Size of downloaded dataset files:** 55.46 GB
- **Size of the generated dataset:** 148.28 GB
- **Total amount of disk used:** 203.75 GB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "Média de débat d'idées, de culture et de littérature. Récits, décryptages, analyses, portraits et critiques autour de la vie des idées. Magazine engagé, ouvert aux autres et au monde.. Bring up to date in french"
}
```
#### unshuffled_deduplicated_frr
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Hiragana’ Practice’Sheet’1’(A -O)’ ’ Name:’________ __________________________’Section:’_______________ _’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ..."
}
```
#### unshuffled_deduplicated_fy
- **Size of downloaded dataset files:** 10.27 MB
- **Size of the generated dataset:** 26.73 MB
- **Total amount of disk used:** 37.00 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Nim in sêfte ride op Holmsjön, yn ien fan 'e lytse marren yn de omkriten, of nim se op avontueren lykas nonresidential. lâns Indalsälven wetter. Holm Sportklubb hawwe kano 's te huur, yn gearwurking mei de Baltyske Power konferinsje."
}
```
#### unshuffled_deduplicated_ga
- **Size of downloaded dataset files:** 22.22 MB
- **Size of the generated dataset:** 63.86 MB
- **Total amount of disk used:** 86.08 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Is fóram é seo chun plé a dhéanamh ar an leabhar atá roghnaithe do mhí na Samhna 2013 amháin. Ní féidir ach le baill chláraithe..."
}
```
#### unshuffled_deduplicated_gd
- **Size of downloaded dataset files:** 0.42 MB
- **Size of the generated dataset:** 1.36 MB
- **Total amount of disk used:** 1.78 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "Zhou Yujun, a 'phàrtaidh Rùnaire Comataidh Sgìre Yanfeng ann Hengyang bhaile agus a Sgìre pàrtaidh agus an riaghaltas a' bhuidheann-riochdachaidh a 'tighinn a chèilidh air ar companaidh air Apr. 14, 2017."
}
```
#### unshuffled_deduplicated_gl
- **Size of downloaded dataset files:** 155.85 MB
- **Size of the generated dataset:** 408.34 MB
- **Total amount of disk used:** 564.19 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"O persoal de Inditex da provincia de Pontevedra segue a reclamar iguais condicións laborais no conxunto do país - CIG: Confeder..."
}
```
#### unshuffled_deduplicated_gn
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.02 MB
- **Total amount of disk used:** 0.03 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"º ÑÆÚÓ À Ã Ð É Æ ¾ ÄÂ Î À ¼ Æ É ÄÛ = Ü Ý\\\"Þ ßà á â ã ä å æçè ã é ê â å àë ì æê íî é á ë ï í çì àð í Ü à ñ ê é ò ä ì\"..."
}
```
#### unshuffled_deduplicated_gom
- **Size of downloaded dataset files:** 0.38 MB
- **Size of the generated dataset:** 1.87 MB
- **Total amount of disk used:** 2.24 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"दुष्ट शीळ हें कौरवांचें । रामें सविस्तर देखूनि साचें । बोलिले वचनें जें दुर्वाचे । करी तयांचें अनुस्मरण ॥२२०॥\"..."
}
```
#### unshuffled_deduplicated_gu
- **Size of downloaded dataset files:** 162.97 MB
- **Size of the generated dataset:** 759.34 MB
- **Total amount of disk used:** 922.32 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"અધિક માસ ચાલે છે. સમગ્ર ભારતમાં અને તેમાંય ખાસ કરીને પવિત્ર કે ધાર્મિક કહેવાય છે તેવા સ્થાનક પર કથાનો દોર ચાલે છે. ઉનાળાની કાળઝ..."
}
```
#### unshuffled_deduplicated_he
- **Size of downloaded dataset files:** 3.04 GB
- **Size of the generated dataset:** 10.47 GB
- **Total amount of disk used:** 13.51 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"זקוקים לרשתות נגד יתושים? מחפשים רשת מתאימה לחלון צר וקטן? רשתות נגד יתושים אקורדיון של חברת קליר-מש הן הפתרון.\\nרשתות לחלונות ..."
}
```
#### unshuffled_deduplicated_hi
- **Size of downloaded dataset files:** 2.01 GB
- **Size of the generated dataset:** 9.57 GB
- **Total amount of disk used:** 11.58 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"'आइटम गर्ल' बनकर हिट हुई थीं राखी सावंत, आज करीना-कटरीना तक फॉलो कर रही हैं ट्रेंड नक्सलियों का दम निकालेगा बाइक ग्रेनेड लॉन्च..."
}
```
#### unshuffled_deduplicated_hr
- **Size of downloaded dataset files:** 46.74 MB
- **Size of the generated dataset:** 121.50 MB
- **Total amount of disk used:** 168.23 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"U raspravi je sudjelovao i HSS-ov saborski zastupnik rekavši kako poljoprivrednici ne osjete mjere o kojima ministar govori jer..."
}
```
#### unshuffled_deduplicated_hsb
- **Size of downloaded dataset files:** 0.72 MB
- **Size of the generated dataset:** 1.89 MB
- **Total amount of disk used:** 2.61 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Budyšin (SN/BŠe). Elektronikarjo mějachu lětsa cyle hinaši zazběh do swojeho wukubłanja. Wokrjesne rjemjeslnistwo bě mjenujcy w..."
}
```
#### unshuffled_deduplicated_ht
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan..."
}
```
#### unshuffled_deduplicated_hu
- **Size of downloaded dataset files:** 7.37 GB
- **Size of the generated dataset:** 19.09 GB
- **Total amount of disk used:** 26.46 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"monster - Amatőr, házi szex videók és kezdő csjaok pornó filmjei. - Free amateur, home made sex videos and online porn movies. ..."
}
```
#### unshuffled_deduplicated_hy
- **Size of downloaded dataset files:** 393.62 MB
- **Size of the generated dataset:** 1.56 GB
- **Total amount of disk used:** 1.96 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Արցախի Հանրապետության հռչակման 26-րդ տարեդարձի կապակցությամբ Շուշիի Արվեստի կենտրոնում կազմակերպվել է մոսկվաբնակ նկարիչներ՝ հայ..."
}
```
#### unshuffled_deduplicated_ia
- **Size of downloaded dataset files:** 0.05 MB
- **Size of the generated dataset:** 0.38 MB
- **Total amount of disk used:** 0.43 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha h..."
}
```
#### unshuffled_deduplicated_id
- **Size of downloaded dataset files:** 6.00 GB
- **Size of the generated dataset:** 17.05 GB
- **Total amount of disk used:** 23.05 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Perihal dari itu, kalau kunci hal yang demikian hilang, pemilik wajib melapor ke bengkel sah untuk dibuatkan kunci baru dengan ..."
}
```
#### unshuffled_deduplicated_ie
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "Plastic Yo Yo Metal Yo Yos Wooden Yo Yo Keychain Yo Yo Translucent Yo Yo Light Up Yo Yo Globe Yo Yo Stress Reliever Yo Yo Jellyfish Yo Yo Sports Ball Yo Yo Sound Yo Yo Miniature Yo Yo Promotional Yo Yo Novelty Yo Yo Video Game Yo Yo ECO Recycled Yo Yo"
}
```
#### unshuffled_deduplicated_ilo
- **Size of downloaded dataset files:** 0.23 MB
- **Size of the generated dataset:** 0.68 MB
- **Total amount of disk used:** 0.91 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Segun ken ni Ping-ay, ti yellow corn ti maysa kadagiti nadakamat a liberalized agricultural commodity iti daytoy a free trade k..."
}
```
#### unshuffled_deduplicated_io
- **Size of downloaded dataset files:** 0.04 MB
- **Size of the generated dataset:** 0.14 MB
- **Total amount of disk used:** 0.19 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Chekia esas parlamentala republiko. La chefo di stato esas la prezidanto. Til 2013 lu elektesis dal parlamento. Pos ta yaro, ol..."
}
```
#### unshuffled_deduplicated_is
- **Size of downloaded dataset files:** 332.87 MB
- **Size of the generated dataset:** 894.28 MB
- **Total amount of disk used:** 1.23 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Eyjar.net - upplýsinga- og fréttamiðill um Vestmannaeyjar - Fréttir - Nái núverandi stefna stjórnvalda fram að ganga mun það va..."
}
```
#### unshuffled_deduplicated_it
- **Size of downloaded dataset files:** 27.93 GB
- **Size of the generated dataset:** 74.09 GB
- **Total amount of disk used:** 102.03 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Jaundice - causes, treatment & pathology massaggio a osteochondrosis dellindizio di una controindicazione\\nTrattamento su un co..."
}
```
#### unshuffled_deduplicated_ja
- **Size of downloaded dataset files:** 40.80 GB
- **Size of the generated dataset:** 113.63 GB
- **Total amount of disk used:** 154.44 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"神社などへ一緒に同行して、様々な角度のショットで家族写真やお子様の写真を撮影致します！お好みに合わせて様々な写真を取ることができますので、その場でカメラマンへのリクエストも可能です！お子様の晴れ姿を、緊張していない自然な笑顔で残しませんか？\\n※七五三の..."
}
```
#### unshuffled_deduplicated_jbo
- **Size of downloaded dataset files:** 0.20 MB
- **Size of the generated dataset:** 0.70 MB
- **Total amount of disk used:** 0.91 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "ni'o 23 la cimast. cu 23moi djedi fi'o masti la cimast. noi ke'a cu cimoi masti .i 22 la cimast. cu purlamdei .ije 24 la cimast. cu bavlamdei"
}
```
#### unshuffled_deduplicated_jv
- **Size of downloaded dataset files:** 0.21 MB
- **Size of the generated dataset:** 0.62 MB
- **Total amount of disk used:** 0.82 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"José Mourinho (diwaca: [ʒuˈzɛ moˈɾiɲu]; lair ing Setubal, Portugal, 26 Januari 1963; umur 55 taun) iku salah siji pelatih bal k..."
}
```
#### unshuffled_deduplicated_ka
- **Size of downloaded dataset files:** 377.23 MB
- **Size of the generated dataset:** 1.99 GB
- **Total amount of disk used:** 2.36 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"წამიყვანე შენთან ერთად (ქართულად) / Возьми меня с собой (картулад) / (რუსული სერიალები ქართულად) (რუსების პორნო ონლაინში) (ruse..."
}
```
#### unshuffled_deduplicated_kk
- **Size of downloaded dataset files:** 389.12 MB
- **Size of the generated dataset:** 1.59 GB
- **Total amount of disk used:** 1.97 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Түлкібас ауданында «Латын негізді әліпби мен емле ережесі туралы насихат» жобасының тобы семинар өткізді\\nЕлорданың «Қазақстан»..."
}
```
#### unshuffled_deduplicated_km
- **Size of downloaded dataset files:** 114.48 MB
- **Size of the generated dataset:** 610.61 MB
- **Total amount of disk used:** 725.09 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ខ្សឹបដាក់ត្រចៀក៖ លោក សួស សុផានិត នាយផ្នែករដ្ឋបាលព្រៃឈើ ស្រុកភ្នំក្រវាញ់ ដែលទើបឡើងកាន់តំណែងថ្មី បើកដៃឲ្យឈ្នួញ ប្រព្រឹត្តបទល្មើស ..."
}
```
#### unshuffled_deduplicated_kn
- **Size of downloaded dataset files:** 215.52 MB
- **Size of the generated dataset:** 1.08 GB
- **Total amount of disk used:** 1.30 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ರಾಷ್ಟ್ರಪತಿ ಪ್ರಣಬ್ ಮುಖರ್ಜಿಯಿಂದ ಪದ್ಮ ಪ್ರಶಸ್ತಿ ಪ್ರದಾನ | President Pranab Mukherjee Confers Padma Awards | Photo Gallery on Kannada..."
}
```
#### unshuffled_deduplicated_ko
- **Size of downloaded dataset files:** 4.46 GB
- **Size of the generated dataset:** 12.00 GB
- **Total amount of disk used:** 16.47 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"CIA 프로젝트에서는 데이터베이스로 들어오는 요청을 중간에 수집(Sniffing)하고 수집한 데이터를 분석(Parsing)하여 그로 인한 결과를 판단하여 알릴 수 있는 시스템(Push Service)이 필요하다. 그리고 연구를 ..."
}
```
#### unshuffled_deduplicated_krc
- **Size of downloaded dataset files:** 0.62 MB
- **Size of the generated dataset:** 2.41 MB
- **Total amount of disk used:** 3.03 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Шамханланы, Бийлени къаршысына ябушуп, Батыр уланларыбызны къоллары булан «ортакъ ожакъ» къургъанбыз. Шо иш уллу зараллы иш бол..."
}
```
#### unshuffled_deduplicated_ku
- **Size of downloaded dataset files:** 23.34 MB
- **Size of the generated dataset:** 63.09 MB
- **Total amount of disk used:** 86.43 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Me di 114 bernameyên xwe yên berê da perçeyên ji berhemên zanyarî yên kurdzanên mezin bi wergera kurdî da ...\\nMe di 114 bernam..."
}
```
#### unshuffled_deduplicated_kv
- **Size of downloaded dataset files:** 0.33 MB
- **Size of the generated dataset:** 1.21 MB
- **Total amount of disk used:** 1.54 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Коми кытшыслӧн ыджытжык тор вӧр увтын куйлӧ, сійӧн и фаунасӧ татӧн аркмӧтӧны вӧрын олісь подаэз. Ассямаӧн лоӧ сія, мый кытшас с..."
}
```
#### unshuffled_deduplicated_kw
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.02 MB
- **Total amount of disk used:** 0.02 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼Pray without ceasing🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏..."
}
```
#### unshuffled_deduplicated_ky
- **Size of downloaded dataset files:** 106.22 MB
- **Size of the generated dataset:** 408.40 MB
- **Total amount of disk used:** 514.61 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Turmush: Бишкек шаардык кеңешинин кезексиз отурумунда мэрге ишенбөөчүлүк көрсөтүү маселеси каралат, - депутат Т.Сагынов\\nБишкек..."
}
```
#### unshuffled_deduplicated_la
- **Size of downloaded dataset files:** 3.42 MB
- **Size of the generated dataset:** 9.79 MB
- **Total amount of disk used:** 13.22 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Hæ sunt generationes Noë: Noë vir justus atque perfectus fuit in generationibus suis; cum Deo ambulavit.\\nEcce ego adducam aqua..."
}
```
#### unshuffled_deduplicated_lb
- **Size of downloaded dataset files:** 8.30 MB
- **Size of the generated dataset:** 21.42 MB
- **Total amount of disk used:** 29.72 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Während dem Gaardefestival \\\"Ambiance Jardins\\\" vum 15. bis de 17. Mee huet den SNJ nees zesumme mam Groupe Animateur en Inform..."
}
```
#### unshuffled_deduplicated_lez
- **Size of downloaded dataset files:** 0.77 MB
- **Size of the generated dataset:** 3.08 MB
- **Total amount of disk used:** 3.84 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Ахцегь хуьр, виридалай ч1ехи лезги хуьрерикая я. Ам Урусатдин виридалай къиблепатавай хуьрерикай я. Ин хуьр...\"..."
}
```
#### unshuffled_deduplicated_li
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.03 MB
- **Total amount of disk used:** 0.04 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"'t Good Goedenraad aan de Ezerbaek besjteit oet 'n kesjtièl mèt gesjlote haof en 'n park van 26 hectare. Hie in sjtoon väól beu..."
}
```
#### unshuffled_deduplicated_lmo
- **Size of downloaded dataset files:** 0.10 MB
- **Size of the generated dataset:** 0.46 MB
- **Total amount of disk used:** 0.57 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Serét (en tortonés: Sregh; en piemontés: Srèj) l'è 'n cümü italià, de la regiù del Piemónt, en Pruvìncia de Alessandria. El g'h..."
}
```
#### unshuffled_deduplicated_lo
- **Size of downloaded dataset files:** 23.63 MB
- **Size of the generated dataset:** 119.29 MB
- **Total amount of disk used:** 142.92 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"ຜູ້ພິພາກສາ ປະຈຳເຂດ ສຫລ ທ່ານນຶ່ງ ຕັດສິນວ່າ ໂຄງການເກັບກຳຂໍ້ມູນ ທາງໂທລະສັບ ຂອງອົງການ ຄວາມໝັ້ນຄົງແຫ່ງຊາດ ແມ່ນຖືກຕ້ອງ ຕາມກົດໝາຍ.\\nກະ..."
}
```
#### unshuffled_deduplicated_lrc
- **Size of downloaded dataset files:** 0.02 MB
- **Size of the generated dataset:** 0.06 MB
- **Total amount of disk used:** 0.08 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"آرلینگتون یئ گئل د شأریا ڤولاتچە ڤیرجینیا و یئ گئل د شأریا ڤولات ڤولاتچە یا یأکاگئرئتە ئمریکاە. ئی شأر دویومی کألوٙن شأر د راسا..."
}
```
#### unshuffled_deduplicated_lt
- **Size of downloaded dataset files:** 1.65 GB
- **Size of the generated dataset:** 4.20 GB
- **Total amount of disk used:** 5.86 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Čir vir vir pavasaris! Čia čia čia… dalinamės labai simpatiška video pamokėle, kurią pristato ab888art galerija.\\nBe galo papra..."
}
```
#### unshuffled_deduplicated_lv
- **Size of downloaded dataset files:** 710.45 MB
- **Size of the generated dataset:** 1.91 GB
- **Total amount of disk used:** 2.62 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Dekoratīvi sliekšņi MITSUBISHI OUTLANDER 2007, izgatavoti no ovālas formas, pulētas nerūsējošā tērauda caurules...\\ndažādas tūn..."
}
```
#### unshuffled_deduplicated_mai
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.01 MB
- **Total amount of disk used:** 0.01 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"१ · २ · ३ · ४ · ५ · ६ · ७ · ८ · ९ · १० · ११ · १२ · १३ · १४ · १५ · १६ · १७ · १८ · १९ · २० · २१ · २२ · २३ · २४ · २५ · २६ · २७ · २..."
}
```
#### unshuffled_deduplicated_mg
- **Size of downloaded dataset files:** 4.30 MB
- **Size of the generated dataset:** 13.59 MB
- **Total amount of disk used:** 17.89 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Nanamboatra taratasy apetaka sy soso-kevitra ho an'ny olona te-hanatevin-daharana ity fihetsiketsehana ity i Anocrena.\\nNosorat..."
}
```
#### unshuffled_deduplicated_mhr
- **Size of downloaded dataset files:** 1.63 MB
- **Size of the generated dataset:** 6.26 MB
- **Total amount of disk used:** 7.89 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Акрет жап годым Уганда кундемым Пигмей племена- влак айлен шогеныт. мемнан эран 1 курым гыч Банту племена влакат тиде кундемышк..."
}
```
#### unshuffled_deduplicated_min
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.31 MB
- **Total amount of disk used:** 0.33 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\" ..."
}
```
#### unshuffled_deduplicated_mk
- **Size of downloaded dataset files:** 303.12 MB
- **Size of the generated dataset:** 1.19 GB
- **Total amount of disk used:** 1.49 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"„Филм плус“ е насловен првиот филмски месечник во Македонија, чиј прв број ќе биде промовиран вечер во „Менада“. Новото македон..."
}
```
#### unshuffled_deduplicated_ml
- **Size of downloaded dataset files:** 496.80 MB
- **Size of the generated dataset:** 2.69 GB
- **Total amount of disk used:** 3.18 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"സ്ത്രീ പ്രവേശനം സര്ക്കാര് പൂര്ണമായും അംഗീകരിക്കുന്നുവെന്നും ശബരിമലയുടെ സുരക്ഷയില് ഇടപെടുമെന്നും സര്ക്കാര് ഹൈക്കോടതിയില്\\..."
}
```
#### unshuffled_deduplicated_mn
- **Size of downloaded dataset files:** 219.52 MB
- **Size of the generated dataset:** 883.46 MB
- **Total amount of disk used:** 1.10 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"МУБИС-ын багш мэргэжлийн хөрвөх сургалтыг төгссөн багшид багшлах эрх олгох тухай ~ БМДИ-ийн захирлын тушаал - Багшийн мэргэжил ..."
}
```
#### unshuffled_deduplicated_mr
- **Size of downloaded dataset files:** 299.68 MB
- **Size of the generated dataset:** 1.49 GB
- **Total amount of disk used:** 1.79 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Home / motivational marathi story / उद्योजकता (Entrepreneurship) / यांना हे जमलय, तर आपल्याला का नाही जमणार ?\\nयापैकी कोणाचीही ..."
}
```
#### unshuffled_deduplicated_mrj
- **Size of downloaded dataset files:** 0.29 MB
- **Size of the generated dataset:** 1.10 MB
- **Total amount of disk used:** 1.38 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Лӹпӹвлӓ (латинлӓ Lepidoptera ; алыкмарла лыве-влак) — капшангывлӓ йыхыш пырышы сӱмӓн нӹл шылдыран капшангывлӓ. Цилӓжӹ 180000 тӹ..."
}
```
#### unshuffled_deduplicated_ms
- **Size of downloaded dataset files:** 16.39 MB
- **Size of the generated dataset:** 49.45 MB
- **Total amount of disk used:** 65.85 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Sanad pertama daripada Zuhair bin Harb daripada ‘Affan daripada Hammad daripada Thabit daripada Anas.\\nSanad kedua daripada ‘Ab..."
}
```
#### unshuffled_deduplicated_mt
- **Size of downloaded dataset files:** 5.90 MB
- **Size of the generated dataset:** 17.68 MB
- **Total amount of disk used:** 23.58 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "tibgħat il-kawża lura lill-Qorti Ġenerali għall-annullament jew għat-tnaqqis tal-penalità imposta mill-Kummissjoni bid-deċiżjoni inizjali kif emendata bid-deċiżjoni ta’ rettifika;"
}
```
#### unshuffled_deduplicated_mwl
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Deciplina social i outónoma que angloba atebidades de ouserbaçon, de análeze, de çcriçon, cumparaçon, de sistematizaçon i de sp..."
}
```
#### unshuffled_deduplicated_my
- **Size of downloaded dataset files:** 207.14 MB
- **Size of the generated dataset:** 1.11 GB
- **Total amount of disk used:** 1.32 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ျမ၀တီ - ရန္ကုန္တိုင္းေဒသႀကီး ေျမာက္ဥကၠလာပႏွင္႕ ဗဟန္းၿမိဳ႔နယ္ မေကြးတိုင္း ေဒသႀကီး ပခုကၠဴၿမိဳ႔နယ္တို႔၌ ျမန္မာ႕တပ္မေတာ္အား ေထာက္ခံ..."
}
```
#### unshuffled_deduplicated_myv
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"2018 иень умарьковонь 6-це чистэ сась паро куля! Россиянь культурань Министерствась макссь невтемань конёв (прокатной удостовер..."
}
```
#### unshuffled_deduplicated_mzn
- **Size of downloaded dataset files:** 0.16 MB
- **Size of the generated dataset:** 0.63 MB
- **Total amount of disk used:** 0.79 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"قرآن یا قوران اسلام ِآسمونی کتاب هسته. مسلمونون گانّّه قرآن ره خدا، وحی جه برسنییه، «محمد معجزه» هسته و ثقلین حدیث دله ونه خَو..."
}
```
#### unshuffled_deduplicated_nah
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.01 MB
- **Total amount of disk used:** 0.01 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "In mācuīlpōhualxihuitl VI (inic chicuacē) in mācuīlpōhualli xiuhitl cāhuitl īhuīcpa 501 xihuitl oc 600 xihuitl."
}
```
#### unshuffled_deduplicated_nap
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.01 MB
- **Total amount of disk used:** 0.02 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ò AUDIT í Ç è î ÿ å å 30 ò ÿ ÿ é, õ ñ ì ÿ, ê ã- ò à ì. å â å í ç â à à é ñ è å é ó ó ë. å å å û è å î é è à. à è à AUDIT 1-7 â ..."
}
```
#### unshuffled_deduplicated_nds
- **Size of downloaded dataset files:** 5.27 MB
- **Size of the generated dataset:** 13.48 MB
- **Total amount of disk used:** 18.76 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Dor kann sik vun nu af an de hele plattdüütsche Welt – vun Niebüll bit New York, vun Helgoland bit Honolulu – drapen. Allens, w..."
}
```
#### unshuffled_deduplicated_ne
- **Size of downloaded dataset files:** 240.63 MB
- **Size of the generated dataset:** 1.24 GB
- **Total amount of disk used:** 1.48 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"बर्दिबास नगरपालिकाको तेस्रो नगर परिषदबाट पारित आ.व.२०७३।७४ को संशोधित र २०७४।७५ को प्रस्तावित नीति, कार्यक्रम तथा बजेट\\nअार्थिक..."
}
```
#### unshuffled_deduplicated_new
- **Size of downloaded dataset files:** 0.83 MB
- **Size of the generated dataset:** 4.26 MB
- **Total amount of disk used:** 5.09 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"थ्व शहरयागु अक्षांश ३४.७००१६४ उत्तर व देशान्तर ८६.३७६४६९ पश्चिम खः (34.700164° N 86.376469° W)। थ्व थासे ७२२६७३२ वर्ग मिटर (२.७..."
}
```
#### unshuffled_deduplicated_nl
- **Size of downloaded dataset files:** 15.73 GB
- **Size of the generated dataset:** 41.91 GB
- **Total amount of disk used:** 57.65 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Op vrijdag 31 augustus wordt het nieuwe studiejaar van de masteropleiding architectuur geopend met een dagexcursie naar Venlo.\\..."
}
```
#### unshuffled_deduplicated_nn
- **Size of downloaded dataset files:** 23.58 MB
- **Size of the generated dataset:** 58.32 MB
- **Total amount of disk used:** 81.90 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "Planomtale krav til innhald Bakgrunn: Spørsmål frå fleire kommunar om kva ein planomtale/planbeskrivelse bør innehalde Fylkeskommunen og fylkesmannen har i ein del saker reist motsegn på formelt grunnlag"
}
```
#### unshuffled_deduplicated_no
- **Size of downloaded dataset files:** 1.96 GB
- **Size of the generated dataset:** 5.11 GB
- **Total amount of disk used:** 7.07 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Ytterligere aktører i primærhelsetjenesten og andre NHS-virksomheter ble infisert, inkludert legekontor.Læreren vår er så attra..."
}
```
#### unshuffled_deduplicated_oc
- **Size of downloaded dataset files:** 1.34 MB
- **Size of the generated dataset:** 4.00 MB
- **Total amount of disk used:** 5.34 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": ".рф (rf, còdi punycode: .xn--p1ai)[1] es lo nom de domeni en rus per Russia. Foguèt activat lo 12 de mai de 2010. Lo còdi latin es .ru."
}
```
#### unshuffled_deduplicated_or
- **Size of downloaded dataset files:** 38.72 MB
- **Size of the generated dataset:** 197.63 MB
- **Total amount of disk used:** 236.36 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ଭୁବନେଶ୍ୱର, ୨୭/୧– (ଓଡ଼ିଆ ପୁଅ) ସିପିଆଇ ଜାତୀୟ ପରିଷଦର ଆହ୍ୱାନକ୍ରମେ ଗତକାଲି ଜାନୁୟାରୀ ୨୬ ସାଧାରଣତନ୍ତ୍ର ଦିବସକୁ ଦେଶ ବ୍ୟାପୀ ସମ୍ବିଧାନ ସୁରକ୍ଷା ..."
}
```
#### unshuffled_deduplicated_os
- **Size of downloaded dataset files:** 2.83 MB
- **Size of the generated dataset:** 11.00 MB
- **Total amount of disk used:** 13.83 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"1. Лæппу æмæ чызг казрæдзийы зæрдæмæ куы фæцæуынц æмæ, куы сфæнд кæнынц сæ цард баиу кæнын, уæд лæппу бар ракуры чызгæй, цæмæй ..."
}
```
#### unshuffled_deduplicated_pa
- **Size of downloaded dataset files:** 102.39 MB
- **Size of the generated dataset:** 483.04 MB
- **Total amount of disk used:** 585.42 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ਰਜਿ: ਨੰ: PB/JL-138/2018-20 ਜਿਲਦ 63, ਬਾਨੀ ਸੰਪਾਦਕ (ਸਵ:) ਡਾ: ਸਾਧੂ ਸਿੰਘ ਹਮਦਰਦ ਫ਼ੋਨ : 0181-2455961-62-63, 5032400, ਫੈਕਸ : 2455960, 2..."
}
```
#### unshuffled_deduplicated_pam
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Áku pu i Anak ning Aláya at ngeni ipákit kó kékayu ngan nûng makanánu lang susúlat détinang kulit a mágkas. Lauan ya ing tarátu..."
}
```
#### unshuffled_deduplicated_pl
- **Size of downloaded dataset files:** 20.19 GB
- **Size of the generated dataset:** 50.59 GB
- **Total amount of disk used:** 70.78 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"System informatyczny - Załącznik nr 1 do zarządzenia Wójta Gminy Podegrodzie Nr 530/2013 z dnia 27 maja 2013 r\\nSystem informat..."
}
```
#### unshuffled_deduplicated_pms
- **Size of downloaded dataset files:** 0.71 MB
- **Size of the generated dataset:** 2.00 MB
- **Total amount of disk used:** 2.72 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Louvigné-du-Désert a l'é na comun-a fransèisa ant la region aministrativa dla Brëtagna, ant ël dipartiment d'Ille-et-Vilaine. A..."
}
```
#### unshuffled_deduplicated_pnb
- **Size of downloaded dataset files:** 2.58 MB
- **Size of the generated dataset:** 9.44 MB
- **Total amount of disk used:** 12.02 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"ایہ فائل Wikimedia Commons توں اے تے دوجیاں ویونتاں تے وی ورتی جاےکدی اے۔ گل بات اس دے فائل گل بات صفہ تے تھلے دتی گئی۔\"..."
}
```
#### unshuffled_deduplicated_ps
- **Size of downloaded dataset files:** 71.83 MB
- **Size of the generated dataset:** 254.79 MB
- **Total amount of disk used:** 326.61 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Many people usually use the time period ‘business to business (B2B) advertising,’ however most of them do not know precisely wh..."
}
```
#### unshuffled_deduplicated_pt
- **Size of downloaded dataset files:** 26.00 GB
- **Size of the generated dataset:** 68.37 GB
- **Total amount of disk used:** 94.37 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Você pode estar lendo este texto no sofá, levantar pra pegar uma breja na geladeira, dar uma cagada e sentar novamente, sem int..."
}
```
#### unshuffled_deduplicated_qu
- **Size of downloaded dataset files:** 0.02 MB
- **Size of the generated dataset:** 0.07 MB
- **Total amount of disk used:** 0.09 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Warayu wichay (kastilla simipi: Ascensión de Guarayos) nisqaqa Buliwya mama llaqtapi, Santa Krus suyupi, huk llaqtam, Warayu pruwinsyap uma llaqtanmi."
}
```
#### unshuffled_deduplicated_rm
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.01 MB
- **Total amount of disk used:** 0.01 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"practicists agrars / practicistas agraras AFP pon far ina furmaziun da basa scursanida per cuntanscher in attestat federal da q..."
}
```
#### unshuffled_deduplicated_ro
- **Size of downloaded dataset files:** 4.48 GB
- **Size of the generated dataset:** 11.66 GB
- **Total amount of disk used:** 16.14 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"“În viață, oportunitatea nu este totul. Cine atrage Lumina, cineva bun în umbră. Timpul ne creează.” maestru\\nLyn.Evans: Ce mar..."
}
```
#### unshuffled_deduplicated_ru
- **Size of downloaded dataset files:** 166.68 GB
- **Size of the generated dataset:** 611.70 GB
- **Total amount of disk used:** 778.38 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Доступ к данному профилю для публичного просмотра закрыт администрацией сайта - профиль находится на модерации.\\nРазработчикам ..."
}
```
#### unshuffled_deduplicated_sa
- **Size of downloaded dataset files:** 7.27 MB
- **Size of the generated dataset:** 38.33 MB
- **Total amount of disk used:** 45.60 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"अनिरुद्धनगरे क्रीडिता रामलीला सम्प्रति समाप्ता अस्ति । तस्य कानिचन् चित्राणि पूर्वमेव प्रकाशितानि सन्ति । द्वौ चलचित्रौ अपि ..."
}
```
#### unshuffled_deduplicated_sah
- **Size of downloaded dataset files:** 7.01 MB
- **Size of the generated dataset:** 27.46 MB
- **Total amount of disk used:** 34.49 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████..."
}
```
#### unshuffled_deduplicated_scn
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "La gilusìa è nu sintimentu dulurusu ca nasci d'un disideriu di pussessu sclusivu ntê cunfrunti dâ pirsuna amata e dû timuri, dû suspettu o dâ cirtizza dâ sò nfidiltati."
}
```
#### unshuffled_deduplicated_sd
- **Size of downloaded dataset files:** 74.17 MB
- **Size of the generated dataset:** 275.48 MB
- **Total amount of disk used:** 349.66 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"هر ڪو ڄاڻي ٿو ته جڏهن توهان هڪ وڏي خريد ڪرڻ چاهيون ٿا, توهان پڄي ضروري حڪم ۾ ان جي ڪم ڪرڻ جي هٿ ۾ لاڳاپو ڪيو آهي. جي شيء آهي ته..."
}
```
#### unshuffled_deduplicated_sh
- **Size of downloaded dataset files:** 1.45 MB
- **Size of the generated dataset:** 6.44 MB
- **Total amount of disk used:** 7.87 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Opština Gornja Radgona se nalazi u sjeveroistočnoj Sloveniji i graniči s susjednom Austriji duž rijeke Mure. Sa tridesetim nase..."
}
```
#### unshuffled_deduplicated_si
- **Size of downloaded dataset files:** 175.62 MB
- **Size of the generated dataset:** 842.57 MB
- **Total amount of disk used:** 1.02 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"ලාංකීය සිතිවිලි සිංහල බ්ලොග් කියවනය කොත්තු සින්ඩිය ලංකා Blogger හත්මාළුව ලංකා බ්ලොග් කියවනය මාතලන්ගේ සින්ඩිය මොබයිල්lk\\nඅවකාශය ..."
}
```
#### unshuffled_deduplicated_sk
- **Size of downloaded dataset files:** 1.96 GB
- **Size of the generated dataset:** 4.80 GB
- **Total amount of disk used:** 6.76 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Aktivity | Agentúra podporovaného zamestnávania | vzdelávanie pre klientov, vzdelávanie pre odborníkov, kurzy\\nŠpecializované k..."
}
```
#### unshuffled_deduplicated_sl
- **Size of downloaded dataset files:** 523.22 MB
- **Size of the generated dataset:** 1.32 GB
- **Total amount of disk used:** 1.85 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Če Creatures, ki je želel, da pridejo na čas, predvsem je povedlo – razlikuje od ljubosumja začel grizenja kolen (ali zadnjica)..."
}
```
#### unshuffled_deduplicated_so
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.02 MB
- **Total amount of disk used:** 0.02 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"тттттттттттттттттттттттттттттттт тттттттттттттттттттттттттттттттт тттттттттттттттттттттттттттттттт ттттттттттттттттуууууууууууу..."
}
```
#### unshuffled_deduplicated_sq
- **Size of downloaded dataset files:** 445.36 MB
- **Size of the generated dataset:** 1.21 GB
- **Total amount of disk used:** 1.66 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Çfarë do të më pëlqente tek një femër ose çfarë do të më shndërronte në një shpërthim drite? – Albert Vataj\\nTë gjithëve një zo..."
}
```
#### unshuffled_deduplicated_sr
- **Size of downloaded dataset files:** 665.03 MB
- **Size of the generated dataset:** 2.36 GB
- **Total amount of disk used:** 3.03 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Корисни савети за сваки дан. На сајту су разне категорије, као што су љепота, мода, кување и поправка властитим рукама.\\nШколск..."
}
```
#### unshuffled_deduplicated_su
- **Size of downloaded dataset files:** 0.05 MB
- **Size of the generated dataset:** 0.16 MB
- **Total amount of disk used:** 0.21 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Kartu krédit nyaéta \"duit plastik\" anu dikaluarkeun ku bank pikeun alat pambayaran di tempat-tempat nu tangtu samisal jiga di hotél, réstoran, tempat rékréasi jeung sajabana.[1]"
}
```
#### unshuffled_deduplicated_sv
- **Size of downloaded dataset files:** 10.19 GB
- **Size of the generated dataset:** 26.33 GB
- **Total amount of disk used:** 36.51 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"1783 är ett viktigt årtal i den nya tidens historia. Det året slöts en fred i Paris och därmed blev de 13 brittiska kolonierna ..."
}
```
#### unshuffled_deduplicated_sw
- **Size of downloaded dataset files:** 2.95 MB
- **Size of the generated dataset:** 8.98 MB
- **Total amount of disk used:** 11.92 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Miripuko hiyo inakuja mwanzoni mwa Wiki Takatifu kuelekea Pasaka na ikiwa ni wiki chache tu kabla ya Papa Francis kuanza ziara yake katika nchi hiyo yenye idadi kubwa kabisa ya watu katika ulimwengu wa nchi za Kiarabu."
}
```
#### unshuffled_deduplicated_ta
- **Size of downloaded dataset files:** 971.12 MB
- **Size of the generated dataset:** 5.48 GB
- **Total amount of disk used:** 6.45 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"பொழுது சாய்ந்து வெகு நேரமாகிவிட்டது. கூலி வேலைக்குப் போயிருந்த 'சித்தாள் ' பெண்கள் எல்லோரும் வீடு திரும்பி விட்டார்கள். இன்னும்..."
}
```
#### unshuffled_deduplicated_te
- **Size of downloaded dataset files:** 342.43 MB
- **Size of the generated dataset:** 1.70 GB
- **Total amount of disk used:** 2.04 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"హర్యానాలో టోల్ దగ్గర సిబ్బంది.. స్థానిక ప్రజలు కొట్టుకున్నారు. కర్నాల్ అనే గ్రామానికి సమీపంలో టోల్ గేట్ ఉంది. అయితే సాధారణంగా స..."
}
```
#### unshuffled_deduplicated_tg
- **Size of downloaded dataset files:** 62.90 MB
- **Size of the generated dataset:** 261.68 MB
- **Total amount of disk used:** 324.60 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Ҳумайро гуфтааст, мухолифи низом аст, низоме, ки дар Тоҷикистон вуҷуд дорад. Ба ин маънӣ, худро мухолифи давлату ҳукумати Тоҷик..."
}
```
#### unshuffled_deduplicated_th
- **Size of downloaded dataset files:** 3.54 GB
- **Size of the generated dataset:** 17.11 GB
- **Total amount of disk used:** 20.65 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ฟันที่แลดูขาวสะอาดไม่มีเศษอาหารติดอยู่ เหงือกสีชมพู ไม่เจ็บ หรือมีเลือดออกเวลาแปรงฟันหรือขัดฟัน ไม่มีปัญหาเรื่องกลิ่นปาก ทำให้ก..."
}
```
#### unshuffled_deduplicated_tk
- **Size of downloaded dataset files:** 2.22 MB
- **Size of the generated dataset:** 7.12 MB
- **Total amount of disk used:** 9.34 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Türkmenistanyň Prezidenti agyr atletika boýunça dünýä çempionatyna taýýarlyk işleriniň barşy bilen tanyşdy\\nHalallykdan kemal t..."
}
```
#### unshuffled_deduplicated_tl
- **Size of downloaded dataset files:** 151.34 MB
- **Size of the generated dataset:** 431.69 MB
- **Total amount of disk used:** 583.04 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"“Gusto ko manawagan sa mga Unit Head ng Chanel 2 Salve. Kasi napapansin ko iyon mga alaga ko ang taping halos once a week lang,..."
}
```
#### unshuffled_deduplicated_tr
- **Size of downloaded dataset files:** 10.39 GB
- **Size of the generated dataset:** 28.47 GB
- **Total amount of disk used:** 38.86 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Son yıllarda görülen ay tutulmalarına göre daha etkili olacağı söylenen Kanlı veya Kırmızı Ay Tutulmasına saatler kaldı. Bu akş..."
}
```
#### unshuffled_deduplicated_tt
- **Size of downloaded dataset files:** 85.89 MB
- **Size of the generated dataset:** 321.37 MB
- **Total amount of disk used:** 407.26 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"\\\"Иремнең вафатына 40 көн узгач, Алмаз да безнең өйгә кереп үлде\\\". Арчада 35 яшьлек ир өстенә кондызлар ега башлаган агач төшк..."
}
```
#### unshuffled_deduplicated_tyv
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.01 MB
- **Total amount of disk used:** 0.01 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Экии, хүндүлуг аалчылар болгаш тыва дылдың деткикчилери! Тыва дылдың болгаш чогаалдың ховар бир башкызынга, Менги Ооржакка, ажы..."
}
```
#### unshuffled_deduplicated_ug
- **Size of downloaded dataset files:** 20.53 MB
- **Size of the generated dataset:** 86.44 MB
- **Total amount of disk used:** 106.97 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"زاڭ-ءتۇزىم | عىلىم-تەحنيكا | ءتىل-ادەبيەت | تۇرمىس | دەنە تاربيە | ساياحات-ورتا | سۋرەتتى حابار | سىر سۇحبات | ارناۋلى تاقىرىپ ..."
}
```
#### unshuffled_deduplicated_uk
- **Size of downloaded dataset files:** 8.04 GB
- **Size of the generated dataset:** 29.86 GB
- **Total amount of disk used:** 37.90 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Про надання роз'яснення (щодо форми письмового зобов'язання громадян про зворотне ввезення/вивезення товарів), Державна митна с..."
}
```
#### unshuffled_deduplicated_ur
- **Size of downloaded dataset files:** 483.59 MB
- **Size of the generated dataset:** 1.82 GB
- **Total amount of disk used:** 2.31 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"آئیے اہم اسلامی کتب کو یونیکوڈ میں انٹرنیٹ پر پیش کرنے کے لئے مل جل کر آن لائن ٹائپنگ کریں۔ محدث ٹائپنگ پراجیکٹ کے ذریعے آپ روز..."
}
```
#### unshuffled_deduplicated_uz
- **Size of downloaded dataset files:** 4.30 MB
- **Size of the generated dataset:** 12.00 MB
- **Total amount of disk used:** 16.29 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Qurama tog'lari tizmasining Toshkentdan 154 km uzoqlikdagi Toshkent-Ush yo'li yeqasidaxushmanzara tabiat qo'ynida joylashgan maydoni 30 ga.\nBolalarni sog'lomlashtirish oromgohi Bo'stonliq tumani Oqtosh muntaqasining soy-salqin gushasida joylashgan."
}
```
#### unshuffled_deduplicated_vec
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.02 MB
- **Total amount of disk used:** 0.02 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Par ogni pónto, ła derivada ła xe ła pendensa de ła reta tangente a ła curva de ła funsion f. Ła reta de cołor róso l'è senpre ..."
}
```
#### unshuffled_deduplicated_vi
- **Size of downloaded dataset files:** 10.71 GB
- **Size of the generated dataset:** 33.60 GB
- **Total amount of disk used:** 44.31 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Canh chua cá bông lau không chỉ là món ăn giải nhiệt, thanh mát ngày hè mà còn là món siêu bổ dưỡng, rất tốt cho người gầy ốm. ..."
}
```
#### unshuffled_deduplicated_vo
- **Size of downloaded dataset files:** 0.30 MB
- **Size of the generated dataset:** 2.10 MB
- **Total amount of disk used:** 2.40 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Sarniguet binon zif in ziläk: Hautes-Pyrénées, in topäd: Midi-Pyrénées, in Fransän. Sarniguet topon videtü 43°19’ 7’’ N e lunetü 0°5’ 19’’ L."
}
```
#### unshuffled_deduplicated_wa
- **Size of downloaded dataset files:** 0.08 MB
- **Size of the generated dataset:** 0.22 MB
- **Total amount of disk used:** 0.29 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Cisse pådje ci n' est co k' on djermon, dj' ô bén k' el pådje est djusse sibåtcheye, eyet co trop tene; et s' divreut ele ecråxhî ene miete."
}
```
#### unshuffled_deduplicated_war
- **Size of downloaded dataset files:** 0.55 MB
- **Size of the generated dataset:** 2.36 MB
- **Total amount of disk used:** 2.90 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "An Honce amo in usa ka baryo ngan munisipalidad ha distrito han Rožňava ha rehiyon han Košice ha nasod han Slovakia.\nAn Rumegies amo in usa ka komyun ha departamento han Nord ngan ha rehiyon han Nord-Pas-de-Calais ha nasod han Fransya."
}
```
#### unshuffled_deduplicated_wuu
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.03 MB
- **Total amount of disk used:** 0.04 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"伊春元旦天气 伊春腊八天气 伊春春节天气 伊春情人节天气 伊春元宵节天气 伊春愚人节天气 伊春清明节天气 伊春劳动节天气 伊春母亲节天气 伊春端午节天气 伊春七夕节天气 伊春教师节天气 伊春中秋节天气 伊春国庆节天气 伊春重阳节天气 伊春万圣节天气 伊春..."
}
```
#### unshuffled_deduplicated_xal
- **Size of downloaded dataset files:** 0.03 MB
- **Size of the generated dataset:** 0.12 MB
- **Total amount of disk used:** 0.15 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Арнгудин Орн гисн Европд бәәдг һазр. 2007 җилин тooһaр эн орн нутгт 3,600,523 әмтн бәәдг билә. Арнгудин Орнин хотл балһсна нерн..."
}
```
#### unshuffled_deduplicated_xmf
- **Size of downloaded dataset files:** 0.94 MB
- **Size of the generated dataset:** 4.63 MB
- **Total amount of disk used:** 5.58 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"მოჩამილი ტექსტი წჷმორინელი რე Creative Commons Attribution-ShareAlike ლიცენზიათ; შილებე გეძინელი პირობეფიშ არსებუა. კილიშკილიშა..."
}
```
#### unshuffled_deduplicated_yi
- **Size of downloaded dataset files:** 22.20 MB
- **Size of the generated dataset:** 88.29 MB
- **Total amount of disk used:** 110.49 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ממשותדיק - חבֿרה, איך אַרבעט איצט אױף אַ זשורנאַל. טאָמער איר האָט עפּעס צוצוגעבן זאָלט איר שיקן מיר אַן אָנזאָג. ס'װעט הײסן \\\"..."
}
```
#### unshuffled_deduplicated_yo
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.03 MB
- **Total amount of disk used:** 0.04 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Copyright © 2018 BBC. BBC kò mọ̀ nípa àwọn ohun tí ó wà ní àwọn ojú òpó tí ó wà ní ìta. Ọwọ́ tí a fi mú ìbáṣepọ̀ ti ìta.\"..."
}
```
#### unshuffled_deduplicated_yue
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 你還不爆 我累了 投降輸一半可以嗎\"..."
}
```
#### unshuffled_deduplicated_zh
- **Size of downloaded dataset files:** 99.98 GB
- **Size of the generated dataset:** 267.88 GB
- **Total amount of disk used:** 367.86 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"中国铝灰网 中国有色金属矿产网 中国黄莲网 中国水轮发电机网 中国抽油泵网 中国数控雕刻机网 中国不锈钢抛光网 中国磨具加工网 中国压铸铝网 中国耐水腻子网 中国手机摄像头网 中国粗粮网 中国车门锁网 中国钛粉网 中国轮圈网\\n天天中奖彩票图 天天中彩票..."
}
```
</details>
<details>
<summary>Click to expand the Data/size information for each language (original)</summary>
#### unshuffled_original_af
- **Size of downloaded dataset files:** 85.79 MB
- **Size of the generated dataset:** 254.08 MB
- **Total amount of disk used:** 339.87 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "aanlyn markte as gevolg van ons voortgesette 'n begrip opsie handel sakeplan pdf terwyl ons steeds die gereelde ons binêre opsies handel"
}
```
#### unshuffled_original_als
- **Size of downloaded dataset files:** 1.49 MB
- **Size of the generated dataset:** 5.30 MB
- **Total amount of disk used:** 6.78 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"De Nazionalpark hät e Flächi vo 170,3 km² und isch dodemit s grösti Naturschutzgebiet vo de Schwiz. Er ligt uf em Gebiet vo de ..."
}
```
#### unshuffled_original_am
- **Size of downloaded dataset files:** 102.79 MB
- **Size of the generated dataset:** 378.06 MB
- **Total amount of disk used:** 480.85 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"አየር መንገዱ ከአዲስ አበባ ወደ ሮም ጣሊያን በማምራት ላይ በነበረበት ጊዜ ረዳት አብራሪው የጉዞውን አቅጣጫ በመቀየር ጄኔቭ አውሮፓላን ማረፊያ በማሳረፍ እጁን ለፖሊስ ሰጥቷል።\\nየኢትዮጵያ መንግስት የ..."
}
```
#### unshuffled_original_an
- **Size of downloaded dataset files:** 0.15 MB
- **Size of the generated dataset:** 1.33 MB
- **Total amount of disk used:** 1.48 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"واااااااأسفاه الأمم تفتخر ب 0 أمي ووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووووو..."
}
```
#### unshuffled_original_ar
- **Size of downloaded dataset files:** 22.23 GB
- **Size of the generated dataset:** 87.94 GB
- **Total amount of disk used:** 110.17 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"مرحبا بك عزيز الزائر نتمنى لك أوقاتاً سعيدة معنا وأن نزداد شرفا بخدمتك ولا تنسى التسجيل معنا لتستفيد بكل جديد\\nأهلا وسهلا بك زا..."
}
```
#### unshuffled_original_arz
- **Size of downloaded dataset files:** 15.90 MB
- **Size of the generated dataset:** 70.13 MB
- **Total amount of disk used:** 86.03 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"بنى عجل : قبيلة من عجل بن لجيم بن صعب بن على بن بكر بن وائل انتقل اغلبهم الى البصرة فى العراق و اصفهان و خراسان فى ايران و اذرب..."
}
```
#### unshuffled_original_as
- **Size of downloaded dataset files:** 21.43 MB
- **Size of the generated dataset:** 117.73 MB
- **Total amount of disk used:** 139.17 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"আমি, এই সংগঠনৰ সদস্য সকলে একেলগ হৈ অসমকে ধৰি ভাৰতৰ উত্তৰ পূৰ্বাঞ্চলৰ অমূল্য কলা-সাংস্কৃতিক সম্পদৰাজি বৃহত্তৰ অষ্ট্ৰেলিয়াৰ সন্মু..."
}
```
#### unshuffled_original_ast
- **Size of downloaded dataset files:** 0.92 MB
- **Size of the generated dataset:** 2.54 MB
- **Total amount of disk used:** 3.46 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"The Killers llanzaron el so álbum debú, Hot Fuss, en xunu de 2004 nel Reinu Xuníu, al traviés de la discográfica Lizard King, y..."
}
```
#### unshuffled_original_av
- **Size of downloaded dataset files:** 0.08 MB
- **Size of the generated dataset:** 0.42 MB
- **Total amount of disk used:** 0.50 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Жинда малъараб ва божизе бегьулеб рагІудаса кьуризе бегьуларо гьев. Гьес насихІат гьабизе кколелъул бацІцІадаб диналъул рахъалъ..."
}
```
#### unshuffled_original_az
- **Size of downloaded dataset files:** 927.76 MB
- **Size of the generated dataset:** 2.96 GB
- **Total amount of disk used:** 3.89 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"AZTV-Artıq 7 ildir ki, Abşeron rayonu dotasiya almadan bütün xərclərini yerli daxilolmalar hesabına maliyyələşdirir.\\nDünən, 10..."
}
```
#### unshuffled_original_azb
- **Size of downloaded dataset files:** 6.64 MB
- **Size of the generated dataset:** 28.47 MB
- **Total amount of disk used:** 35.11 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"لعلی ١٣-جو عصرده یاشاییب یاراتمیش گؤرکملی آذربایجان شاعرلریندندیر. ١٢٢٤-جی ایلده تبریزده آنادان اولموشدور، گنج یاشلاریندا تیجار..."
}
```
#### unshuffled_original_ba
- **Size of downloaded dataset files:** 33.22 MB
- **Size of the generated dataset:** 133.70 MB
- **Total amount of disk used:** 166.92 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Күҙәтеү ҡуласаһы моделен хәҙер Мифтахетдин Аҡмулла исемендәге Башҡорт дәүләт педагогия университетында ла эшләргә мөмкин\\t\\nКүҙ..."
}
```
#### unshuffled_original_bar
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": " vo"
}
```
#### unshuffled_original_bcl
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"& ÿ ó / í 0 - ø û ù ö ú ð ï ú \\u0014 ù þ ô ö í ÷ ò \\u0014 ÷ í ù û ö í \\u0001 û ñ ç þ \\u0001 ð \\u0007 þ ò ñ ñ ò ô \\u0017 û ö ô ÷..."
}
```
#### unshuffled_original_be
- **Size of downloaded dataset files:** 498.29 MB
- **Size of the generated dataset:** 1.88 GB
- **Total amount of disk used:** 2.38 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Брэсцкія ўлады не дазволілі прафсаюзу РЭП правесці пікетаванне ў парку Воінаў-інтэрнацыяналістаў 30 мая 2018 года.\\nСітуацыю пр..."
}
```
#### unshuffled_original_bg
- **Size of downloaded dataset files:** 8.34 GB
- **Size of the generated dataset:** 33.75 GB
- **Total amount of disk used:** 42.09 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ЖАЛБОПОДАТЕЛЯТ директор на Дирекция „ Обжалване и данъчно-осигурителна практика“- Бургас, редовно призован, се представлява от ..."
}
```
#### unshuffled_original_bh
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.12 MB
- **Total amount of disk used:** 0.13 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"सुकमा जिला भारत के छत्तीसगढ़ राज्य में एगो जिला बाटे। एकर मुख्यालय सुकमा शहर बाटे। एकर कुल रकबा 5636 वर्ग कि॰मी॰ बाटे।\"..."
}
```
#### unshuffled_original_bn
- **Size of downloaded dataset files:** 2.14 GB
- **Size of the generated dataset:** 10.77 GB
- **Total amount of disk used:** 12.91 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ভড়ং সর্বস্ব বাংলা আর্ট অ্যান্ড কালচারের হিসাব গুলিয়ে দেওয়ার ম্যাজিকের নাম ব্রাত্য রাইসু November 23, 2017\\nভড়ং সর্বস্ব বাংলা আর..."
}
```
#### unshuffled_original_bo
- **Size of downloaded dataset files:** 28.94 MB
- **Size of the generated dataset:** 195.40 MB
- **Total amount of disk used:** 224.34 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"བོད་མི་འདི་དག་ནི་རང་རྒྱུད་སྒོ་རུ་ཕུད་དེ་གཞན་རྒྱུད་པང་དུ་ཉར་ནས་གསོ་སྐྱོང་བྱེད་དགོས་ཟེར་བ་དང་གཅིག་མཚུངས་རེད།\\nཚན་རིག་ནི་དང་ཐོག་རང..."
}
```
#### unshuffled_original_bpy
- **Size of downloaded dataset files:** 0.34 MB
- **Size of the generated dataset:** 4.35 MB
- **Total amount of disk used:** 4.69 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"পৌরসভা এহার আয়তন (লয়াহান) ২,৭৩০,.৬৩ বর্গ কিলোমিটার। পৌরসভা এহার মাপাহানর অক্ষাংশ বারো দ্রাঘিমাংশ ইলতাই 18.63° S 48.18° W ।[১]..."
}
```
#### unshuffled_original_br
- **Size of downloaded dataset files:** 9.18 MB
- **Size of the generated dataset:** 30.20 MB
- **Total amount of disk used:** 39.38 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Ar mank Magalhães(Daveoù a vank) a zo ur spesad evned, Spheniscus magellanicus an anv skiantel anezhañ.\\nGallout a reer implijo..."
}
```
#### unshuffled_original_bs
- **Size of downloaded dataset files:** 0.05 MB
- **Size of the generated dataset:** 0.48 MB
- **Total amount of disk used:** 0.53 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ž šř é ú šř šř ě šř ž é č ě ž ů ě ď éé ýš ě ě Ž č š ý ě ď é ýš ě ď ě éé ýš ě č ž ě š ý ď ě ýš é ú č ž č š ý ď ý ž é éě ď é č ýš..."
}
```
#### unshuffled_original_bxr
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.01 MB
- **Total amount of disk used:** 0.02 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"2002 оной хабар буряад хэлэ бэшэгэй һалбари Үндэһэтэнэй хүмүүнлиг ухаанай дээдэ һургуули болгогдожо өөршэлэгдөө.\\nХарин мүнөө б..."
}
```
#### unshuffled_original_ca
- **Size of downloaded dataset files:** 3.10 GB
- **Size of the generated dataset:** 8.62 GB
- **Total amount of disk used:** 11.73 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Daniel Vendrell, conegut com Vandrell, ha sigut un dels il•lustradors contemporanis més influents, representant a la nova onada..."
}
```
#### unshuffled_original_cbk
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano yo gano..."
}
```
#### unshuffled_original_ce
- **Size of downloaded dataset files:** 2.09 MB
- **Size of the generated dataset:** 8.73 MB
- **Total amount of disk used:** 10.82 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Шаьш анархисташ ду бохучу жигархойн дIахьедарехь дуьйцу, оьрсийн ницкъаллийн структурийн а, федералан каналан а Iалашонаш \\\"мар..."
}
```
#### unshuffled_original_ceb
- **Size of downloaded dataset files:** 11.07 MB
- **Size of the generated dataset:** 40.97 MB
- **Total amount of disk used:** 52.03 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Si Isko walay pupamilok nga nagtan-aw sa unahan, natugaw. “Naunsa ka gud diha Isko nga layo man kaayo ang imong panan-aw?” ni I..."
}
```
#### unshuffled_original_ckb
- **Size of downloaded dataset files:** 111.88 MB
- **Size of the generated dataset:** 510.97 MB
- **Total amount of disk used:** 622.85 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"رسی رۆژ - ساڵێک دوای بومەلەرزەی کرماشان میوانی بەرنامە : کاک سیاوەش حەیاتی چالاکی مەدەنی -قەسری شیرین\\nپارچە موزیک 30 / 10 / 20..."
}
```
#### unshuffled_original_cs
- **Size of downloaded dataset files:** 21.72 GB
- **Size of the generated dataset:** 57.08 GB
- **Total amount of disk used:** 78.80 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Akce anarchistů proti připravovanému novému služební řádu a nízkým mzdám 1903 – Historie českého anarchismu (1880 – 1939)\\nRost..."
}
```
#### unshuffled_original_cv
- **Size of downloaded dataset files:** 9.40 MB
- **Size of the generated dataset:** 41.05 MB
- **Total amount of disk used:** 50.45 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Шыранӑ чухне ӑнсӑртран латин кирилл саспаллисем вырӑнне латин саспаллисене ҫырсан, сайт эсир ҫырнине юсама тӑрӑшӗ.\\nКу сайтра ч..."
}
```
#### unshuffled_original_cy
- **Size of downloaded dataset files:** 81.74 MB
- **Size of the generated dataset:** 224.93 MB
- **Total amount of disk used:** 306.67 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Mae capeli Cymreig yr Andes ym Mhatagonia wedi cyhoeddi na fydd gwasanaethau yno weddill y mis, oherwydd yr eira trwm sydd wedi..."
}
```
#### unshuffled_original_da
- **Size of downloaded dataset files:** 6.00 GB
- **Size of the generated dataset:** 16.76 GB
- **Total amount of disk used:** 22.76 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Den 2.-5. februar 2016 løb det tredje kursus i uddannelsen af 4kommunesamarbejdets Local Impact Coaches, af stablen i Gentofte ..."
}
```
#### unshuffled_original_de
- **Size of downloaded dataset files:** 119.51 GB
- **Size of the generated dataset:** 331.22 GB
- **Total amount of disk used:** 450.73 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Auf dieser Seite gibt es mind. ein YouTube Video. Cookies für diese Website wurden abgelehnt. Dadurch können keine YouTube Vide..."
}
```
#### unshuffled_original_diq
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "Zıwanê Slawki, zıwano merdumanê Slawano. Zıwanê Slawki yew lızgeyê Zıwananê Hind u Ewropao. Keyeyê Zıwananê Slawki beno hirê letey:"
}
```
#### unshuffled_original_dsb
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.01 MB
- **Total amount of disk used:** 0.02 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Pśiklaskaju južo pśed pśedstajenim... 1500 źiśi njamóžo wěcej docakaś, měsćańska hala w Chóśebuzu - wupśedana."
}
```
#### unshuffled_original_dv
- **Size of downloaded dataset files:** 24.91 MB
- **Size of the generated dataset:** 131.63 MB
- **Total amount of disk used:** 156.54 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ބ. އަތޮޅުގައި ހުޅުވަން ތައްޔާރުވަމުން އަންނަ ވައްކަރު ރިސޯޓުގައި ވަޒީފާ އަދާކުރަން ޝައުގުވެރިވާ ފަރާތްތަކަށް ކުރިމަތިލުމުގެ ފުރ..."
}
```
#### unshuffled_original_el
- **Size of downloaded dataset files:** 17.31 GB
- **Size of the generated dataset:** 66.27 GB
- **Total amount of disk used:** 83.58 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Νεκρός εντοπίστηκε μέσα στο σπίτι του στην οδό Ηρώδου Αττικού στον αριθμό 7 ο επικεφαλής του προξενικού τμήματος της Ρωσικής πρ..."
}
```
#### unshuffled_original_eml
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.02 MB
- **Total amount of disk used:** 0.03 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"A séguit dal prucès ad rubutiśasiòṅ di abitànt dal pòpul ad Mikenes, Angoras 'l è finî dènt'r a 'n robot cun la tèsta dna rana ..."
}
```
#### unshuffled_original_en
- **Size of downloaded dataset files:** 903.83 GB
- **Size of the generated dataset:** 2525.44 GB
- **Total amount of disk used:** 3429.27 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Mtendere Village was inspired by the vision of Chief Napoleon Dzombe, which he shared with John Blanchard during his first visi..."
}
```
#### unshuffled_original_eo
- **Size of downloaded dataset files:** 117.07 MB
- **Size of the generated dataset:** 314.18 MB
- **Total amount of disk used:** 431.27 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Ĉu ... preĝi | mediti | ricevi instigojn || kanti | muziki || informiĝi | legi | studi || prepari Diservon\\nTemas pri kolekto d..."
}
```
#### unshuffled_original_es
- **Size of downloaded dataset files:** 106.04 GB
- **Size of the generated dataset:** 298.49 GB
- **Total amount of disk used:** 404.53 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Como se librará de la celulitis en el gimnasio La piel superflua en las manos después del adelgazamiento, Los bailes fáciles pa..."
}
```
#### unshuffled_original_et
- **Size of downloaded dataset files:** 1.88 GB
- **Size of the generated dataset:** 5.17 GB
- **Total amount of disk used:** 7.06 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"MTÜ AB Video järgib oma tegevuses kodanikuühenduste eetilise tegevuse üldtunnustatud põhimõtteid, mis on lühidalt kokkuvõetud 7..."
}
```
#### unshuffled_original_eu
- **Size of downloaded dataset files:** 248.19 MB
- **Size of the generated dataset:** 894.83 MB
- **Total amount of disk used:** 1.14 GB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "Gure jarduerek eraikuntzarekin, elkarbizitzarekin, hirigintzarekin eta ekologiarekin dute harremana, baita ideia eta konponbideak irudikatu eta garatzearekin ere, eraikuntza sektorea hobetuz, pertsonen erosotasuna eta bizi-kalitatea hobetzeko."
}
```
#### unshuffled_original_fa
- **Size of downloaded dataset files:** 20.96 GB
- **Size of the generated dataset:** 84.21 GB
- **Total amount of disk used:** 105.17 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"قـــــــــــــــــرار بود با هم کنـــــــــــــار بیایم نه اینکه از کنــــــــــــار هم رد بشیم...!!!\\nاگر روزی دلت لبریز غم بو..."
}
```
#### unshuffled_original_fi
- **Size of downloaded dataset files:** 9.97 GB
- **Size of the generated dataset:** 28.57 GB
- **Total amount of disk used:** 38.54 GB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Kiitos Deelle kaikesta - 1,5 viikkoa kulunut, kun Dee ei ole enää ollut omani. Reilu viikko sitten sunnuntaina vein Deen uuteen kotiinsa. Itselläni on ollut niin ristiriitaiset t..."
}
```
#### unshuffled_original_fr
- **Size of downloaded dataset files:** 105.32 GB
- **Size of the generated dataset:** 303.19 GB
- **Total amount of disk used:** 408.51 GB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "Média de débat d'idées, de culture et de littérature. Récits, décryptages, analyses, portraits et critiques autour de la vie des idées. Magazine engagé, ouvert aux autres et au monde.. Bring up to date in french"
}
```
#### unshuffled_original_frr
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Hiragana’ Practice’Sheet’1’(A -O)’ ’ Name:’________ __________________________’Section:’_______________ _’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ..."
}
```
#### unshuffled_original_fy
- **Size of downloaded dataset files:** 12.40 MB
- **Size of the generated dataset:** 36.24 MB
- **Total amount of disk used:** 48.64 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Nim in sêfte ride op Holmsjön, yn ien fan 'e lytse marren yn de omkriten, of nim se op avontueren lykas nonresidential. lâns Indalsälven wetter. Holm Sportklubb hawwe kano 's te huur, yn gearwurking mei de Baltyske Power konferinsje."
}
```
#### unshuffled_original_ga
- **Size of downloaded dataset files:** 29.27 MB
- **Size of the generated dataset:** 92.37 MB
- **Total amount of disk used:** 121.63 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Is fóram é seo chun plé a dhéanamh ar an leabhar atá roghnaithe do mhí na Samhna 2013 amháin. Ní féidir ach le baill chláraithe..."
}
```
#### unshuffled_original_gd
- **Size of downloaded dataset files:** 0.52 MB
- **Size of the generated dataset:** 2.02 MB
- **Total amount of disk used:** 2.55 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "Zhou Yujun, a 'phàrtaidh Rùnaire Comataidh Sgìre Yanfeng ann Hengyang bhaile agus a Sgìre pàrtaidh agus an riaghaltas a' bhuidheann-riochdachaidh a 'tighinn a chèilidh air ar companaidh air Apr. 14, 2017."
}
```
#### unshuffled_original_gl
- **Size of downloaded dataset files:** 235.38 MB
- **Size of the generated dataset:** 656.48 MB
- **Total amount of disk used:** 891.87 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"O persoal de Inditex da provincia de Pontevedra segue a reclamar iguais condicións laborais no conxunto do país - CIG: Confeder..."
}
```
#### unshuffled_original_gn
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.04 MB
- **Total amount of disk used:** 0.05 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"º ÑÆÚÓ À Ã Ð É Æ ¾ ÄÂ Î À ¼ Æ É ÄÛ = Ü Ý\\\"Þ ßà á â ã ä å æçè ã é ê â å àë ì æê íî é á ë ï í çì àð í Ü à ñ ê é ò ä ì\"..."
}
```
#### unshuffled_original_gom
- **Size of downloaded dataset files:** 0.44 MB
- **Size of the generated dataset:** 2.25 MB
- **Total amount of disk used:** 2.71 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"दुष्ट शीळ हें कौरवांचें । रामें सविस्तर देखूनि साचें । बोलिले वचनें जें दुर्वाचे । करी तयांचें अनुस्मरण ॥२२०॥\"..."
}
```
#### unshuffled_original_gu
- **Size of downloaded dataset files:** 232.02 MB
- **Size of the generated dataset:** 1.09 GB
- **Total amount of disk used:** 1.33 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"અધિક માસ ચાલે છે. સમગ્ર ભારતમાં અને તેમાંય ખાસ કરીને પવિત્ર કે ધાર્મિક કહેવાય છે તેવા સ્થાનક પર કથાનો દોર ચાલે છે. ઉનાળાની કાળઝ..."
}
```
#### unshuffled_original_he
- **Size of downloaded dataset files:** 5.66 GB
- **Size of the generated dataset:** 21.11 GB
- **Total amount of disk used:** 26.77 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"זקוקים לרשתות נגד יתושים? מחפשים רשת מתאימה לחלון צר וקטן? רשתות נגד יתושים אקורדיון של חברת קליר-מש הן הפתרון.\\nרשתות לחלונות ..."
}
```
#### unshuffled_original_hi
- **Size of downloaded dataset files:** 3.66 GB
- **Size of the generated dataset:** 17.93 GB
- **Total amount of disk used:** 21.59 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"'आइटम गर्ल' बनकर हिट हुई थीं राखी सावंत, आज करीना-कटरीना तक फॉलो कर रही हैं ट्रेंड नक्सलियों का दम निकालेगा बाइक ग्रेनेड लॉन्च..."
}
```
#### unshuffled_original_hr
- **Size of downloaded dataset files:** 79.42 MB
- **Size of the generated dataset:** 243.83 MB
- **Total amount of disk used:** 323.24 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"U raspravi je sudjelovao i HSS-ov saborski zastupnik rekavši kako poljoprivrednici ne osjete mjere o kojima ministar govori jer..."
}
```
#### unshuffled_original_hsb
- **Size of downloaded dataset files:** 1.39 MB
- **Size of the generated dataset:** 4.49 MB
- **Total amount of disk used:** 5.87 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Budyšin (SN/BŠe). Elektronikarjo mějachu lětsa cyle hinaši zazběh do swojeho wukubłanja. Wokrjesne rjemjeslnistwo bě mjenujcy w..."
}
```
#### unshuffled_original_ht
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan..."
}
```
#### unshuffled_original_hu
- **Size of downloaded dataset files:** 15.69 GB
- **Size of the generated dataset:** 43.07 GB
- **Total amount of disk used:** 58.77 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"monster - Amatőr, házi szex videók és kezdő csjaok pornó filmjei. - Free amateur, home made sex videos and online porn movies. ..."
}
```
#### unshuffled_original_hy
- **Size of downloaded dataset files:** 897.36 MB
- **Size of the generated dataset:** 3.94 GB
- **Total amount of disk used:** 4.84 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Արցախի Հանրապետության հռչակման 26-րդ տարեդարձի կապակցությամբ Շուշիի Արվեստի կենտրոնում կազմակերպվել է մոսկվաբնակ նկարիչներ՝ հայ..."
}
```
#### unshuffled_original_ia
- **Size of downloaded dataset files:** 0.08 MB
- **Size of the generated dataset:** 0.69 MB
- **Total amount of disk used:** 0.78 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha ha h..."
}
```
#### unshuffled_original_id
- **Size of downloaded dataset files:** 10.60 GB
- **Size of the generated dataset:** 32.32 GB
- **Total amount of disk used:** 42.91 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Perihal dari itu, kalau kunci hal yang demikian hilang, pemilik wajib melapor ke bengkel sah untuk dibuatkan kunci baru dengan ..."
}
```
#### unshuffled_original_ie
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.02 MB
- **Total amount of disk used:** 0.02 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "Plastic Yo Yo Metal Yo Yos Wooden Yo Yo Keychain Yo Yo Translucent Yo Yo Light Up Yo Yo Globe Yo Yo Stress Reliever Yo Yo Jellyfish Yo Yo Sports Ball Yo Yo Sound Yo Yo Miniature Yo Yo Promotional Yo Yo Novelty Yo Yo Video Game Yo Yo ECO Recycled Yo Yo"
}
```
#### unshuffled_original_ilo
- **Size of downloaded dataset files:** 0.27 MB
- **Size of the generated dataset:** 0.92 MB
- **Total amount of disk used:** 1.20 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Segun ken ni Ping-ay, ti yellow corn ti maysa kadagiti nadakamat a liberalized agricultural commodity iti daytoy a free trade k..."
}
```
#### unshuffled_original_io
- **Size of downloaded dataset files:** 0.04 MB
- **Size of the generated dataset:** 0.16 MB
- **Total amount of disk used:** 0.20 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Chekia esas parlamentala republiko. La chefo di stato esas la prezidanto. Til 2013 lu elektesis dal parlamento. Pos ta yaro, ol..."
}
```
#### unshuffled_original_is
- **Size of downloaded dataset files:** 533.03 MB
- **Size of the generated dataset:** 1.52 GB
- **Total amount of disk used:** 2.06 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Eyjar.net - upplýsinga- og fréttamiðill um Vestmannaeyjar - Fréttir - Nái núverandi stefna stjórnvalda fram að ganga mun það va..."
}
```
#### unshuffled_original_it
- **Size of downloaded dataset files:** 52.16 GB
- **Size of the generated dataset:** 147.38 GB
- **Total amount of disk used:** 199.54 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Jaundice - causes, treatment & pathology massaggio a osteochondrosis dellindizio di una controindicazione\\nTrattamento su un co..."
}
```
#### unshuffled_original_ja
- **Size of downloaded dataset files:** 79.56 GB
- **Size of the generated dataset:** 232.22 GB
- **Total amount of disk used:** 311.78 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"神社などへ一緒に同行して、様々な角度のショットで家族写真やお子様の写真を撮影致します！お好みに合わせて様々な写真を取ることができますので、その場でカメラマンへのリクエストも可能です！お子様の晴れ姿を、緊張していない自然な笑顔で残しませんか？\\n※七五三の..."
}
```
#### unshuffled_original_jbo
- **Size of downloaded dataset files:** 0.21 MB
- **Size of the generated dataset:** 0.77 MB
- **Total amount of disk used:** 0.98 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "ni'o 23 la cimast. cu 23moi djedi fi'o masti la cimast. noi ke'a cu cimoi masti .i 22 la cimast. cu purlamdei .ije 24 la cimast. cu bavlamdei"
}
```
#### unshuffled_original_jv
- **Size of downloaded dataset files:** 0.22 MB
- **Size of the generated dataset:** 0.69 MB
- **Total amount of disk used:** 0.91 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"José Mourinho (diwaca: [ʒuˈzɛ moˈɾiɲu]; lair ing Setubal, Portugal, 26 Januari 1963; umur 55 taun) iku salah siji pelatih bal k..."
}
```
#### unshuffled_original_ka
- **Size of downloaded dataset files:** 680.74 MB
- **Size of the generated dataset:** 3.77 GB
- **Total amount of disk used:** 4.45 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"წამიყვანე შენთან ერთად (ქართულად) / Возьми меня с собой (картулад) / (რუსული სერიალები ქართულად) (რუსების პორნო ონლაინში) (ruse..."
}
```
#### unshuffled_original_kk
- **Size of downloaded dataset files:** 615.06 MB
- **Size of the generated dataset:** 2.83 GB
- **Total amount of disk used:** 3.45 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Түлкібас ауданында «Латын негізді әліпби мен емле ережесі туралы насихат» жобасының тобы семинар өткізді\\nЕлорданың «Қазақстан»..."
}
```
#### unshuffled_original_km
- **Size of downloaded dataset files:** 193.28 MB
- **Size of the generated dataset:** 1.10 GB
- **Total amount of disk used:** 1.30 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ខ្សឹបដាក់ត្រចៀក៖ លោក សួស សុផានិត នាយផ្នែករដ្ឋបាលព្រៃឈើ ស្រុកភ្នំក្រវាញ់ ដែលទើបឡើងកាន់តំណែងថ្មី បើកដៃឲ្យឈ្នួញ ប្រព្រឹត្តបទល្មើស ..."
}
```
#### unshuffled_original_kn
- **Size of downloaded dataset files:** 342.15 MB
- **Size of the generated dataset:** 1.76 GB
- **Total amount of disk used:** 2.11 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ರಾಷ್ಟ್ರಪತಿ ಪ್ರಣಬ್ ಮುಖರ್ಜಿಯಿಂದ ಪದ್ಮ ಪ್ರಶಸ್ತಿ ಪ್ರದಾನ | President Pranab Mukherjee Confers Padma Awards | Photo Gallery on Kannada..."
}
```
#### unshuffled_original_ko
- **Size of downloaded dataset files:** 8.81 GB
- **Size of the generated dataset:** 25.29 GB
- **Total amount of disk used:** 34.10 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"CIA 프로젝트에서는 데이터베이스로 들어오는 요청을 중간에 수집(Sniffing)하고 수집한 데이터를 분석(Parsing)하여 그로 인한 결과를 판단하여 알릴 수 있는 시스템(Push Service)이 필요하다. 그리고 연구를 ..."
}
```
#### unshuffled_original_krc
- **Size of downloaded dataset files:** 0.66 MB
- **Size of the generated dataset:** 2.68 MB
- **Total amount of disk used:** 3.34 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Шамханланы, Бийлени къаршысына ябушуп, Батыр уланларыбызны къоллары булан «ортакъ ожакъ» къургъанбыз. Шо иш уллу зараллы иш бол..."
}
```
#### unshuffled_original_ku
- **Size of downloaded dataset files:** 33.38 MB
- **Size of the generated dataset:** 99.06 MB
- **Total amount of disk used:** 132.44 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Me di 114 bernameyên xwe yên berê da perçeyên ji berhemên zanyarî yên kurdzanên mezin bi wergera kurdî da ...\\nMe di 114 bernam..."
}
```
#### unshuffled_original_kv
- **Size of downloaded dataset files:** 0.40 MB
- **Size of the generated dataset:** 2.38 MB
- **Total amount of disk used:** 2.78 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Коми кытшыслӧн ыджытжык тор вӧр увтын куйлӧ, сійӧн и фаунасӧ татӧн аркмӧтӧны вӧрын олісь подаэз. Ассямаӧн лоӧ сія, мый кытшас с..."
}
```
#### unshuffled_original_kw
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.04 MB
- **Total amount of disk used:** 0.05 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼Pray without ceasing🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏🏼🙏..."
}
```
#### unshuffled_original_ky
- **Size of downloaded dataset files:** 152.64 MB
- **Size of the generated dataset:** 630.79 MB
- **Total amount of disk used:** 783.43 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Turmush: Бишкек шаардык кеңешинин кезексиз отурумунда мэрге ишенбөөчүлүк көрсөтүү маселеси каралат, - депутат Т.Сагынов\\nБишкек..."
}
```
#### unshuffled_original_la
- **Size of downloaded dataset files:** 5.46 MB
- **Size of the generated dataset:** 27.80 MB
- **Total amount of disk used:** 33.26 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Hæ sunt generationes Noë: Noë vir justus atque perfectus fuit in generationibus suis; cum Deo ambulavit.\\nEcce ego adducam aqua..."
}
```
#### unshuffled_original_lb
- **Size of downloaded dataset files:** 10.73 MB
- **Size of the generated dataset:** 30.60 MB
- **Total amount of disk used:** 41.32 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Während dem Gaardefestival \\\"Ambiance Jardins\\\" vum 15. bis de 17. Mee huet den SNJ nees zesumme mam Groupe Animateur en Inform..."
}
```
#### unshuffled_original_lez
- **Size of downloaded dataset files:** 0.83 MB
- **Size of the generated dataset:** 3.38 MB
- **Total amount of disk used:** 4.20 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Ахцегь хуьр, виридалай ч1ехи лезги хуьрерикая я. Ам Урусатдин виридалай къиблепатавай хуьрерикай я. Ин хуьр...\"..."
}
```
#### unshuffled_original_li
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.03 MB
- **Total amount of disk used:** 0.04 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"'t Good Goedenraad aan de Ezerbaek besjteit oet 'n kesjtièl mèt gesjlote haof en 'n park van 26 hectare. Hie in sjtoon väól beu..."
}
```
#### unshuffled_original_lmo
- **Size of downloaded dataset files:** 0.10 MB
- **Size of the generated dataset:** 0.47 MB
- **Total amount of disk used:** 0.58 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Serét (en tortonés: Sregh; en piemontés: Srèj) l'è 'n cümü italià, de la regiù del Piemónt, en Pruvìncia de Alessandria. El g'h..."
}
```
#### unshuffled_original_lo
- **Size of downloaded dataset files:** 33.92 MB
- **Size of the generated dataset:** 182.36 MB
- **Total amount of disk used:** 216.28 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"ຜູ້ພິພາກສາ ປະຈຳເຂດ ສຫລ ທ່ານນຶ່ງ ຕັດສິນວ່າ ໂຄງການເກັບກຳຂໍ້ມູນ ທາງໂທລະສັບ ຂອງອົງການ ຄວາມໝັ້ນຄົງແຫ່ງຊາດ ແມ່ນຖືກຕ້ອງ ຕາມກົດໝາຍ.\\nກະ..."
}
```
#### unshuffled_original_lrc
- **Size of downloaded dataset files:** 0.02 MB
- **Size of the generated dataset:** 0.07 MB
- **Total amount of disk used:** 0.09 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"آرلینگتون یئ گئل د شأریا ڤولاتچە ڤیرجینیا و یئ گئل د شأریا ڤولات ڤولاتچە یا یأکاگئرئتە ئمریکاە. ئی شأر دویومی کألوٙن شأر د راسا..."
}
```
#### unshuffled_original_lt
- **Size of downloaded dataset files:** 3.44 GB
- **Size of the generated dataset:** 9.45 GB
- **Total amount of disk used:** 12.89 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Čir vir vir pavasaris! Čia čia čia… dalinamės labai simpatiška video pamokėle, kurią pristato ab888art galerija.\\nBe galo papra..."
}
```
#### unshuffled_original_lv
- **Size of downloaded dataset files:** 1.49 GB
- **Size of the generated dataset:** 4.27 GB
- **Total amount of disk used:** 5.75 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Dekoratīvi sliekšņi MITSUBISHI OUTLANDER 2007, izgatavoti no ovālas formas, pulētas nerūsējošā tērauda caurules...\\ndažādas tūn..."
}
```
#### unshuffled_original_mai
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.33 MB
- **Total amount of disk used:** 0.34 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"१ · २ · ३ · ४ · ५ · ६ · ७ · ८ · ९ · १० · ११ · १२ · १३ · १४ · १५ · १६ · १७ · १८ · १९ · २० · २१ · २२ · २३ · २४ · २५ · २६ · २७ · २..."
}
```
#### unshuffled_original_mg
- **Size of downloaded dataset files:** 6.22 MB
- **Size of the generated dataset:** 21.79 MB
- **Total amount of disk used:** 28.01 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Nanamboatra taratasy apetaka sy soso-kevitra ho an'ny olona te-hanatevin-daharana ity fihetsiketsehana ity i Anocrena.\\nNosorat..."
}
```
#### unshuffled_original_mhr
- **Size of downloaded dataset files:** 1.84 MB
- **Size of the generated dataset:** 7.55 MB
- **Total amount of disk used:** 9.38 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Акрет жап годым Уганда кундемым Пигмей племена- влак айлен шогеныт. мемнан эран 1 курым гыч Банту племена влакат тиде кундемышк..."
}
```
#### unshuffled_original_min
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.63 MB
- **Total amount of disk used:** 0.64 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\" ..."
}
```
#### unshuffled_original_mk
- **Size of downloaded dataset files:** 508.24 MB
- **Size of the generated dataset:** 2.20 GB
- **Total amount of disk used:** 2.71 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"„Филм плус“ е насловен првиот филмски месечник во Македонија, чиј прв број ќе биде промовиран вечер во „Менада“. Новото македон..."
}
```
#### unshuffled_original_ml
- **Size of downloaded dataset files:** 938.69 MB
- **Size of the generated dataset:** 5.24 GB
- **Total amount of disk used:** 6.18 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"സ്ത്രീ പ്രവേശനം സര്ക്കാര് പൂര്ണമായും അംഗീകരിക്കുന്നുവെന്നും ശബരിമലയുടെ സുരക്ഷയില് ഇടപെടുമെന്നും സര്ക്കാര് ഹൈക്കോടതിയില്\\..."
}
```
#### unshuffled_original_mn
- **Size of downloaded dataset files:** 472.36 MB
- **Size of the generated dataset:** 2.33 GB
- **Total amount of disk used:** 2.81 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Монгол улс, Улаанбаатар хот - 14191 Энхтайваны өргөн чөлөө - 10, Багш хөгжлийн ордон, Багшийн мэргэжил дээшлүүлэх институт\\nБаг..."
}
```
#### unshuffled_original_mr
- **Size of downloaded dataset files:** 525.31 MB
- **Size of the generated dataset:** 2.82 GB
- **Total amount of disk used:** 3.34 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Home / motivational marathi story / उद्योजकता (Entrepreneurship) / यांना हे जमलय, तर आपल्याला का नाही जमणार ?\\nयापैकी कोणाचीही ..."
}
```
#### unshuffled_original_mrj
- **Size of downloaded dataset files:** 0.30 MB
- **Size of the generated dataset:** 1.16 MB
- **Total amount of disk used:** 1.47 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Лӹпӹвлӓ (латинлӓ Lepidoptera ; алыкмарла лыве-влак) — капшангывлӓ йыхыш пырышы сӱмӓн нӹл шылдыран капшангывлӓ. Цилӓжӹ 180000 тӹ..."
}
```
#### unshuffled_original_ms
- **Size of downloaded dataset files:** 28.46 MB
- **Size of the generated dataset:** 122.33 MB
- **Total amount of disk used:** 150.79 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Sanad pertama daripada Zuhair bin Harb daripada ‘Affan daripada Hammad daripada Thabit daripada Anas.\\nSanad kedua daripada ‘Ab..."
}
```
#### unshuffled_original_mt
- **Size of downloaded dataset files:** 7.53 MB
- **Size of the generated dataset:** 24.47 MB
- **Total amount of disk used:** 32.00 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "tibgħat il-kawża lura lill-Qorti Ġenerali għall-annullament jew għat-tnaqqis tal-penalità imposta mill-Kummissjoni bid-deċiżjoni inizjali kif emendata bid-deċiżjoni ta’ rettifika;"
}
```
#### unshuffled_original_mwl
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Deciplina social i outónoma que angloba atebidades de ouserbaçon, de análeze, de çcriçon, cumparaçon, de sistematizaçon i de sp..."
}
```
#### unshuffled_original_my
- **Size of downloaded dataset files:** 369.85 MB
- **Size of the generated dataset:** 2.02 GB
- **Total amount of disk used:** 2.39 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ျမ၀တီ - ရန္ကုန္တိုင္းေဒသႀကီး ေျမာက္ဥကၠလာပႏွင္႕ ဗဟန္းၿမိဳ႔နယ္ မေကြးတိုင္း ေဒသႀကီး ပခုကၠဴၿမိဳ႔နယ္တို႔၌ ျမန္မာ႕တပ္မေတာ္အား ေထာက္ခံ..."
}
```
#### unshuffled_original_myv
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"2018 иень умарьковонь 6-це чистэ сась паро куля! Россиянь культурань Министерствась макссь невтемань конёв (прокатной удостовер..."
}
```
#### unshuffled_original_mzn
- **Size of downloaded dataset files:** 0.18 MB
- **Size of the generated dataset:** 0.72 MB
- **Total amount of disk used:** 0.90 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"قرآن یا قوران اسلام ِآسمونی کتاب هسته. مسلمونون گانّّه قرآن ره خدا، وحی جه برسنییه، «محمد معجزه» هسته و ثقلین حدیث دله ونه خَو..."
}
```
#### unshuffled_original_nah
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.01 MB
- **Total amount of disk used:** 0.01 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "In mācuīlpōhualxihuitl VI (inic chicuacē) in mācuīlpōhualli xiuhitl cāhuitl īhuīcpa 501 xihuitl oc 600 xihuitl."
}
```
#### unshuffled_original_nap
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.02 MB
- **Total amount of disk used:** 0.02 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ò AUDIT í Ç è î ÿ å å 30 ò ÿ ÿ é, õ ñ ì ÿ, ê ã- ò à ì. å â å í ç â à à é ñ è å é ó ó ë. å å å û è å î é è à. à è à AUDIT 1-7 â ..."
}
```
#### unshuffled_original_nds
- **Size of downloaded dataset files:** 6.74 MB
- **Size of the generated dataset:** 18.23 MB
- **Total amount of disk used:** 24.99 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Dor kann sik vun nu af an de hele plattdüütsche Welt – vun Niebüll bit New York, vun Helgoland bit Honolulu – drapen. Allens, w..."
}
```
#### unshuffled_original_ne
- **Size of downloaded dataset files:** 355.29 MB
- **Size of the generated dataset:** 1.87 GB
- **Total amount of disk used:** 2.22 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"बर्दिबास नगरपालिकाको तेस्रो नगर परिषदबाट पारित आ.व.२०७३।७४ को संशोधित र २०७४।७५ को प्रस्तावित नीति, कार्यक्रम तथा बजेट\\nअार्थिक..."
}
```
#### unshuffled_original_new
- **Size of downloaded dataset files:** 1.03 MB
- **Size of the generated dataset:** 5.77 MB
- **Total amount of disk used:** 6.79 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"थ्व शहरयागु अक्षांश ३४.७००१६४ उत्तर व देशान्तर ८६.३७६४६९ पश्चिम खः (34.700164° N 86.376469° W)। थ्व थासे ७२२६७३२ वर्ग मिटर (२.७..."
}
```
#### unshuffled_original_nl
- **Size of downloaded dataset files:** 29.35 GB
- **Size of the generated dataset:** 83.23 GB
- **Total amount of disk used:** 112.58 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Op vrijdag 31 augustus wordt het nieuwe studiejaar van de masteropleiding architectuur geopend met een dagexcursie naar Venlo.\\..."
}
```
#### unshuffled_original_nn
- **Size of downloaded dataset files:** 32.86 MB
- **Size of the generated dataset:** 90.84 MB
- **Total amount of disk used:** 123.70 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "Planomtale krav til innhald Bakgrunn: Spørsmål frå fleire kommunar om kva ein planomtale/planbeskrivelse bør innehalde Fylkeskommunen og fylkesmannen har i ein del saker reist motsegn på formelt grunnlag"
}
```
#### unshuffled_original_no
- **Size of downloaded dataset files:** 3.11 GB
- **Size of the generated dataset:** 8.65 GB
- **Total amount of disk used:** 11.76 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Ytterligere aktører i primærhelsetjenesten og andre NHS-virksomheter ble infisert, inkludert legekontor.Læreren vår er så attra..."
}
```
#### unshuffled_original_oc
- **Size of downloaded dataset files:** 1.57 MB
- **Size of the generated dataset:** 6.12 MB
- **Total amount of disk used:** 7.71 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": ".рф (rf, còdi punycode: .xn--p1ai)[1] es lo nom de domeni en rus per Russia. Foguèt activat lo 12 de mai de 2010. Lo còdi latin es .ru."
}
```
#### unshuffled_original_or
- **Size of downloaded dataset files:** 49.84 MB
- **Size of the generated dataset:** 260.15 MB
- **Total amount of disk used:** 309.99 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ଭୁବନେଶ୍ୱର, ୨୭/୧– (ଓଡ଼ିଆ ପୁଅ) ସିପିଆଇ ଜାତୀୟ ପରିଷଦର ଆହ୍ୱାନକ୍ରମେ ଗତକାଲି ଜାନୁୟାରୀ ୨୬ ସାଧାରଣତନ୍ତ୍ର ଦିବସକୁ ଦେଶ ବ୍ୟାପୀ ସମ୍ବିଧାନ ସୁରକ୍ଷା ..."
}
```
#### unshuffled_original_os
- **Size of downloaded dataset files:** 3.09 MB
- **Size of the generated dataset:** 12.90 MB
- **Total amount of disk used:** 15.99 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"1. Лæппу æмæ чызг казрæдзийы зæрдæмæ куы фæцæуынц æмæ, куы сфæнд кæнынц сæ цард баиу кæнын, уæд лæппу бар ракуры чызгæй, цæмæй ..."
}
```
#### unshuffled_original_pa
- **Size of downloaded dataset files:** 164.21 MB
- **Size of the generated dataset:** 801.16 MB
- **Total amount of disk used:** 965.37 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ਰਜਿ: ਨੰ: PB/JL-138/2018-20 ਜਿਲਦ 63, ਬਾਨੀ ਸੰਪਾਦਕ (ਸਵ:) ਡਾ: ਸਾਧੂ ਸਿੰਘ ਹਮਦਰਦ ਫ਼ੋਨ : 0181-2455961-62-63, 5032400, ਫੈਕਸ : 2455960, 2..."
}
```
#### unshuffled_original_pam
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Áku pu i Anak ning Aláya at ngeni ipákit kó kékayu ngan nûng makanánu lang susúlat détinang kulit a mágkas. Lauan ya ing tarátu..."
}
```
#### unshuffled_original_pl
- **Size of downloaded dataset files:** 42.88 GB
- **Size of the generated dataset:** 117.12 GB
- **Total amount of disk used:** 160.01 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"System informatyczny - Załącznik nr 1 do zarządzenia Wójta Gminy Podegrodzie Nr 530/2013 z dnia 27 maja 2013 r\\nSystem informat..."
}
```
#### unshuffled_original_pms
- **Size of downloaded dataset files:** 0.75 MB
- **Size of the generated dataset:** 2.15 MB
- **Total amount of disk used:** 2.92 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Louvigné-du-Désert a l'é na comun-a fransèisa ant la region aministrativa dla Brëtagna, ant ël dipartiment d'Ille-et-Vilaine. A..."
}
```
#### unshuffled_original_pnb
- **Size of downloaded dataset files:** 3.22 MB
- **Size of the generated dataset:** 12.04 MB
- **Total amount of disk used:** 15.26 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"ایہ فائل Wikimedia Commons توں اے تے دوجیاں ویونتاں تے وی ورتی جاےکدی اے۔ گل بات اس دے فائل گل بات صفہ تے تھلے دتی گئی۔\"..."
}
```
#### unshuffled_original_ps
- **Size of downloaded dataset files:** 103.66 MB
- **Size of the generated dataset:** 379.51 MB
- **Total amount of disk used:** 483.17 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Many people usually use the time period ‘business to business (B2B) advertising,’ however most of them do not know precisely wh..."
}
```
#### unshuffled_original_pt
- **Size of downloaded dataset files:** 47.26 GB
- **Size of the generated dataset:** 132.64 GB
- **Total amount of disk used:** 179.89 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Você pode estar lendo este texto no sofá, levantar pra pegar uma breja na geladeira, dar uma cagada e sentar novamente, sem int..."
}
```
#### unshuffled_original_qu
- **Size of downloaded dataset files:** 0.02 MB
- **Size of the generated dataset:** 0.08 MB
- **Total amount of disk used:** 0.10 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Warayu wichay (kastilla simipi: Ascensión de Guarayos) nisqaqa Buliwya mama llaqtapi, Santa Krus suyupi, huk llaqtam, Warayu pruwinsyap uma llaqtanmi."
}
```
#### unshuffled_original_rm
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.01 MB
- **Total amount of disk used:** 0.01 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"practicists agrars / practicistas agraras AFP pon far ina furmaziun da basa scursanida per cuntanscher in attestat federal da q..."
}
```
#### unshuffled_original_ro
- **Size of downloaded dataset files:** 9.53 GB
- **Size of the generated dataset:** 26.87 GB
- **Total amount of disk used:** 36.40 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"“În viață, oportunitatea nu este totul. Cine atrage Lumina, cineva bun în umbră. Timpul ne creează.” maestru\\nLyn.Evans: Ce mar..."
}
```
#### unshuffled_original_ru
- **Size of downloaded dataset files:** 319.76 GB
- **Size of the generated dataset:** 1241.63 GB
- **Total amount of disk used:** 1561.38 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Доступ к данному профилю для публичного просмотра закрыт администрацией сайта - профиль находится на модерации.\\nРазработчикам ..."
}
```
#### unshuffled_original_sa
- **Size of downloaded dataset files:** 17.52 MB
- **Size of the generated dataset:** 97.06 MB
- **Total amount of disk used:** 114.58 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"अनिरुद्धनगरे क्रीडिता रामलीला सम्प्रति समाप्ता अस्ति । तस्य कानिचन् चित्राणि पूर्वमेव प्रकाशितानि सन्ति । द्वौ चलचित्रौ अपि ..."
}
```
#### unshuffled_original_sah
- **Size of downloaded dataset files:** 9.08 MB
- **Size of the generated dataset:** 43.82 MB
- **Total amount of disk used:** 52.90 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████..."
}
```
#### unshuffled_original_scn
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
{
"id": 0,
"text": "La gilusìa è nu sintimentu dulurusu ca nasci d'un disideriu di pussessu sclusivu ntê cunfrunti dâ pirsuna amata e dû timuri, dû suspettu o dâ cirtizza dâ sò nfidiltati."
}
```
#### unshuffled_original_sd
- **Size of downloaded dataset files:** 90.62 MB
- **Size of the generated dataset:** 364.25 MB
- **Total amount of disk used:** 454.88 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"هر ڪو ڄاڻي ٿو ته جڏهن توهان هڪ وڏي خريد ڪرڻ چاهيون ٿا, توهان پڄي ضروري حڪم ۾ ان جي ڪم ڪرڻ جي هٿ ۾ لاڳاپو ڪيو آهي. جي شيء آهي ته..."
}
```
#### unshuffled_original_sh
- **Size of downloaded dataset files:** 3.46 MB
- **Size of the generated dataset:** 25.84 MB
- **Total amount of disk used:** 29.30 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Opština Gornja Radgona se nalazi u sjeveroistočnoj Sloveniji i graniči s susjednom Austriji duž rijeke Mure. Sa tridesetim nase..."
}
```
#### unshuffled_original_si
- **Size of downloaded dataset files:** 310.93 MB
- **Size of the generated dataset:** 1.47 GB
- **Total amount of disk used:** 1.78 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"ලාංකීය සිතිවිලි සිංහල බ්ලොග් කියවනය කොත්තු සින්ඩිය ලංකා Blogger හත්මාළුව ලංකා බ්ලොග් කියවනය මාතලන්ගේ සින්ඩිය මොබයිල්lk\\nඅවකාශය ..."
}
```
#### unshuffled_original_sk
- **Size of downloaded dataset files:** 3.71 GB
- **Size of the generated dataset:** 9.81 GB
- **Total amount of disk used:** 13.52 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Aktivity | Agentúra podporovaného zamestnávania | vzdelávanie pre klientov, vzdelávanie pre odborníkov, kurzy\\nŠpecializované k..."
}
```
#### unshuffled_original_sl
- **Size of downloaded dataset files:** 956.20 MB
- **Size of the generated dataset:** 2.68 GB
- **Total amount of disk used:** 3.63 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Če Creatures, ki je želel, da pridejo na čas, predvsem je povedlo – razlikuje od ljubosumja začel grizenja kolen (ali zadnjica)..."
}
```
#### unshuffled_original_so
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.06 MB
- **Total amount of disk used:** 0.06 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"тттттттттттттттттттттттттттттттт тттттттттттттттттттттттттттттттт тттттттттттттттттттттттттттттттт ттттттттттттттттуууууууууууу..."
}
```
#### unshuffled_original_sq
- **Size of downloaded dataset files:** 861.84 MB
- **Size of the generated dataset:** 2.44 GB
- **Total amount of disk used:** 3.30 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Çfarë do të më pëlqente tek një femër ose çfarë do të më shndërronte në një shpërthim drite? – Albert Vataj\\nTë gjithëve një zo..."
}
```
#### unshuffled_original_sr
- **Size of downloaded dataset files:** 1.08 GB
- **Size of the generated dataset:** 4.13 GB
- **Total amount of disk used:** 5.21 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Корисни савети за сваки дан. На сајту су разне категорије, као што су љепота, мода, кување и поправка властитим рукама.\\nШколск..."
}
```
#### unshuffled_original_su
- **Size of downloaded dataset files:** 0.06 MB
- **Size of the generated dataset:** 0.23 MB
- **Total amount of disk used:** 0.28 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Kartu krédit nyaéta \"duit plastik\" anu dikaluarkeun ku bank pikeun alat pambayaran di tempat-tempat nu tangtu samisal jiga di hotél, réstoran, tempat rékréasi jeung sajabana.[1]"
}
```
#### unshuffled_original_sv
- **Size of downloaded dataset files:** 17.18 GB
- **Size of the generated dataset:** 47.00 GB
- **Total amount of disk used:** 64.18 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"1783 är ett viktigt årtal i den nya tidens historia. Det året slöts en fred i Paris och därmed blev de 13 brittiska kolonierna ..."
}
```
#### unshuffled_original_sw
- **Size of downloaded dataset files:** 3.71 MB
- **Size of the generated dataset:** 14.07 MB
- **Total amount of disk used:** 17.78 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Miripuko hiyo inakuja mwanzoni mwa Wiki Takatifu kuelekea Pasaka na ikiwa ni wiki chache tu kabla ya Papa Francis kuanza ziara yake katika nchi hiyo yenye idadi kubwa kabisa ya watu katika ulimwengu wa nchi za Kiarabu."
}
```
#### unshuffled_original_ta
- **Size of downloaded dataset files:** 1.74 GB
- **Size of the generated dataset:** 9.93 GB
- **Total amount of disk used:** 11.67 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"பொழுது சாய்ந்து வெகு நேரமாகிவிட்டது. கூலி வேலைக்குப் போயிருந்த 'சித்தாள் ' பெண்கள் எல்லோரும் வீடு திரும்பி விட்டார்கள். இன்னும்..."
}
```
#### unshuffled_original_te
- **Size of downloaded dataset files:** 522.47 MB
- **Size of the generated dataset:** 2.61 GB
- **Total amount of disk used:** 3.13 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"హర్యానాలో టోల్ దగ్గర సిబ్బంది.. స్థానిక ప్రజలు కొట్టుకున్నారు. కర్నాల్ అనే గ్రామానికి సమీపంలో టోల్ గేట్ ఉంది. అయితే సాధారణంగా స..."
}
```
#### unshuffled_original_tg
- **Size of downloaded dataset files:** 90.97 MB
- **Size of the generated dataset:** 397.43 MB
- **Total amount of disk used:** 488.41 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Ҳумайро гуфтааст, мухолифи низом аст, низоме, ки дар Тоҷикистон вуҷуд дорад. Ба ин маънӣ, худро мухолифи давлату ҳукумати Тоҷик..."
}
```
#### unshuffled_original_th
- **Size of downloaded dataset files:** 7.38 GB
- **Size of the generated dataset:** 38.29 GB
- **Total amount of disk used:** 45.67 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ฟันที่แลดูขาวสะอาดไม่มีเศษอาหารติดอยู่ เหงือกสีชมพู ไม่เจ็บ หรือมีเลือดออกเวลาแปรงฟันหรือขัดฟัน ไม่มีปัญหาเรื่องกลิ่นปาก ทำให้ก..."
}
```
#### unshuffled_original_tk
- **Size of downloaded dataset files:** 2.96 MB
- **Size of the generated dataset:** 10.66 MB
- **Total amount of disk used:** 13.62 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"Türkmenistanyň Prezidenti agyr atletika boýunça dünýä çempionatyna taýýarlyk işleriniň barşy bilen tanyşdy\\nHalallykdan kemal t..."
}
```
#### unshuffled_original_tl
- **Size of downloaded dataset files:** 204.89 MB
- **Size of the generated dataset:** 606.30 MB
- **Total amount of disk used:** 811.19 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"“Gusto ko manawagan sa mga Unit Head ng Chanel 2 Salve. Kasi napapansin ko iyon mga alaga ko ang taping halos once a week lang,..."
}
```
#### unshuffled_original_tr
- **Size of downloaded dataset files:** 21.96 GB
- **Size of the generated dataset:** 63.58 GB
- **Total amount of disk used:** 85.54 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Son yıllarda görülen ay tutulmalarına göre daha etkili olacağı söylenen Kanlı veya Kırmızı Ay Tutulmasına saatler kaldı. Bu akş..."
}
```
#### unshuffled_original_tt
- **Size of downloaded dataset files:** 151.06 MB
- **Size of the generated dataset:** 703.42 MB
- **Total amount of disk used:** 854.47 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"\\\"Иремнең вафатына 40 көн узгач, Алмаз да безнең өйгә кереп үлде\\\". Арчада 35 яшьлек ир өстенә кондызлар ега башлаган агач төшк..."
}
```
#### unshuffled_original_tyv
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.01 MB
- **Total amount of disk used:** 0.01 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Экии, хүндүлуг аалчылар болгаш тыва дылдың деткикчилери! Тыва дылдың болгаш чогаалдың ховар бир башкызынга, Менги Ооржакка, ажы..."
}
```
#### unshuffled_original_ug
- **Size of downloaded dataset files:** 27.92 MB
- **Size of the generated dataset:** 127.42 MB
- **Total amount of disk used:** 155.35 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"زاڭ-ءتۇزىم | عىلىم-تەحنيكا | ءتىل-ادەبيەت | تۇرمىس | دەنە تاربيە | ساياحات-ورتا | سۋرەتتى حابار | سىر سۇحبات | ارناۋلى تاقىرىپ ..."
}
```
#### unshuffled_original_uk
- **Size of downloaded dataset files:** 14.42 GB
- **Size of the generated dataset:** 56.44 GB
- **Total amount of disk used:** 70.86 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Про надання роз'яснення (щодо форми письмового зобов'язання громадян про зворотне ввезення/вивезення товарів), Державна митна с..."
}
```
#### unshuffled_original_ur
- **Size of downloaded dataset files:** 712.61 MB
- **Size of the generated dataset:** 2.80 GB
- **Total amount of disk used:** 3.51 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"آئیے اہم اسلامی کتب کو یونیکوڈ میں انٹرنیٹ پر پیش کرنے کے لئے مل جل کر آن لائن ٹائپنگ کریں۔ محدث ٹائپنگ پراجیکٹ کے ذریعے آپ روز..."
}
```
#### unshuffled_original_uz
- **Size of downloaded dataset files:** 5.78 MB
- **Size of the generated dataset:** 21.46 MB
- **Total amount of disk used:** 27.24 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Qurama tog'lari tizmasining Toshkentdan 154 km uzoqlikdagi Toshkent-Ush yo'li yeqasidaxushmanzara tabiat qo'ynida joylashgan maydoni 30 ga.\nBolalarni sog'lomlashtirish oromgohi Bo'stonliq tumani Oqtosh muntaqasining soy-salqin gushasida joylashgan."
}
```
#### unshuffled_original_vec
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.02 MB
- **Total amount of disk used:** 0.03 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Par ogni pónto, ła derivada ła xe ła pendensa de ła reta tangente a ła curva de ła funsion f. Ła reta de cołor róso l'è senpre ..."
}
```
#### unshuffled_original_vi
- **Size of downloaded dataset files:** 21.50 GB
- **Size of the generated dataset:** 72.23 GB
- **Total amount of disk used:** 93.73 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Canh chua cá bông lau không chỉ là món ăn giải nhiệt, thanh mát ngày hè mà còn là món siêu bổ dưỡng, rất tốt cho người gầy ốm. ..."
}
```
#### unshuffled_original_vo
- **Size of downloaded dataset files:** 0.30 MB
- **Size of the generated dataset:** 2.12 MB
- **Total amount of disk used:** 2.42 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Sarniguet binon zif in ziläk: Hautes-Pyrénées, in topäd: Midi-Pyrénées, in Fransän. Sarniguet topon videtü 43°19’ 7’’ N e lunetü 0°5’ 19’’ L."
}
```
#### unshuffled_original_wa
- **Size of downloaded dataset files:** 0.09 MB
- **Size of the generated dataset:** 0.29 MB
- **Total amount of disk used:** 0.38 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "Cisse pådje ci n' est co k' on djermon, dj' ô bén k' el pådje est djusse sibåtcheye, eyet co trop tene; et s' divreut ele ecråxhî ene miete."
}
```
#### unshuffled_original_war
- **Size of downloaded dataset files:** 0.64 MB
- **Size of the generated dataset:** 2.68 MB
- **Total amount of disk used:** 3.32 MB
An example of 'train' looks as follows.
```
{
"id": 1,
"text": "An Honce amo in usa ka baryo ngan munisipalidad ha distrito han Rožňava ha rehiyon han Košice ha nasod han Slovakia.\nAn Rumegies amo in usa ka komyun ha departamento han Nord ngan ha rehiyon han Nord-Pas-de-Calais ha nasod han Fransya."
}
```
#### unshuffled_original_wuu
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.12 MB
- **Total amount of disk used:** 0.13 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"伊春元旦天气 伊春腊八天气 伊春春节天气 伊春情人节天气 伊春元宵节天气 伊春愚人节天气 伊春清明节天气 伊春劳动节天气 伊春母亲节天气 伊春端午节天气 伊春七夕节天气 伊春教师节天气 伊春中秋节天气 伊春国庆节天气 伊春重阳节天气 伊春万圣节天气 伊春..."
}
```
#### unshuffled_original_xal
- **Size of downloaded dataset files:** 0.03 MB
- **Size of the generated dataset:** 0.12 MB
- **Total amount of disk used:** 0.15 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Арнгудин Орн гисн Европд бәәдг һазр. 2007 җилин тooһaр эн орн нутгт 3,600,523 әмтн бәәдг билә. Арнгудин Орнин хотл балһсна нерн..."
}
```
#### unshuffled_original_xmf
- **Size of downloaded dataset files:** 1.05 MB
- **Size of the generated dataset:** 6.12 MB
- **Total amount of disk used:** 7.17 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"მოჩამილი ტექსტი წჷმორინელი რე Creative Commons Attribution-ShareAlike ლიცენზიათ; შილებე გეძინელი პირობეფიშ არსებუა. კილიშკილიშა..."
}
```
#### unshuffled_original_yi
- **Size of downloaded dataset files:** 33.33 MB
- **Size of the generated dataset:** 147.60 MB
- **Total amount of disk used:** 180.94 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"ממשותדיק - חבֿרה, איך אַרבעט איצט אױף אַ זשורנאַל. טאָמער איר האָט עפּעס צוצוגעבן זאָלט איר שיקן מיר אַן אָנזאָג. ס'װעט הײסן \\\"..."
}
```
#### unshuffled_original_yo
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.06 MB
- **Total amount of disk used:** 0.06 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"text": "\"Copyright © 2018 BBC. BBC kò mọ̀ nípa àwọn ohun tí ó wà ní àwọn ojú òpó tí ó wà ní ìta. Ọwọ́ tí a fi mú ìbáṣepọ̀ ti ìta.\"..."
}
```
#### unshuffled_original_yue
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 0.00 MB
- **Total amount of disk used:** 0.00 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 我 灌 我 灌 我 灌 灌 灌 你還不爆 我累了 投降輸一半可以嗎\"..."
}
```
#### unshuffled_original_zh
- **Size of downloaded dataset files:** 206.00 GB
- **Size of the generated dataset:** 545.61 GB
- **Total amount of disk used:** 751.61 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"id": 1,
"text": "\"中国铝灰网 中国有色金属矿产网 中国黄莲网 中国水轮发电机网 中国抽油泵网 中国数控雕刻机网 中国不锈钢抛光网 中国磨具加工网 中国压铸铝网 中国耐水腻子网 中国手机摄像头网 中国粗粮网 中国车门锁网 中国钛粉网 中国轮圈网\\n天天中奖彩票图 天天中彩票..."
}
```
</details>
### Data Fields
The data fields are the same among all configs.
- `id`: a `int64` feature.
- `text`: a `string` feature.
### Data Splits
<details>
<summary>Click to expand the number of samples per configuration</summary>
| Language | Language code | Name original | Train original | Words original | Size original | Name deduplicated | Train deduplicated | Words deduplicated | Size deduplicated |
| ----------------- | ------------- | ----------------------- | -------------- | --------------- | ------------- | --------------------------- | ------------------ | ------------------ | ----------------- |
| Afrikaans | af | unshuffled_original_af | 201117 | 43,482,801 | 241M | unshuffled_deduplicated_af | 130640 | 29,533,437 | 163M |
| Albanian | sq | unshuffled_original_sq | 672077 | 374,196,110 | 2.3G | unshuffled_deduplicated_sq | 461598 | 186,856,699 | 1.2G |
| Alemannic | als | unshuffled_original_als | 7324 | 841,750 | 5.0M | unshuffled_deduplicated_als | 4518 | 459,001 | 2.8M |
| Amharic | am | unshuffled_original_am | 83663 | 28,301,601 | 360M | unshuffled_deduplicated_am | 43102 | 16,086,628 | 206M |
| Arabic | ar | unshuffled_original_ar | 16365602 | 8,117,162,828 | 82G | unshuffled_deduplicated_ar | 9006977 | 3,171,221,354 | 32G |
| Aragonese | an | unshuffled_original_an | 2449 | 52,896 | 1.3M | unshuffled_deduplicated_an | 2025 | 45,669 | 801K |
| Armenian | hy | unshuffled_original_hy | 659430 | 273,919,388 | 3.7G | unshuffled_deduplicated_hy | 396093 | 110,196,043 | 1.5G |
| Assamese | as | unshuffled_original_as | 14985 | 6,956,663 | 113M | unshuffled_deduplicated_as | 9212 | 4,366,570 | 71M |
| Asturian | ast | unshuffled_original_ast | 6999 | 381,005 | 2.4M | unshuffled_deduplicated_ast | 5343 | 325,237 | 2.0M |
| Avaric | av | unshuffled_original_av | 456 | 24,720 | 409K | unshuffled_deduplicated_av | 360 | 19,478 | 324K |
| Azerbaijani | az | unshuffled_original_az | 912330 | 322,641,710 | 2.8G | unshuffled_deduplicated_az | 626796 | 167,742,296 | 1.5G |
| Bashkir | ba | unshuffled_original_ba | 42551 | 9,796,764 | 128M | unshuffled_deduplicated_ba | 27050 | 6,922,589 | 90M |
| Basque | eu | unshuffled_original_eu | 506883 | 120,456,652 | 848M | unshuffled_deduplicated_eu | 256513 | 45,359,710 | 342M |
| Bavarian | bar | unshuffled_original_bar | 4 | 399 | 503 | unshuffled_deduplicated_bar | 4 | 399 | 503 |
| Belarusian | be | unshuffled_original_be | 586031 | 144,579,630 | 1.8G | unshuffled_deduplicated_be | 307405 | 83,499,037 | 1.1G |
| Bengali | bn | unshuffled_original_bn | 1675515 | 623,575,733 | 11G | unshuffled_deduplicated_bn | 1114481 | 363,766,143 | 5.8G |
| Bihari | bh | unshuffled_original_bh | 336 | 8,848 | 110K | unshuffled_deduplicated_bh | 82 | 2,875 | 34K |
| Bishnupriya | bpy | unshuffled_original_bpy | 6046 | 198,286 | 4.1M | unshuffled_deduplicated_bpy | 1770 | 96,940 | 1.7M |
| Bosnian | bs | unshuffled_original_bs | 2143 | 106,448 | 447K | unshuffled_deduplicated_bs | 702 | 20,485 | 116K |
| Breton | br | unshuffled_original_br | 37085 | 5,013,241 | 29M | unshuffled_deduplicated_br | 14724 | 2,890,384 | 16M |
| Bulgarian | bg | unshuffled_original_bg | 5869686 | 2,947,648,106 | 32G | unshuffled_deduplicated_bg | 3398679 | 1,268,114,977 | 14G |
| Burmese | my | unshuffled_original_my | 232329 | 56,111,184 | 1.9G | unshuffled_deduplicated_my | 136639 | 30,102,173 | 1.1G |
| Catalan | ca | unshuffled_original_ca | 4390754 | 1,360,212,450 | 8.0G | unshuffled_deduplicated_ca | 2458067 | 729,333,440 | 4.3G |
| Cebuano | ceb | unshuffled_original_ceb | 56248 | 6,603,567 | 39M | unshuffled_deduplicated_ceb | 26145 | 3,675,024 | 24M |
| Central Bikol | bcl | unshuffled_original_bcl | 1 | 312 | 885 | unshuffled_deduplicated_bcl | 1 | 312 | 885 |
| Central Khmer | km | unshuffled_original_km | 159363 | 20,690,610 | 1.1G | unshuffled_deduplicated_km | 108346 | 10,082,245 | 581M |
| Central Kurdish | ckb | unshuffled_original_ckb | 103639 | 48,478,334 | 487M | unshuffled_deduplicated_ckb | 68210 | 18,726,721 | 226M |
| Chavacano | cbk | unshuffled_original_cbk | 1 | 130 | 520 | unshuffled_deduplicated_cbk | 1 | 130 | 520 |
| Chechen | ce | unshuffled_original_ce | 4042 | 711,051 | 8.3M | unshuffled_deduplicated_ce | 2984 | 568,146 | 6.7M |
| Chinese | zh | unshuffled_original_zh | 60137667 | 14,986,424,850 | 508G | unshuffled_deduplicated_zh | 41708901 | 6,350,215,113 | 249G |
| Chuvash | cv | unshuffled_original_cv | 20281 | 3,041,614 | 39M | unshuffled_deduplicated_cv | 10130 | 2,054,810 | 26M |
| Cornish | kw | unshuffled_original_kw | 203 | 8,329 | 44K | unshuffled_deduplicated_kw | 68 | 2,704 | 14K |
| Croatian | hr | unshuffled_original_hr | 582219 | 34,232,765 | 226M | unshuffled_deduplicated_hr | 321484 | 16,727,640 | 110M |
| Czech | cs | unshuffled_original_cs | 21001388 | 7,715,977,441 | 53G | unshuffled_deduplicated_cs | 12308039 | 3,540,997,509 | 24G |
| Danish | da | unshuffled_original_da | 7664010 | 2,637,463,889 | 16G | unshuffled_deduplicated_da | 4771098 | 1,620,091,317 | 9.5G |
| Dhivehi | dv | unshuffled_original_dv | 21018 | 7,559,472 | 126M | unshuffled_deduplicated_dv | 17024 | 4,726,660 | 79M |
| Dimli | diq | unshuffled_original_diq | 1 | 19 | 146 | unshuffled_deduplicated_diq | 1 | 19 | 146 |
| Dutch | nl | unshuffled_original_nl | 34682142 | 13,020,136,373 | 78G | unshuffled_deduplicated_nl | 20812149 | 6,598,786,137 | 39G |
| Eastern Mari | mhr | unshuffled_original_mhr | 3212 | 565,992 | 7.2M | unshuffled_deduplicated_mhr | 2515 | 469,297 | 6.0M |
| Egyptian Arabic | arz | unshuffled_original_arz | 158113 | 7,305,151 | 66M | unshuffled_deduplicated_arz | 79928 | 3,659,419 | 33M |
| Emilian-Romagnol | eml | unshuffled_original_eml | 84 | 6,376 | 25K | unshuffled_deduplicated_eml | 80 | 6,121 | 24K |
| English | en | unshuffled_original_en | 455994980 | 418,187,793,408 | 2.3T | unshuffled_deduplicated_en | 304230423 | 215,841,256,971 | 1.2T |
| Erzya | myv | unshuffled_original_myv | 6 | 90 | 1.4K | unshuffled_deduplicated_myv | 5 | 78 | 1.2K |
| Esperanto | eo | unshuffled_original_eo | 121171 | 48,486,161 | 299M | unshuffled_deduplicated_eo | 84752 | 37,324,446 | 228M |
| Estonian | et | unshuffled_original_et | 2093621 | 643,163,730 | 4.8G | unshuffled_deduplicated_et | 1172041 | 309,931,463 | 2.3G |
| Finnish | fi | unshuffled_original_fi | 8557453 | 3,196,666,419 | 27G | unshuffled_deduplicated_fi | 5326443 | 1,597,855,468 | 13G |
| French | fr | unshuffled_original_fr | 96742378 | 46,896,036,417 | 282G | unshuffled_deduplicated_fr | 59448891 | 23,206,776,649 | 138G |
| Galician | gl | unshuffled_original_gl | 544388 | 102,011,291 | 620M | unshuffled_deduplicated_gl | 284320 | 63,600,602 | 384M |
| Georgian | ka | unshuffled_original_ka | 563916 | 171,950,621 | 3.6G | unshuffled_deduplicated_ka | 372158 | 91,569,739 | 1.9G |
| German | de | unshuffled_original_de | 104913504 | 44,878,908,446 | 308G | unshuffled_deduplicated_de | 62398034 | 21,529,164,172 | 145G |
| Goan Konkani | gom | unshuffled_original_gom | 640 | 124,277 | 2.2M | unshuffled_deduplicated_gom | 484 | 102,306 | 1.8M |
| Guarani | gn | unshuffled_original_gn | 106 | 7,382 | 36K | unshuffled_deduplicated_gn | 68 | 4,680 | 24K |
| Gujarati | gu | unshuffled_original_gu | 240691 | 72,045,701 | 1.1G | unshuffled_deduplicated_gu | 169834 | 50,023,432 | 722M |
| Haitian | ht | unshuffled_original_ht | 13 | 1,014 | 3.9K | unshuffled_deduplicated_ht | 9 | 832 | 3.3K |
| Hebrew | he | unshuffled_original_he | 3808397 | 2,067,753,528 | 20G | unshuffled_deduplicated_he | 2375030 | 1,032,018,056 | 9.8G |
| Hindi | hi | unshuffled_original_hi | 3264660 | 1,372,234,782 | 17G | unshuffled_deduplicated_hi | 1909387 | 745,774,934 | 8.9G |
| Hungarian | hu | unshuffled_original_hu | 11197780 | 5,163,936,345 | 40G | unshuffled_deduplicated_hu | 6582908 | 2,339,127,555 | 18G |
| Icelandic | is | unshuffled_original_is | 625673 | 219,900,094 | 1.5G | unshuffled_deduplicated_is | 389515 | 129,818,331 | 846M |
| Ido | io | unshuffled_original_io | 694 | 25,702 | 147K | unshuffled_deduplicated_io | 617 | 22,773 | 130K |
| Iloko | ilo | unshuffled_original_ilo | 2638 | 142,942 | 874K | unshuffled_deduplicated_ilo | 1578 | 105,564 | 636K |
| Indonesian | id | unshuffled_original_id | 16236463 | 4,574,692,265 | 30G | unshuffled_deduplicated_id | 9948521 | 2,394,957,629 | 16G |
| Interlingua | ia | unshuffled_original_ia | 1040 | 180,231 | 662K | unshuffled_deduplicated_ia | 529 | 100,019 | 360K |
| Interlingue | ie | unshuffled_original_ie | 101 | 5,352 | 24K | unshuffled_deduplicated_ie | 11 | 602 | 1.6K |
| Irish | ga | unshuffled_original_ga | 83223 | 14,483,593 | 88M | unshuffled_deduplicated_ga | 46493 | 10,017,303 | 60M |
| Italian | it | unshuffled_original_it | 46981781 | 22,248,707,341 | 137G | unshuffled_deduplicated_it | 28522082 | 11,250,012,896 | 69G |
| Japanese | ja | unshuffled_original_ja | 62721527 | 4,962,979,182 | 216G | unshuffled_deduplicated_ja | 39496439 | 1,123,067,063 | 106G |
| Javanese | jv | unshuffled_original_jv | 1445 | 104,896 | 659K | unshuffled_deduplicated_jv | 1163 | 86,654 | 583K |
| Kalmyk | xal | unshuffled_original_xal | 39 | 10,277 | 113K | unshuffled_deduplicated_xal | 36 | 10,155 | 112K |
| Kannada | kn | unshuffled_original_kn | 350363 | 81,186,863 | 1.7G | unshuffled_deduplicated_kn | 251064 | 49,343,462 | 1.1G |
| Karachay-Balkar | krc | unshuffled_original_krc | 1581 | 185,436 | 2.6M | unshuffled_deduplicated_krc | 1377 | 166,496 | 2.3M |
| Kazakh | kk | unshuffled_original_kk | 524591 | 191,126,469 | 2.7G | unshuffled_deduplicated_kk | 338073 | 108,388,743 | 1.5G |
| Kirghiz | ky | unshuffled_original_ky | 146993 | 44,194,823 | 600M | unshuffled_deduplicated_ky | 86561 | 28,982,620 | 388M |
| Komi | kv | unshuffled_original_kv | 1549 | 201,404 | 2.3M | unshuffled_deduplicated_kv | 924 | 95,243 | 1.2M |
| Korean | ko | unshuffled_original_ko | 7345075 | 2,368,765,142 | 24G | unshuffled_deduplicated_ko | 3675420 | 1,120,375,149 | 12G |
| Kurdish | ku | unshuffled_original_ku | 46535 | 15,561,003 | 94M | unshuffled_deduplicated_ku | 29054 | 9,946,440 | 60M |
| Lao | lo | unshuffled_original_lo | 52910 | 4,133,311 | 174M | unshuffled_deduplicated_lo | 32652 | 2,583,342 | 114M |
| Latin | la | unshuffled_original_la | 94588 | 4,122,201 | 26M | unshuffled_deduplicated_la | 18808 | 1,328,038 | 8.3M |
| Latvian | lv | unshuffled_original_lv | 1593820 | 520,761,977 | 4.0G | unshuffled_deduplicated_lv | 843195 | 236,428,905 | 1.8G |
| Lezghian | lez | unshuffled_original_lez | 1485 | 247,646 | 3.3M | unshuffled_deduplicated_lez | 1381 | 224,871 | 3.0M |
| Limburgan | li | unshuffled_original_li | 137 | 4,730 | 29K | unshuffled_deduplicated_li | 118 | 4,283 | 27K |
| Lithuanian | lt | unshuffled_original_lt | 2977757 | 1,159,661,742 | 8.8G | unshuffled_deduplicated_lt | 1737411 | 516,183,525 | 3.9G |
| Lojban | jbo | unshuffled_original_jbo | 832 | 154,330 | 736K | unshuffled_deduplicated_jbo | 617 | 141,973 | 678K |
| Lombard | lmo | unshuffled_original_lmo | 1401 | 75,229 | 443K | unshuffled_deduplicated_lmo | 1374 | 73,665 | 433K |
| Low German | nds | unshuffled_original_nds | 18174 | 2,906,347 | 18M | unshuffled_deduplicated_nds | 8714 | 2,146,417 | 13M |
| Lower Sorbian | dsb | unshuffled_original_dsb | 65 | 1,787 | 13K | unshuffled_deduplicated_dsb | 37 | 966 | 7.1K |
| Luxembourgish | lb | unshuffled_original_lb | 34807 | 4,403,577 | 29M | unshuffled_deduplicated_lb | 21735 | 3,087,650 | 21M |
| Macedonian | mk | unshuffled_original_mk | 437871 | 189,289,873 | 2.1G | unshuffled_deduplicated_mk | 299457 | 102,849,595 | 1.2G |
| Maithili | mai | unshuffled_original_mai | 123 | 69,161 | 317K | unshuffled_deduplicated_mai | 25 | 874 | 11K |
| Malagasy | mg | unshuffled_original_mg | 17957 | 3,068,360 | 21M | unshuffled_deduplicated_mg | 13343 | 1,872,044 | 13M |
| Malay | ms | unshuffled_original_ms | 534016 | 16,696,882 | 111M | unshuffled_deduplicated_ms | 183443 | 6,045,753 | 42M |
| Malayalam | ml | unshuffled_original_ml | 603937 | 189,534,472 | 4.9G | unshuffled_deduplicated_ml | 453904 | 95,892,551 | 2.5G |
| Maltese | mt | unshuffled_original_mt | 26598 | 2,995,654 | 24M | unshuffled_deduplicated_mt | 16383 | 2,163,358 | 17M |
| Marathi | mr | unshuffled_original_mr | 326804 | 162,609,404 | 2.7G | unshuffled_deduplicated_mr | 212556 | 82,130,803 | 1.4G |
| Mazanderani | mzn | unshuffled_original_mzn | 1055 | 73,870 | 691K | unshuffled_deduplicated_mzn | 917 | 64,481 | 602K |
| Minangkabau | min | unshuffled_original_min | 220 | 5,682 | 608K | unshuffled_deduplicated_min | 166 | 4,825 | 310K |
| Mingrelian | xmf | unshuffled_original_xmf | 3783 | 299,098 | 5.8M | unshuffled_deduplicated_xmf | 2418 | 228,629 | 4.4M |
| Mirandese | mwl | unshuffled_original_mwl | 8 | 171 | 1.2K | unshuffled_deduplicated_mwl | 7 | 152 | 1.1K |
| Modern Greek | el | unshuffled_original_el | 10425596 | 5,479,180,137 | 62G | unshuffled_deduplicated_el | 6521169 | 2,412,419,435 | 27G |
| Mongolian | mn | unshuffled_original_mn | 395605 | 181,307,167 | 2.2G | unshuffled_deduplicated_mn | 197878 | 68,362,013 | 838M |
| Nahuatl languages | nah | unshuffled_original_nah | 61 | 1,234 | 12K | unshuffled_deduplicated_nah | 58 | 1,193 | 11K |
| Neapolitan | nap | unshuffled_original_nap | 73 | 5,282 | 17K | unshuffled_deduplicated_nap | 55 | 4,147 | 13K |
| Nepali | ne | unshuffled_original_ne | 299938 | 107,448,208 | 1.8G | unshuffled_deduplicated_ne | 219334 | 71,628,317 | 1.2G |
| Newari | new | unshuffled_original_new | 4696 | 564,697 | 5.5M | unshuffled_deduplicated_new | 2126 | 288,995 | 4.1M |
| Northern Frisian | frr | unshuffled_original_frr | 7 | 1,516 | 4.4K | unshuffled_deduplicated_frr | 7 | 1,516 | 4.4K |
| Northern Luri | lrc | unshuffled_original_lrc | 88 | 8,022 | 76K | unshuffled_deduplicated_lrc | 72 | 6,740 | 63K |
| Norwegian | no | unshuffled_original_no | 5546211 | 1,344,326,388 | 8.0G | unshuffled_deduplicated_no | 3229940 | 804,894,377 | 4.7G |
| Norwegian Nynorsk | nn | unshuffled_original_nn | 185884 | 14,764,980 | 85M | unshuffled_deduplicated_nn | 109118 | 9,435,139 | 54M |
| Occitan | oc | unshuffled_original_oc | 10709 | 750,301 | 5.8M | unshuffled_deduplicated_oc | 6485 | 512,678 | 3.7M |
| Oriya | or | unshuffled_original_or | 59463 | 14,938,567 | 248M | unshuffled_deduplicated_or | 44230 | 11,321,740 | 188M |
| Ossetian | os | unshuffled_original_os | 5213 | 1,031,268 | 13M | unshuffled_deduplicated_os | 2559 | 878,765 | 11M |
| Pampanga | pam | unshuffled_original_pam | 3 | 130 | 760 | unshuffled_deduplicated_pam | 1 | 52 | 304 |
| Panjabi | pa | unshuffled_original_pa | 127467 | 61,847,806 | 763M | unshuffled_deduplicated_pa | 87235 | 37,555,835 | 460M |
| Persian | fa | unshuffled_original_fa | 13704702 | 9,096,554,121 | 79G | unshuffled_deduplicated_fa | 8203495 | 4,363,505,319 | 38G |
| Piemontese | pms | unshuffled_original_pms | 3225 | 362,013 | 2.1M | unshuffled_deduplicated_pms | 2859 | 337,246 | 1.9M |
| Polish | pl | unshuffled_original_pl | 35440972 | 15,277,255,137 | 109G | unshuffled_deduplicated_pl | 20682611 | 6,708,709,674 | 47G |
| Portuguese | pt | unshuffled_original_pt | 42114520 | 20,641,903,898 | 124G | unshuffled_deduplicated_pt | 26920397 | 10,751,156,918 | 64G |
| Pushto | ps | unshuffled_original_ps | 98216 | 46,559,441 | 361M | unshuffled_deduplicated_ps | 67921 | 31,347,348 | 242M |
| Quechua | qu | unshuffled_original_qu | 452 | 10,186 | 78K | unshuffled_deduplicated_qu | 411 | 8,691 | 67K |
| Romanian | ro | unshuffled_original_ro | 9387265 | 3,984,317,058 | 25G | unshuffled_deduplicated_ro | 5044757 | 1,741,794,069 | 11G |
| Romansh | rm | unshuffled_original_rm | 41 | 1,093 | 7.4K | unshuffled_deduplicated_rm | 34 | 960 | 6.5K |
| Russia Buriat | bxr | unshuffled_original_bxr | 42 | 963 | 13K | unshuffled_deduplicated_bxr | 36 | 809 | 11K |
| Russian | ru | unshuffled_original_ru | 161836003 | 92,522,407,837 | 1.2T | unshuffled_deduplicated_ru | 115954598 | 46,692,691,520 | 568G |
| Sanskrit | sa | unshuffled_original_sa | 14291 | 4,331,569 | 93M | unshuffled_deduplicated_sa | 7121 | 1,713,930 | 37M |
| Scottish Gaelic | gd | unshuffled_original_gd | 5799 | 310,689 | 1.9M | unshuffled_deduplicated_gd | 3883 | 207,110 | 1.3M |
| Serbian | sr | unshuffled_original_sr | 1013619 | 364,395,411 | 3.9G | unshuffled_deduplicated_sr | 645747 | 207,561,168 | 2.2G |
| Serbo-Croatian | sh | unshuffled_original_sh | 36700 | 5,292,184 | 25M | unshuffled_deduplicated_sh | 17610 | 1,040,573 | 5.8M |
| Sicilian | scn | unshuffled_original_scn | 21 | 554 | 3.3K | unshuffled_deduplicated_scn | 17 | 468 | 2.8K |
| Sindhi | sd | unshuffled_original_sd | 44280 | 43,530,158 | 347M | unshuffled_deduplicated_sd | 33925 | 33,028,015 | 263M |
| Sinhala | si | unshuffled_original_si | 203082 | 93,053,465 | 1.4G | unshuffled_deduplicated_si | 120684 | 50,864,857 | 802M |
| Slovak | sk | unshuffled_original_sk | 5492194 | 1,322,247,763 | 9.1G | unshuffled_deduplicated_sk | 2820821 | 656,346,179 | 4.5G |
| Slovenian | sl | unshuffled_original_sl | 1746604 | 387,399,700 | 2.5G | unshuffled_deduplicated_sl | 886223 | 193,926,684 | 1.3G |
| Somali | so | unshuffled_original_so | 156 | 1,202 | 61K | unshuffled_deduplicated_so | 42 | 472 | 16K |
| South Azerbaijani | azb | unshuffled_original_azb | 15446 | 2,175,054 | 27M | unshuffled_deduplicated_azb | 9985 | 1,528,709 | 19M |
| Spanish | es | unshuffled_original_es | 88199221 | 47,545,122,279 | 278G | unshuffled_deduplicated_es | 56326016 | 25,928,290,729 | 149G |
| Sundanese | su | unshuffled_original_su | 805 | 30,321 | 211K | unshuffled_deduplicated_su | 511 | 20,278 | 141K |
| Swahili | sw | unshuffled_original_sw | 41986 | 2,211,927 | 13M | unshuffled_deduplicated_sw | 24803 | 1,376,963 | 8.1M |
| Swedish | sv | unshuffled_original_sv | 17395625 | 7,155,994,312 | 44G | unshuffled_deduplicated_sv | 11014487 | 4,106,120,608 | 25G |
| Tagalog | tl | unshuffled_original_tl | 458206 | 98,949,299 | 573M | unshuffled_deduplicated_tl | 294132 | 70,121,601 | 407M |
| Tajik | tg | unshuffled_original_tg | 89002 | 31,758,142 | 379M | unshuffled_deduplicated_tg | 56259 | 21,029,893 | 249M |
| Tamil | ta | unshuffled_original_ta | 1263280 | 420,537,132 | 9.3G | unshuffled_deduplicated_ta | 833101 | 226,013,330 | 5.1G |
| Tatar | tt | unshuffled_original_tt | 135923 | 51,034,893 | 670M | unshuffled_deduplicated_tt | 82738 | 23,825,695 | 305M |
| Telugu | te | unshuffled_original_te | 475703 | 123,711,517 | 2.5G | unshuffled_deduplicated_te | 312644 | 79,094,167 | 1.6G |
| Thai | th | unshuffled_original_th | 6064129 | 951,743,087 | 36G | unshuffled_deduplicated_th | 3749826 | 368,965,202 | 16G |
| Tibetan | bo | unshuffled_original_bo | 26795 | 1,483,589 | 187M | unshuffled_deduplicated_bo | 15762 | 936,556 | 138M |
| Turkish | tr | unshuffled_original_tr | 18535253 | 7,577,388,700 | 60G | unshuffled_deduplicated_tr | 11596446 | 3,365,734,289 | 27G |
| Turkmen | tk | unshuffled_original_tk | 6456 | 1,113,869 | 11M | unshuffled_deduplicated_tk | 4694 | 752,326 | 6.8M |
| Tuvinian | tyv | unshuffled_original_tyv | 34 | 759 | 12K | unshuffled_deduplicated_tyv | 24 | 540 | 7.9K |
| Uighur | ug | unshuffled_original_ug | 22255 | 8,657,141 | 122M | unshuffled_deduplicated_ug | 15503 | 5,852,225 | 83M |
| Ukrainian | uk | unshuffled_original_uk | 12973467 | 4,204,381,276 | 53G | unshuffled_deduplicated_uk | 7782375 | 2,252,380,351 | 28G |
| Upper Sorbian | hsb | unshuffled_original_hsb | 7959 | 545,351 | 4.2M | unshuffled_deduplicated_hsb | 3084 | 236,867 | 1.8M |
| Urdu | ur | unshuffled_original_ur | 638596 | 331,817,982 | 2.7G | unshuffled_deduplicated_ur | 428674 | 218,030,228 | 1.7G |
| Uzbek | uz | unshuffled_original_uz | 27537 | 2,450,256 | 21M | unshuffled_deduplicated_uz | 15074 | 1,381,644 | 12M |
| Venetian | vec | unshuffled_original_vec | 73 | 3,492 | 18K | unshuffled_deduplicated_vec | 64 | 3,199 | 17K |
| Vietnamese | vi | unshuffled_original_vi | 14898250 | 12,036,845,359 | 68G | unshuffled_deduplicated_vi | 9897709 | 5,577,159,843 | 32G |
| Volapük | vo | unshuffled_original_vo | 3366 | 321,121 | 2.0M | unshuffled_deduplicated_vo | 3317 | 318,568 | 2.0M |
| Walloon | wa | unshuffled_original_wa | 1001 | 50,720 | 273K | unshuffled_deduplicated_wa | 677 | 37,543 | 203K |
| Waray | war | unshuffled_original_war | 9760 | 397,315 | 2.5M | unshuffled_deduplicated_war | 9161 | 336,311 | 2.2M |
| Welsh | cy | unshuffled_original_cy | 157698 | 37,422,441 | 213M | unshuffled_deduplicated_cy | 98225 | 23,574,673 | 133M |
| Western Frisian | fy | unshuffled_original_fy | 33053 | 5,691,077 | 35M | unshuffled_deduplicated_fy | 20661 | 4,223,816 | 26M |
| Western Mari | mrj | unshuffled_original_mrj | 757 | 93,338 | 1.2M | unshuffled_deduplicated_mrj | 669 | 87,780 | 1.1M |
| Western Panjabi | pnb | unshuffled_original_pnb | 4599 | 1,426,986 | 12M | unshuffled_deduplicated_pnb | 3463 | 1,111,112 | 9.0M |
| Wu Chinese | wuu | unshuffled_original_wuu | 214 | 11,189 | 109K | unshuffled_deduplicated_wuu | 64 | 4,333 | 32K |
| Yakut | sah | unshuffled_original_sah | 22301 | 2,547,623 | 42M | unshuffled_deduplicated_sah | 8555 | 1,789,174 | 26M |
| Yiddish | yi | unshuffled_original_yi | 59364 | 13,834,320 | 141M | unshuffled_deduplicated_yi | 32919 | 8,212,970 | 84M |
| Yoruba | yo | unshuffled_original_yo | 214 | 8,906 | 55K | unshuffled_deduplicated_yo | 49 | 3,518 | 27K |
| Yue Chinese | yue | unshuffled_original_yue | 11 | 186 | 3.7K | unshuffled_deduplicated_yue | 7 | 128 | 2.2K |
</details>
## Dataset Creation
### Curation Rationale
OSCAR was constructed new pipeline derived from the [fastText's one](https://github.com/facebookresearch/fastText), called [_goclassy_](https://github.com/pjox/goclassy). Goclassy reuses the [fastText linear classifier](https://fasttext.cc) and the pre-trained fastText model for language recognition, but it completely rewrites and parallelises their pipeline in an asynchronous manner.
The order of operations is more or less the same as in the fastText pre-processing pipeline but instead of clustering multiple operations into a single blocking process, a worker is launched for each operation but bounding the number of possible parallel operations at a given time by the number of available threads instead of the number of CPUs. Goclassy is implemented in the [Go programming language](https://golang.org/) so it lets the [Go runtime](https://golang.org/src/runtime/mprof.go) handle the scheduling of the processes. Thus the goclassy's pipeline one does not have to wait for a whole WET file to download, decompress and classify in order to start downloading and processing the next one, a new file will start downloading and processing as soon as the scheduler is able to allocate a new process.
Filtering and cleaning processes at line level are done before feeding each line to the classifier. Lines shorter than 100 UTF-8 characters and lines containing invalid UTF-8 characters are discarted and are not classified. After all files are proccesed the deduplicated versions are constructed and everything is then splitted in shards and compressed.
### Source Data
#### Initial Data Collection and Normalization
[Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies.
Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics.
To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR, the **November 2018** snapshot was used. It surpasses 20TB of uncompressed data and contains more than 50 thousand plain text files where each file consists of the plain text from multiple websites along its metadata header.
#### Who are the source language producers?
The data comes from multiple web pages in a large variety of languages.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models.
## Considerations for Using the Data
### Social Impact of Dataset
OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures.
### Discussion of Biases
OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models.
### Other Known Limitations
The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571).
## Additional Information
### Dataset Curators
The corpus was put together by [Pedro J. Ortiz](https://pjortiz.eu/), [Benoît Sagot](http://pauillac.inria.fr/~sagot/), and [Laurent Romary](https://cv.archives-ouvertes.fr/laurentromary), during work done at [Inria](https://www.inria.fr/en), particularly at the [ALMAnaCH team](https://team.inria.fr/almanach/).
### Licensing Information
These data are released under this licensing scheme
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR
This work is published from: France.
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
```
@inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
}
```
### Contributions
Thanks to [@pjox](https://github.com/pjox) and [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
llm-lens/descriptors-text-davinci-003 | 2023-06-29T02:39:27.000Z | [
"region:us"
] | llm-lens | null | null | null | 0 | 6,614 | ---
dataset_info:
features:
- name: vocab
dtype: string
- name: descriptions
sequence: string
- name: prompt_descriptions
sequence: string
splits:
- name: birdsnap
num_bytes: 322488
num_examples: 500
- name: caltech101
num_bytes: 56880
num_examples: 102
- name: cifar100
num_bytes: 54081
num_examples: 100
- name: coco
num_bytes: 45186
num_examples: 80
- name: dtd
num_bytes: 25204
num_examples: 47
- name: fgvc
num_bytes: 74126
num_examples: 100
- name: food101
num_bytes: 58525
num_examples: 101
- name: full
num_bytes: 3000578
num_examples: 4951
- name: imagenet21k
num_bytes: 604897
num_examples: 998
- name: lvis
num_bytes: 679195
num_examples: 1198
- name: oxfordflowers
num_bytes: 58560
num_examples: 102
- name: oxfordpets
num_bytes: 22322
num_examples: 37
- name: stanfordcars
num_bytes: 157786
num_examples: 196
- name: sun397
num_bytes: 243017
num_examples: 362
- name: visualgenome
num_bytes: 1092697
num_examples: 1913
download_size: 3257945
dataset_size: 6495542
---
# Dataset Card for "descriptors-text-davinci-003"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_GOAT-AI__GOAT-7B-Community | 2023-09-22T17:15:05.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 6,469 | ---
pretty_name: Evaluation run of GOAT-AI/GOAT-7B-Community
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [GOAT-AI/GOAT-7B-Community](https://huggingface.co/GOAT-AI/GOAT-7B-Community)\
\ 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_GOAT-AI__GOAT-7B-Community\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T17:14:52.967997](https://huggingface.co/datasets/open-llm-leaderboard/details_GOAT-AI__GOAT-7B-Community/blob/main/results_2023-09-22T17-14-52.967997.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\": 0.005243288590604027,\n\
\ \"em_stderr\": 0.0007396052260778031,\n \"f1\": 0.06909395973154382,\n\
\ \"f1_stderr\": 0.0015832414439852427,\n \"acc\": 0.3838492484021702,\n\
\ \"acc_stderr\": 0.009135888573374731\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.005243288590604027,\n \"em_stderr\": 0.0007396052260778031,\n\
\ \"f1\": 0.06909395973154382,\n \"f1_stderr\": 0.0015832414439852427\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04473085670962851,\n \
\ \"acc_stderr\": 0.005693886131407048\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7229676400947119,\n \"acc_stderr\": 0.012577891015342414\n\
\ }\n}\n```"
repo_url: https://huggingface.co/GOAT-AI/GOAT-7B-Community
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|arc:challenge|25_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T17_14_52.967997
path:
- '**/details_harness|drop|3_2023-09-22T17-14-52.967997.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T17-14-52.967997.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T17_14_52.967997
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-14-52.967997.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-14-52.967997.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hellaswag|10_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-25T12:51:32.230763.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-25T12:51:32.230763.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-25T12:51:32.230763.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T17_14_52.967997
path:
- '**/details_harness|winogrande|5_2023-09-22T17-14-52.967997.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T17-14-52.967997.parquet'
- config_name: results
data_files:
- split: 2023_07_25T12_51_32.230763
path:
- results_2023-07-25T12:51:32.230763.parquet
- split: 2023_09_22T17_14_52.967997
path:
- results_2023-09-22T17-14-52.967997.parquet
- split: latest
path:
- results_2023-09-22T17-14-52.967997.parquet
---
# Dataset Card for Evaluation run of GOAT-AI/GOAT-7B-Community
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/GOAT-AI/GOAT-7B-Community
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [GOAT-AI/GOAT-7B-Community](https://huggingface.co/GOAT-AI/GOAT-7B-Community) 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_GOAT-AI__GOAT-7B-Community",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T17:14:52.967997](https://huggingface.co/datasets/open-llm-leaderboard/details_GOAT-AI__GOAT-7B-Community/blob/main/results_2023-09-22T17-14-52.967997.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": 0.005243288590604027,
"em_stderr": 0.0007396052260778031,
"f1": 0.06909395973154382,
"f1_stderr": 0.0015832414439852427,
"acc": 0.3838492484021702,
"acc_stderr": 0.009135888573374731
},
"harness|drop|3": {
"em": 0.005243288590604027,
"em_stderr": 0.0007396052260778031,
"f1": 0.06909395973154382,
"f1_stderr": 0.0015832414439852427
},
"harness|gsm8k|5": {
"acc": 0.04473085670962851,
"acc_stderr": 0.005693886131407048
},
"harness|winogrande|5": {
"acc": 0.7229676400947119,
"acc_stderr": 0.012577891015342414
}
}
```
### 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] |
swag | 2023-01-25T14:45:08.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1808.05326",
"region:us"
] | null | Given a partial description like "she opened the hood of the car,"
humans can reason about the situation and anticipate what might come
next ("then, she examined the engine"). SWAG (Situations With Adversarial Generations)
is a large-scale dataset for this task of grounded commonsense
inference, unifying natural language inference and physically grounded reasoning.
The dataset consists of 113k multiple choice questions about grounded situations
(73k training, 20k validation, 20k test).
Each question is a video caption from LSMDC or ActivityNet Captions,
with four answer choices about what might happen next in the scene.
The correct answer is the (real) video caption for the next event in the video;
the three incorrect answers are adversarially generated and human verified,
so as to fool machines but not humans. SWAG aims to be a benchmark for
evaluating grounded commonsense NLI and for learning representations.
The full data contain more information,
but the regular configuration will be more interesting for modeling
(note that the regular data are shuffled). The test set for leaderboard submission
is under the regular configuration. | @inproceedings{zellers2018swagaf,
title={SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference},
author={Zellers, Rowan and Bisk, Yonatan and Schwartz, Roy and Choi, Yejin},
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year={2018}
} | null | 11 | 6,407 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: swag
pretty_name: Situations With Adversarial Generations
dataset_info:
- config_name: regular
features:
- name: video-id
dtype: string
- name: fold-ind
dtype: string
- name: startphrase
dtype: string
- name: sent1
dtype: string
- name: sent2
dtype: string
- name: gold-source
dtype: string
- name: ending0
dtype: string
- name: ending1
dtype: string
- name: ending2
dtype: string
- name: ending3
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
splits:
- name: train
num_bytes: 30274672
num_examples: 73546
- name: validation
num_bytes: 8451771
num_examples: 20006
- name: test
num_bytes: 8417644
num_examples: 20005
download_size: 43954806
dataset_size: 47144087
- config_name: full
features:
- name: video-id
dtype: string
- name: fold-ind
dtype: string
- name: startphrase
dtype: string
- name: gold-ending
dtype: string
- name: distractor-0
dtype: string
- name: distractor-1
dtype: string
- name: distractor-2
dtype: string
- name: distractor-3
dtype: string
- name: gold-source
dtype: string
- name: gold-type
dtype: string
- name: distractor-0-type
dtype: string
- name: distractor-1-type
dtype: string
- name: distractor-2-type
dtype: string
- name: distractor-3-type
dtype: string
- name: sent1
dtype: string
- name: sent2
dtype: string
splits:
- name: train
num_bytes: 34941649
num_examples: 73546
- name: validation
num_bytes: 9832603
num_examples: 20006
download_size: 40537624
dataset_size: 44774252
---
# Dataset Card for Situations With Adversarial Generations
## 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:** [SWAG AF](https://rowanzellers.com/swag/)
- **Repository:** [Github repository](https://github.com/rowanz/swagaf/tree/master/data)
- **Paper:** [SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference](https://arxiv.org/abs/1808.05326)
- **Leaderboard:** [SWAG Leaderboard](https://leaderboard.allenai.org/swag)
- **Point of Contact:** [Rowan Zellers](https://rowanzellers.com/#contact)
### Dataset Summary
Given a partial description like "she opened the hood of the car,"
humans can reason about the situation and anticipate what might come
next ("then, she examined the engine"). SWAG (Situations With Adversarial Generations)
is a large-scale dataset for this task of grounded commonsense
inference, unifying natural language inference and physically grounded reasoning.
The dataset consists of 113k multiple choice questions about grounded situations
(73k training, 20k validation, 20k test).
Each question is a video caption from LSMDC or ActivityNet Captions,
with four answer choices about what might happen next in the scene.
The correct answer is the (real) video caption for the next event in the video;
the three incorrect answers are adversarially generated and human verified,
so as to fool machines but not humans. SWAG aims to be a benchmark for
evaluating grounded commonsense NLI and for learning representations.
### Supported Tasks and Leaderboards
The dataset introduces the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
The `regular` configuration should be used for modeling. An example looks like this:
```
{
"video-id": "anetv_dm5WXFiQZUQ",
"fold-ind": "18419",
"startphrase", "He rides the motorcycle down the hall and into the elevator. He",
"sent1": "He rides the motorcycle down the hall and into the elevator."
"sent2": "He",
"gold-source": "gold",
"ending0": "looks at a mirror in the mirror as he watches someone walk through a door.",
"ending1": "stops, listening to a cup of coffee with the seated woman, who's standing.",
"ending2": "exits the building and rides the motorcycle into a casino where he performs several tricks as people watch.",
"ending3": "pulls the bag out of his pocket and hands it to someone's grandma.",
"label": 2,
}
```
Note that the test are reseved for blind submission on the leaderboard.
The full train and validation sets provide more information regarding the collection process.
### Data Fields
- `video-id`: identification
- `fold-ind`: identification
- `startphrase`: the context to be filled
- `sent1`: the first sentence
- `sent2`: the start of the second sentence (to be filled)
- `gold-source`: generated or comes from the found completion
- `ending0`: first proposition
- `ending1`: second proposition
- `ending2`: third proposition
- `ending3`: fourth proposition
- `label`: the correct proposition
More info concerning the fields can be found [on the original repo](https://github.com/rowanz/swagaf/tree/master/data).
### Data Splits
The dataset consists of 113k multiple choice questions about grounded situations: 73k for training, 20k for validation, and 20k for (blind) test.
## Dataset Creation
### Curation Rationale
The authors seek dataset diversity while minimizing annotation artifacts, conditional stylistic patterns such as length and word-preference biases. To avoid introducing easily “gamed” patterns, they introduce Adversarial Filtering (AF), a generally- applicable treatment involving the iterative refinement of a set of assignments to increase the entropy under a chosen model family. The dataset is then human verified by paid crowdsourcers.
### Source Data
This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)
#### Initial Data Collection and Normalization
The dataset is derived from pairs of consecutive video captions from [ActivityNet Captions](https://cs.stanford.edu/people/ranjaykrishna/densevid/) and the [Large Scale Movie Description Challenge](https://sites.google.com/site/describingmovies/). The two datasets are slightly different in nature and allow us to achieve broader coverage: ActivityNet contains 20k YouTube clips containing one of 203 activity types (such as doing gymnastics or playing guitar); LSMDC consists of 128k movie captions (audio descriptions and scripts).
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
Annotations are first machine generated and then adversarially filtered. Finally, the remaining examples are human-verified by paid crowdsourcers.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Unknown
### Citation Information
```
@inproceedings{zellers2018swagaf,
title={SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference},
author={Zellers, Rowan and Bisk, Yonatan and Schwartz, Roy and Choi, Yejin},
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year={2018}
}
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. |
cosmos_qa | 2023-04-05T10:02:42.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1909.00277",
"region:us"
] | null | Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context | @inproceedings{huang-etal-2019-cosmos,
title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
author = "Huang, Lifu and
Le Bras, Ronan and
Bhagavatula, Chandra and
Choi, Yejin",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1243",
doi = "10.18653/v1/D19-1243",
pages = "2391--2401",
} | null | 9 | 6,357 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: CosmosQA
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
paperswithcode_id: cosmosqa
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answer0
dtype: string
- name: answer1
dtype: string
- name: answer2
dtype: string
- name: answer3
dtype: string
- name: label
dtype: int32
splits:
- name: train
num_bytes: 17159918
num_examples: 25262
- name: test
num_bytes: 5121479
num_examples: 6963
- name: validation
num_bytes: 2186987
num_examples: 2985
download_size: 24399475
dataset_size: 24468384
---
# Dataset Card for "cosmos_qa"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://wilburone.github.io/cosmos/](https://wilburone.github.io/cosmos/)
- **Repository:** https://github.com/wilburOne/cosmosqa/
- **Paper:** [Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://arxiv.org/abs/1909.00277)
- **Point of Contact:** [Lifu Huang](mailto:warrior.fu@gmail.com)
- **Size of downloaded dataset files:** 24.40 MB
- **Size of the generated dataset:** 24.51 MB
- **Total amount of disk used:** 48.91 MB
### Dataset Summary
Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 24.40 MB
- **Size of the generated dataset:** 24.51 MB
- **Total amount of disk used:** 48.91 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answer0": "If he gets married in the church he wo nt have to get a divorce .",
"answer1": "He wants to get married to a different person .",
"answer2": "He wants to know if he does nt like this girl can he divorce her ?",
"answer3": "None of the above choices .",
"context": "\"Do i need to go for a legal divorce ? I wanted to marry a woman but she is not in the same religion , so i am not concern of th...",
"id": "3BFF0DJK8XA7YNK4QYIGCOG1A95STE##3180JW2OT5AF02OISBX66RFOCTG5J7##A2LTOS0AZ3B28A##Blog_56156##q1_a1##378G7J1SJNCDAAIN46FM2P7T6KZEW2",
"label": 1,
"question": "Why is this person asking about divorce ?"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answer0`: a `string` feature.
- `answer1`: a `string` feature.
- `answer2`: a `string` feature.
- `answer3`: a `string` feature.
- `label`: a `int32` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|25262| 2985|6963|
## 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
As reported via email by Yejin Choi, the dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
### Citation Information
```
@inproceedings{huang-etal-2019-cosmos,
title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
author = "Huang, Lifu and
Le Bras, Ronan and
Bhagavatula, Chandra and
Choi, Yejin",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1243",
doi = "10.18653/v1/D19-1243",
pages = "2391--2401",
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
tau/zero_scrolls | 2023-06-30T17:21:02.000Z | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_ids:multiple-choice-qa",
"language:en",
"query-based-summarization",
"long-texts",
"arxiv:2104.02112",
"arxiv:2104.07091",
"arxiv:2104.05938",
"arxiv:2205.11465",
"arxiv:2105.03011",
"arxiv:1712.07040",
"arxiv:2112.08608",
"arxiv:2108.00573",
"arxiv:2305.14196",
"region:us"
] | tau | ZeroSCROLLS: Zero-Shot CompaRison Over Long Language Sequences.
A zero shot benchmark for long text reasoning.
https://zero.scrolls-benchmark.com/ | @misc{shaham2023zeroscrolls,
title={ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding},
author={Uri Shaham and Maor Ivgi and Avia Efrat and Jonathan Berant and Omer Levy},
year={2023},
eprint={2305.14196},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Note that each ZeroSCROLLS task has its own citation. Please see the source to
get the correct citation for each one. | null | 4 | 6,326 | ---
language:
- en
task_categories:
- question-answering
- summarization
- text-generation
task_ids:
- multiple-choice-qa
tags:
- query-based-summarization
- long-texts
---
## Dataset Description
- **Homepage:** [ZeroSCROLLS](https://www.zero.scrolls-benchmark.com/)
- **Leaderboard:** [Leaderboard](https://www.zero.scrolls-benchmark.com/leaderboard)
- **Point of Contact:** [scrolls-benchmark-contact@googlegroups.com](scrolls-benchmark-contact@googlegroups.com)
# Dataset Card for ZeroSCROLLS
## Overview
ZeroSCROLLS is a zero-shot benchmark for natural language understanding over long texts.
The validation sets contain only ~20 examples per task and are meant for eyeballing alone.
## Leaderboard
The ZeroSCROLLS benchmark leaderboard can be found [here](https://www.zero.scrolls-benchmark.com/leaderboard).
## Tasks
ZeroSCROLLS contains the following tasks:
#### GovReport ([Huang et al., 2021](https://arxiv.org/pdf/2104.02112.pdf))
GovReport is a summarization dataset of reports addressing various national policy issues published by the
Congressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary.
The reports and their summaries are longer than their equivalents in other popular long-document summarization datasets;
for example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively.
#### SummScreenFD ([Chen et al., 2022](https://arxiv.org/pdf/2104.07091.pdf))
SummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones).
Given a transcript of a specific episode, the goal is to produce the episode's recap.
The original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts.
For SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows,
making it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows.
Community-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze.
#### QMSum ([Zhong et al., 2021](https://arxiv.org/pdf/2104.05938.pdf))
QMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains.
The corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control,
and committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues.
Annotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions,
while ensuring that the relevant text for answering each query spans at least 200 words or 10 turns.
#### SQuALITY ([Wang et al., 2022](https://arxiv.org/pdf/2205.11465.pdf))
SQuALITY (Wang et al., 2022) is a question-focused summarization dataset, where given a story from Project Gutenberg,
the task is to produce a summary of the story or aspects of it based on a guiding question.
The questions and summaries are original and crowdsourced; experienced writers were guided to design questions that require reading significant parts of the story to answer correctly.
#### Qasper ([Dasigi et al., 2021](https://arxiv.org/pdf/2105.03011.pdf))
Qasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC).
Questions were written by NLP practitioners after reading only the title and abstract of the papers,
while another set of NLP practitioners annotated the answers given the entire document.
Qasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones.
#### NarrativeQA ([Kočiský et al., 2018](https://arxiv.org/pdf/1712.07040.pdf))
NarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites.
Annotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs,
resulting in about 30 questions and answers for each of the 1,567 books and scripts.
They were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast.
Each question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical).
#### QuALITY ([Pang et al., 2022](https://arxiv.org/pdf/2112.08608.pdf))
QuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg,
the Open American National Corpus, and more.
Experienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them,
human annotators must read large portions of the given document.
Reference answers were then calculated using the majority vote between of the annotators and writer's answers.
To measure the difficulty of their questions, Pang et al. conducted a speed validation process,
where another set of annotators were asked to answer questions given only a short period of time to skim through the document.
As a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer.
#### MuSiQue ([Trivedi et al., 2022](https://arxiv.org/pdf/2108.00573.pdf))
MuSiQue is a multi-hop question answering dataset, where the inputs are 20 Wikipedia paragraphs and a question that requires multiple hops between different paragraphs.
In the original dataset, each question also has an unanswerable twin question, where the correct answer is not present in the paragraphs.
#### SpaceDigest (New)
SpaceDigest is a new sentiment aggregation task. Given 50 hotel reviews (without their ratings) from the Space dataset (Angelidis et al., 2021), the task is to determine the percentage of positive reviews.
#### BookSumSort (New)
BookSumSort is a new task based on the BookSum dataset (Kry ́sci ́nski et al., 2022), which contains summaries of chapters (or parts) of novels, plays, and long poems from various sources.
Given a shuffled list of chapter summaries, the task is to reorder them according to the original order of summaries in BookSum.
## Data Fields
Most datasets in the benchmark are in the same input-output format
- `input`: a `string` feature. The input document.
- `output`: this feature is always None, as ZeroSCROLLS contains only test sets.
- `id`: a `string` feature. Unique per input.
- `pid`: a `string` feature, identical to 'id`. Facilitates evaluating tasks with multiple refrences per input.
- `document_start_index`: an `int32` feature. Character index that enables easy parsing of the context document.
- `document_end_index`: an `int32` feature. Character index that enables easy parsing of the context document.
- `query_start_index`: an `int32` feature. Character index that enables easy parsing of the query, if exists.
- `query_end_index`: an `int32` feature. Character index that enables easy parsing of the query, if exists.
- `truncation_seperator`: a `string` feature. The string used to append to a trimmed context document, mentioning the context was trimmed.
Datasets containing multiple documents inside the `input` feature are MuSiQue, SpaceDigest, and BookSumSort. They also have the following feature:
- `inner_docs_start_indices`: a sequence of `int32` feature. Character indexes that enables easy parsing of the the inner documents, e.g. Reviews, of Summaries.
## Citation
If you use the ZeroSCROLLS data, **please make sure to cite all of the original dataset papers.** [[bibtex](https://zero-scrolls-tau.s3.us-east-2.amazonaws.com/zero_scrolls_datasets.bib)]
```
@misc{shaham2023zeroscrolls,
title={ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding},
author={Uri Shaham and Maor Ivgi and Avia Efrat and Jonathan Berant and Omer Levy},
year={2023},
eprint={2305.14196},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
paws | 2023-06-01T14:59:56.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:multi-input-text-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"paraphrase-identification",
"arxiv:1904.01130",
"region:us"
] | null | PAWS: Paraphrase Adversaries from Word Scrambling
This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature
the importance of modeling structure, context, and word order information for the problem
of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the
other one based on the Quora Question Pairs (QQP) dataset.
For further details, see the accompanying paper: PAWS: Paraphrase Adversaries from Word Scrambling
(https://arxiv.org/abs/1904.01130)
PAWS-QQP is not available due to license of QQP. It must be reconstructed by downloading the original
data and then running our scripts to produce the data and attach the labels.
NOTE: There might be some missing or wrong labels in the dataset and we have replaced them with -1. | @InProceedings{paws2019naacl,
title = {{PAWS: Paraphrase Adversaries from Word Scrambling}},
author = {Zhang, Yuan and Baldridge, Jason and He, Luheng},
booktitle = {Proc. of NAACL},
year = {2019}
} | null | 16 | 6,295 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
- semantic-similarity-scoring
- text-scoring
- multi-input-text-classification
paperswithcode_id: paws
pretty_name: 'PAWS: Paraphrase Adversaries from Word Scrambling'
tags:
- paraphrase-identification
dataset_info:
- config_name: labeled_final
features:
- name: id
dtype: int32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 12239978
num_examples: 49401
- name: test
num_bytes: 1987802
num_examples: 8000
- name: validation
num_bytes: 1975870
num_examples: 8000
download_size: 4687157
dataset_size: 16203650
- config_name: labeled_swap
features:
- name: id
dtype: int32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 7963651
num_examples: 30397
download_size: 2257283
dataset_size: 7963651
- config_name: unlabeled_final
features:
- name: id
dtype: int32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 157806996
num_examples: 645652
- name: validation
num_bytes: 2442173
num_examples: 10000
download_size: 47393331
dataset_size: 160249169
config_names:
- labeled_final
- labeled_swap
- unlabeled_final
---
# Dataset Card for PAWS: Paraphrase Adversaries from Word Scrambling
## 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:** [PAWS](https://github.com/google-research-datasets/paws)
- **Repository:** [PAWS](https://github.com/google-research-datasets/paws)
- **Paper:** [PAWS: Paraphrase Adversaries from Word Scrambling](https://arxiv.org/abs/1904.01130)
- **Point of Contact:** [Yuan Zhang](zhangyua@google.com)
### Dataset Summary
PAWS: Paraphrase Adversaries from Word Scrambling
This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset.
For further details, see the accompanying paper: PAWS: Paraphrase Adversaries from Word Scrambling (https://arxiv.org/abs/1904.01130)
PAWS-QQP is not available due to license of QQP. It must be reconstructed by downloading the original data and then running our scripts to produce the data and attach the labels.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
Below are two examples from the dataset:
| | Sentence 1 | Sentence 2 | Label |
| :-- | :---------------------------- | :---------------------------- | :---- |
| (1) | Although interchangeable, the body pieces on the 2 cars are not similar. | Although similar, the body parts are not interchangeable on the 2 cars. | 0 |
| (2) | Katz was born in Sweden in 1947 and moved to New York City at the age of 1. | Katz was born in 1947 in Sweden and moved to New York at the age of one. | 1 |
The first pair has different semantic meaning while the second pair is a paraphrase. State-of-the-art models trained on existing datasets have dismal performance on PAWS (<40% accuracy); however, including PAWS training data for these models improves their accuracy to 85% while maintaining performance on existing datasets such as the [Quora Question Pairs](https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs).
### Data Fields
This corpus contains pairs generated from Wikipedia pages, and can be downloaded
here:
* **PAWS-Wiki Labeled (Final)**: containing pairs that are generated from both word swapping and back translation methods. All pairs have human judgements on both paraphrasing and fluency and they are split into Train/Dev/Test sections.
* **PAWS-Wiki Labeled (Swap-only)**: containing pairs that have no back translation counterparts and therefore they are not included in the first set. Nevertheless, they are high-quality pairs with human judgements on both paraphrasing and fluency, and they can be included as an auxiliary training set.
* **PAWS-Wiki Unlabeled (Final)**: Pairs in this set have noisy labels without human judgments and can also be used as an auxiliary training set. They are generated from both word swapping and back translation methods.
All files are in the tsv format with four columns:
Column Name | Data
:------------ | :--------------------------
id | A unique id for each pair
sentence1 | The first sentence
sentence2 | The second sentence
(noisy_)label | (Noisy) label for each pair
Each label has two possible values: `0` indicates the pair has different meaning, while `1` indicates the pair is a paraphrase.
### Data Splits
The number of examples and the proportion of paraphrase (Yes%) pairs are shown
below:
Data | Train | Dev | Test | Yes%
:------------------ | ------: | -----: | ----: | ----:
Labeled (Final) | 49,401 | 8,000 | 8,000 | 44.2%
Labeled (Swap-only) | 30,397 | -- | -- | 9.6%
Unlabeled (Final) | 645,652 | 10,000 | -- | 50.0%
## Dataset Creation
### Curation Rationale
Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like *flights from New York to Florida* and *flights from Florida to New York*.
### Source Data
#### Initial Data Collection and Normalization
Their automatic generation method is based on two ideas. The first swaps words to generate a sentence pair with the same BOW, controlled by a language model. The second uses back translation to generate paraphrases with high BOW overlap but different word order. These two strategies generate high-quality, diverse PAWS pairs, balanced evenly between paraphrases and non-paraphrases.
#### Who are the source language producers?
Mentioned above.
### Annotations
#### Annotation process
Sentence pairs are presented to five annotators, each of which gives a binary judgment as to whether they are paraphrases or not. They chose binary judgments to make dataset have the same label schema as the QQP corpus. Overall, human agreement is high on both Quora (92.0%) and Wikipedia (94.7%) and each label only takes about 24 seconds. As such, answers are usually straight-forward to human raters.
#### 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
List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
### Licensing Information
The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
### Citation Information
```
@InProceedings{paws2019naacl,
title = {{PAWS: Paraphrase Adversaries from Word Scrambling}},
author = {Zhang, Yuan and Baldridge, Jason and He, Luheng},
booktitle = {Proc. of NAACL},
year = {2019}
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. |
nq_open | 2022-11-03T16:32:11.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|natural_questions",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | The NQ-Open task, introduced by Lee et.al. 2019,
is an open domain question answering benchmark that is derived from Natural Questions.
The goal is to predict an English answer string for an input English question.
All questions can be answered using the contents of English Wikipedia. | @article{doi:10.1162/tacl_a_00276,
author = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, Ming-Wei and Dai, Andrew M. and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav},
title = {Natural Questions: A Benchmark for Question Answering Research},
journal = {Transactions of the Association for Computational Linguistics},
volume = {7},
number = {},
pages = {453-466},
year = {2019},
doi = {10.1162/tacl_a_00276},
URL = {
https://doi.org/10.1162/tacl_a_00276
},
eprint = {
https://doi.org/10.1162/tacl_a_00276
},
abstract = { We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature. }
}
@inproceedings{lee-etal-2019-latent,
title = "Latent Retrieval for Weakly Supervised Open Domain Question Answering",
author = "Lee, Kenton and
Chang, Ming-Wei and
Toutanova, Kristina",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1612",
doi = "10.18653/v1/P19-1612",
pages = "6086--6096",
abstract = "Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn from scratch, we pre-train the retriever with an Inverse Cloze Task. We evaluate on open versions of five QA datasets. On datasets where the questioner already knows the answer, a traditional IR system such as BM25 is sufficient. On datasets where a user is genuinely seeking an answer, we show that learned retrieval is crucial, outperforming BM25 by up to 19 points in exact match.",
} | null | 5 | 6,245 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: NQ-Open
size_categories:
- 10K<n<100K
source_datasets:
- extended|natural_questions
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: null
dataset_info:
features:
- name: question
dtype: string
- name: answer
sequence: string
config_name: nq_open
splits:
- name: train
num_bytes: 6651344
num_examples: 87925
- name: validation
num_bytes: 313841
num_examples: 3610
download_size: 8913614
dataset_size: 6965185
---
# Dataset Card for nq_open
## 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://efficientqa.github.io/
- **Repository:** https://github.com/google-research-datasets/natural-questions/tree/master/nq_open
- **Paper:** https://www.aclweb.org/anthology/P19-1612.pdf
- **Leaderboard:** https://ai.google.com/research/NaturalQuestions/efficientqa
- **Point of Contact:** [Mailing List](efficientqa@googlegroups.com)
### Dataset Summary
The NQ-Open task, introduced by Lee et.al. 2019,
is an open domain question answering benchmark that is derived from Natural Questions.
The goal is to predict an English answer string for an input English question.
All questions can be answered using the contents of English Wikipedia.
### Supported Tasks and Leaderboards
Open Domain Question-Answering,
EfficientQA Leaderboard: https://ai.google.com/research/NaturalQuestions/efficientqa
### Languages
English (`en`)
## Dataset Structure
### Data Instances
```
{
"question": "names of the metropolitan municipalities in south africa",
"answer": [
"Mangaung Metropolitan Municipality",
"Nelson Mandela Bay Metropolitan Municipality",
"eThekwini Metropolitan Municipality",
"City of Tshwane Metropolitan Municipality",
"City of Johannesburg Metropolitan Municipality",
"Buffalo City Metropolitan Municipality",
"City of Ekurhuleni Metropolitan Municipality"
]
}
```
### Data Fields
- `question` - Input open domain question.
- `answer` - List of possible answers to the question
### Data Splits
- Train : 87925
- validation : 1800
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
Natural Questions contains question from aggregated queries to Google Search (Kwiatkowski et al., 2019). To gather an open version of this dataset, we only keep questions with short answers and discard the given evidence document. Answers with many tokens often resemble extractive snippets rather than canonical answers, so we discard answers with more than 5 tokens.
#### 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
Evaluating on this diverse set of question-answer pairs is crucial, because all existing datasets have inherent biases that are problematic for open domain QA systems with learned retrieval.
In the Natural Questions dataset the question askers do not already know the answer. This accurately reflects a distribution of genuine information-seeking questions.
However, annotators must separately find correct answers, which requires assistance from automatic tools and can introduce a moderate bias towards results from the tool.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
All of the Natural Questions data is released under the
[CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@article{doi:10.1162/tacl\_a\_00276,
author = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, Ming-Wei and Dai, Andrew M. and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav},
title = {Natural Questions: A Benchmark for Question Answering Research},
journal = {Transactions of the Association for Computational Linguistics},
volume = {7},
number = {},
pages = {453-466},
year = {2019},
doi = {10.1162/tacl\_a\_00276},
URL = {
https://doi.org/10.1162/tacl_a_00276
},
eprint = {
https://doi.org/10.1162/tacl_a_00276
},
abstract = { We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature. }
}
@inproceedings{lee-etal-2019-latent,
title = "Latent Retrieval for Weakly Supervised Open Domain Question Answering",
author = "Lee, Kenton and
Chang, Ming-Wei and
Toutanova, Kristina",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1612",
doi = "10.18653/v1/P19-1612",
pages = "6086--6096",
abstract = "Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn from scratch, we pre-train the retriever with an Inverse Cloze Task. We evaluate on open versions of five QA datasets. On datasets where the questioner already knows the answer, a traditional IR system such as BM25 is sufficient. On datasets where a user is genuinely seeking an answer, we show that learned retrieval is crucial, outperforming BM25 by up to 19 points in exact match.",
}
```
### Contributions
Thanks to [@Nilanshrajput](https://github.com/Nilanshrajput) for adding this dataset. |
mit-han-lab/pile-val-backup | 2023-08-21T21:37:19.000Z | [
"region:us"
] | mit-han-lab | null | null | null | 2 | 6,091 | This is a backup for the pile val dataset downloaded from here: `https://the-eye.eu/public/AI/pile/val.jsonl.zst`
Please respect the original license of the dataset. |
web_nlg | 2023-06-01T14:59:54.000Z | [
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-db_pedia",
"source_datasets:original",
"language:en",
"language:ru",
"license:cc-by-sa-3.0",
"license:cc-by-nc-sa-4.0",
"license:gfdl",
"region:us"
] | null | The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text). | @inproceedings{web_nlg,
author = {Claire Gardent and
Anastasia Shimorina and
Shashi Narayan and
Laura Perez{-}Beltrachini},
editor = {Regina Barzilay and
Min{-}Yen Kan},
title = {Creating Training Corpora for {NLG} Micro-Planners},
booktitle = {Proceedings of the 55th Annual Meeting of the
Association for Computational Linguistics,
{ACL} 2017, Vancouver, Canada, July 30 - August 4,
Volume 1: Long Papers},
pages = {179--188},
publisher = {Association for Computational Linguistics},
year = {2017},
url = {https://doi.org/10.18653/v1/P17-1017},
doi = {10.18653/v1/P17-1017}
} | null | 10 | 6,039 | ---
annotations_creators:
- found
language_creators:
- crowdsourced
language:
- en
- ru
license:
- cc-by-sa-3.0
- cc-by-nc-sa-4.0
- gfdl
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-db_pedia
- original
task_categories:
- tabular-to-text
task_ids:
- rdf-to-text
paperswithcode_id: webnlg
pretty_name: WebNLG
dataset_info:
- config_name: webnlg_challenge_2017
features:
- name: category
dtype: string
- name: size
dtype: int32
- name: eid
dtype: string
- name: original_triple_sets
sequence:
- name: otriple_set
sequence: string
- name: modified_triple_sets
sequence:
- name: mtriple_set
sequence: string
- name: shape
dtype: string
- name: shape_type
dtype: string
- name: lex
sequence:
- name: comment
dtype: string
- name: lid
dtype: string
- name: text
dtype: string
- name: lang
dtype: string
- name: test_category
dtype: string
- name: dbpedia_links
sequence: string
- name: links
sequence: string
splits:
- name: train
num_bytes: 5594812
num_examples: 6940
- name: dev
num_bytes: 706653
num_examples: 872
- name: test
num_bytes: 3122533
num_examples: 4615
download_size: 25499351
dataset_size: 9423998
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- release_v2.1
- release_v2.1_constrained
- release_v2_constrained
- release_v3.0_en
- release_v3.0_ru
- webnlg_challenge_2017
---
# Dataset Card for WebNLG
## 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:** [WebNLG challenge website](https://webnlg-challenge.loria.fr/)
- **Repository:** [WebNLG GitLab repository](https://gitlab.com/shimorina/webnlg-dataset/-/tree/master/)
- **Paper:** [Creating Training Corpora for NLG Micro-Planning](https://www.aclweb.org/anthology/P17-1017.pdf)
- **Leaderboard:** [WebNLG leaderboards](https://gerbil-nlg.dice-research.org/gerbil/webnlg2020results)
- **Point of Contact:** [anastasia.shimorina@loria.fr](anastasia.shimorina@loria.fr)
### Dataset Summary
The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
```
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
```
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text).
### Supported Tasks and Leaderboards
The dataset supports a Structured to Text task which requires a model takes a set of RDF (Resource Description Format) triples from a database (DBpedia) of the form (subject, property, object) as input and write out a natural language sentence expressing the information contained in the triples. The dataset has supportd two challenges: the [WebNLG2017](https://www.aclweb.org/anthology/W17-3518/) and [WebNLG2020](https://gerbil-nlg.dice-research.org/gerbil/webnlg2020results) challenge. Results were ordered by their [METEOR](https://huggingface.co/metrics/meteor) to the reference, but the leaderboards report a range of other metrics including [BLEU](https://huggingface.co/metrics/bleu), [BERTscore](https://huggingface.co/metrics/bertscore), and [BLEURT](https://huggingface.co/metrics/bleurt). The v3 release (`release_v3.0_en`, `release_v3.0_ru`) for the WebNLG2020 challenge also supports a semantic `parsing` task.
### Languages
All releases contain English (`en`) data. The v3 release (`release_v3.0_ru`) also contains Russian (`ru`) examples.
## Dataset Structure
### Data Instances
A typical example contains the original RDF triples in the set, a modified version which presented to crowd workers, and a set of possible verbalizations for this set of triples:
```
{'2017_test_category': '',
'category': 'Politician',
'eid': 'Id10',
'lex': {'comment': ['good', 'good', 'good'],
'lid': ['Id1', 'Id2', 'Id3'],
'text': ['World War II had Chiang Kai-shek as a commander and United States Army soldier Abner W. Sibal.',
'Abner W. Sibal served in the United States Army during the Second World War and during that war Chiang Kai-shek was one of the commanders.',
'Abner W. Sibal, served in the United States Army and fought in World War II, one of the commanders of which, was Chiang Kai-shek.']},
'modified_triple_sets': {'mtriple_set': [['Abner_W._Sibal | battle | World_War_II',
'World_War_II | commander | Chiang_Kai-shek',
'Abner_W._Sibal | militaryBranch | United_States_Army']]},
'original_triple_sets': {'otriple_set': [['Abner_W._Sibal | battles | World_War_II', 'World_War_II | commander | Chiang_Kai-shek', 'Abner_W._Sibal | branch | United_States_Army'],
['Abner_W._Sibal | militaryBranch | United_States_Army',
'Abner_W._Sibal | battles | World_War_II',
'World_War_II | commander | Chiang_Kai-shek']]},
'shape': '(X (X) (X (X)))',
'shape_type': 'mixed',
'size': 3}
```
### Data Fields
The following fields can be found in the instances:
- `category`: the category of the DBpedia entities present in the RDF triples.
- `eid`: an example ID, only unique per split per category.
- `size`: number of RDF triples in the set.
- `shape`: (since v2) Each set of RDF-triples is a tree, which is characterised by its shape and shape type. `shape` is a string representation of the tree with nested parentheses where X is a node (see [Newick tree format](https://en.wikipedia.org/wiki/Newick_format))
- `shape_type`: (since v2) is a type of the tree shape, which can be: `chain` (the object of one triple is the subject of the other); `sibling` (triples with a shared subject); `mixed` (both chain and sibling types present).
- `test_category`: (for `webnlg_challenge_2017` and `v3`) tells whether the set of RDF triples was present in the training set or not. Several splits of the test set are available: with and without references, and for RDF-to-text generation / for semantic parsing.
- `lex`: the lexicalizations, with:
- `text`: the text to be predicted.
- `lid`: a lexicalization ID, unique per example.
- `comment`: the lexicalizations were rated by crowd workers are either `good` or `bad`
- `lang`: (for `release_v3.0_ru`) the language used because original English texts were kept in the Russian version.
Russian data has additional optional fields comparing to English:
- `dbpedialinks`: RDF triples extracted from DBpedia between English and Russian entities by means of the property `sameAs`.
- `links`: RDF triples created manually for some entities to serve as pointers to translators. There are two types of them:
* with `sameAs` (`Spaniards | sameAs | испанцы`)
* with `includes` (`Tomatoes, guanciale, cheese, olive oil | includes | гуанчиале`). Those were mostly created for string literals to translate some parts of them.
### Data Splits
For `v3.0` releases:
| English (v3.0) | Train | Dev | Test (data-to-text) |
|-----------------|--------|-------|-------|
| **triple sets** | 13,211 | 1,667 | 1,779 |
| **texts** | 35,426 | 4,464 | 5,150 |
|**properties** | 372 | 290 | 220 |
| Russian (v3.0) | Train | Dev | Test (data-to-text) |
|-----------------|--------|-------|---------------------|
| **triple sets** | 5,573 | 790 | 1,102 |
| **texts** | 14,239 | 2,026 | 2,780 |
|**properties** | 226 | 115 | 192 |
## Dataset Creation
### Curation Rationale
The WebNLG dataset was created to promote the development _(i)_ of RDF verbalisers and _(ii)_ of microplanners able to handle a wide range of linguistic constructions. The dataset aims at covering knowledge in different domains ("categories"). The same properties and entities can appear in several categories.
### Source Data
The data was compiled from raw DBpedia triples. [This paper](https://www.aclweb.org/anthology/C16-1141/) explains how the triples were selected.
#### Initial Data Collection and Normalization
Initial triples extracted from DBpedia were modified in several ways. See [official documentation](https://webnlg-challenge.loria.fr/docs/) for the most frequent changes that have been made. An original tripleset and a modified tripleset usually represent a one-to-one mapping. However, there are cases with many-to-one mappings when several original triplesets are mapped to one modified tripleset.
Entities that served as roots of RDF trees are listed in [this file](https://gitlab.com/shimorina/webnlg-dataset/-/blob/master/supplementary/entities_dict.json).
The English WebNLG 2020 dataset (v3.0) for training comprises data-text pairs for 16 distinct DBpedia categories:
- The 10 seen categories used in the 2017 version: Airport, Astronaut, Building, City, ComicsCharacter, Food, Monument, SportsTeam, University, and WrittenWork.
- The 5 unseen categories of 2017, which are now part of the seen data: Athlete, Artist, CelestialBody, MeanOfTransportation, Politician.
- 1 new category: Company.
The Russian dataset (v3.0) comprises data-text pairs for 9 distinct categories: Airport, Astronaut, Building, CelestialBody, ComicsCharacter, Food, Monument, SportsTeam, and University.
#### Who are the source language producers?
There are no source texts, all textual material was compiled during the annotation process.
### Annotations
#### Annotation process
Annotators were first asked to create sentences that verbalise single triples. In a second round, annotators were asked to combine single-triple sentences together into sentences that cover 2 triples. And so on until 7 triples. Quality checks were performed to ensure the quality of the annotations. See Section 3.3 in [the dataset paper](https://www.aclweb.org/anthology/P17-1017.pdf).
Russian data was translated from English with an MT system and then was post-edited by crowdworkers. See Section 2.2 of [this paper](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf).
#### Who are the annotators?
All references were collected through crowdsourcing platforms (CrowdFlower/Figure 8 and Amazon Mechanical Turk). For Russian, post-editing was done using the Yandex.Toloka crowdsourcing platform.
### Personal and Sensitive Information
Neither the dataset as published or the annotation process involves the collection or sharing of any kind of personal / demographic information.
## Considerations for Using the Data
### Social Impact of Dataset
We do not foresee any negative social impact in particular from this dataset or task.
Positive outlooks: Being able to generate good quality text from RDF data would permit, e.g., making this data more accessible to lay users, enriching existing text with information drawn from knowledge bases such as DBpedia or describing, comparing and relating entities present in these knowledge bases.
### Discussion of Biases
This dataset is created using DBpedia RDF triples which naturally exhibit biases that have been found to exist in Wikipedia such as some forms of, e.g., gender bias.
The choice of [entities](https://gitlab.com/shimorina/webnlg-dataset/-/blob/master/supplementary/entities_dict.json), described by RDF trees, was not controlled. As such, they may contain gender biases; for instance, all the astronauts described by RDF triples are male. Hence, in texts, pronouns _he/him/his_ occur more often. Similarly, entities can be related to the Western culture more often than to other cultures.
### Other Known Limitations
The quality of the crowdsourced references is limited, in particular in terms of fluency/naturalness of the collected texts.
Russian data was machine-translated and then post-edited by crowdworkers, so some examples may still exhibit issues related to bad translations.
## Additional Information
### Dataset Curators
The principle curator of the dataset is Anastasia Shimorina (Université de Lorraine / LORIA, France). Throughout the WebNLG releases, several people contributed to their construction: Claire Gardent (CNRS / LORIA, France), Shashi Narayan (Google, UK), Laura Perez-Beltrachini (University of Edinburgh, UK), Elena Khasanova, and Thiago Castro Ferreira (Federal University of Minas Gerais, Brazil).
The dataset construction was funded by the French National Research Agency (ANR).
### Licensing Information
The dataset uses the `cc-by-nc-sa-4.0` license. The source DBpedia project uses the `cc-by-sa-3.0` and `gfdl-1.1` licenses.
### Citation Information
- If you use the WebNLG corpus, cite:
```
@inproceedings{web_nlg,
author = {Claire Gardent and
Anastasia Shimorina and
Shashi Narayan and
Laura Perez{-}Beltrachini},
editor = {Regina Barzilay and
Min{-}Yen Kan},
title = {Creating Training Corpora for {NLG} Micro-Planners},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational
Linguistics, {ACL} 2017, Vancouver, Canada, July 30 - August 4, Volume
1: Long Papers},
pages = {179--188},
publisher = {Association for Computational Linguistics},
year = {2017},
url = {https://doi.org/10.18653/v1/P17-1017},
doi = {10.18653/v1/P17-1017}
}
```
- If you use `release_v2_constrained` in particular, cite:
```
@InProceedings{shimorina2018handling,
author = "Shimorina, Anastasia
and Gardent, Claire",
title = "Handling Rare Items in Data-to-Text Generation",
booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "360--370",
location = "Tilburg University, The Netherlands",
url = "http://aclweb.org/anthology/W18-6543"
}
```
### Contributions
Thanks to [@Shimorina](https://github.com/Shimorina), [@yjernite](https://github.com/yjernite) for adding this dataset. |
kunishou/databricks-dolly-15k-ja | 2023-09-10T13:47:12.000Z | [
"license:cc-by-sa-3.0",
"region:us"
] | kunishou | null | null | null | 52 | 6,002 | ---
license: cc-by-sa-3.0
---
This dataset was created by automatically translating "databricks-dolly-15k" into Japanese.
This dataset is licensed under CC-BY-SA-3.0
Last Update : 2023-05-11
databricks-dolly-15k-ja
https://github.com/kunishou/databricks-dolly-15k-ja
databricks-dolly-15k
https://github.com/databrickslabs/dolly/tree/master/data |
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