id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
sinandraide/hotpot_qa_spread | 2023-10-30T20:36:25.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | sinandraide | null | null | 0 | 191 | 2023-10-30T18:13:11 | ---
task_categories:
- question-answering
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
This dataset is a spread version of the HotpotQA dataset. This version allows it to be compatible with Langchain's HuggingfaceLoader.
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
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[More Information Needed] | 4,535 | [
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HuggingFaceM4/NoCaps | 2022-12-14T04:08:38.000Z | [
"license:cc-by-2.0",
"region:us"
] | HuggingFaceM4 | Dubbed NoCaps, for novel object captioning at scale, NoCaps consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets.
The associated training data consists of COCO image-caption pairs, plus Open Images image-level labels and object bounding boxes.
Since Open Images contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). | @inproceedings{agrawal2019nocaps,
title={nocaps: novel object captioning at scale},
author={Agrawal, Harsh and Desai, Karan and Wang, Yufei and Chen, Xinlei and Jain, Rishabh and Johnson, Mark and Batra, Dhruv and Parikh, Devi and Lee, Stefan and Anderson, Peter},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={8948--8957},
year={2019}
} | 1 | 190 | 2022-12-08T17:11:21 | ---
license: cc-by-2.0
---
# Dataset Card for NoCaps
## 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://nocaps.org/](https://nocaps.org/)
- **Paper:** [nocaps: novel object captioning at scale](https://openaccess.thecvf.com/content_ICCV_2019/papers/Agrawal_nocaps_novel_object_captioning_at_scale_ICCV_2019_paper.pdf)
- **Leaderboard:**
- **Point of Contact:**: contact@nocaps.org
### Dataset Summary
Dubbed NoCaps for novel object captioning at scale, NoCaps consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets.
The associated training data consists of COCO image-caption pairs, plus Open Images image-level labels and object bounding boxes.
Since Open Images contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Each instance has the following structure:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=732x1024 at 0x7F574A3A9B50>,
'image_coco_url': 'https://s3.amazonaws.com/nocaps/val/0013ea2087020901.jpg',
'image_date_captured': '2018-11-06 11:04:33',
'image_file_name': '0013ea2087020901.jpg',
'image_height': 1024,
'image_width': 732,
'image_id': 0,
'image_license': 0,
'image_open_images_id': '0013ea2087020901',
'annotations_ids': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
'annotations_captions': [
'A baby is standing in front of a house.',
'A little girl in a white jacket and sandals.',
'A young child stands in front of a house.',
'A child is wearing a white shirt and standing on a side walk. ',
'A little boy is standing in his diaper with a white shirt on.',
'A child wearing a diaper and shoes stands on the sidewalk.',
'A child is wearing a light-colored shirt during the daytime.',
'A little kid standing on the pavement in a shirt. ',
'Black and white photo of a little girl smiling.',
'a cute baby is standing alone with white shirt'
]
}
```
### Data Fields
- `image`: The image
- `image_coco_url`: URL for the image
- `image_date_captured`: Date at which the image was captured
- `image_file_name`: The file name for the image
- `image_height`: Height of the image
- `image_width`: Width of the image
- `image_id`: Id of the image
- `image_license`: Not sure what this is, it is always at 0
- `image_open_images_id`: Open image id
- `annotations_ids`: Unique ids for the captions (to use in conjunction with `annotations_captions`)
- `annotations_captions`: Captions for the image (to use in conjunction with `annotations_ids`)
### 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 [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. | 4,857 | [
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BioDEX/BioDEX-ICSR | 2023-05-30T15:20:25.000Z | [
"region:us"
] | BioDEX | null | null | 2 | 190 | 2023-04-19T11:10:45 | ---
dataset_info:
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splits:
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num_examples: 3628
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- name: validation
num_bytes: 96385392
num_examples: 2407
download_size: 337571954
dataset_size: 626993692
---
# Dataset Card for "BioDEX-ICSR"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,524 | [
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SinKove/synthetic_mammography_csaw | 2023-10-11T21:04:10.000Z | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"license:openrail",
"medical",
"arxiv:2112.01330",
"arxiv:2307.15208",
"doi:10.57967/hf/1254",
"region:us"
] | SinKove | null | null | 16 | 190 | 2023-10-11T18:50:12 | ---
task_categories:
- image-classification
tags:
- medical
pretty_name: C
size_categories:
- 10K<n<100K
license: openrail
---
# Dataset Card for Synthetic CSAW 100k Mammograms
## Dataset Description
This is a synthetic mammogram dataset created with the latent diffusion model from *Generative AI for Medical Imaging: extending the MONAI Framework* paper.
The generative model was trained on the [CSAW-M dataset](https://arxiv.org/abs/2112.01330).
- **Paper: https://arxiv.org/abs/2307.15208
- **Point of Contact: walter.diaz_sanz@kcl.ac.uk
### Dataset Summary
### Supported Tasks
Classification masking of cancer in mammogram.
The dataset contains 100k synthetic mammograms with 3 labels:
- "Low masking level" (score <= 2),
- "Medium masking level" (2 < score <= 6),
- "High masking level" (score > 6).
## Dataset Structure
- Images
- CSAW-M Labels
### Data Splits
We did not define data splits.
## Dataset Creation
We generated the synthetic data samples using the diffusion model finetuned on the [CSAW-M dataset](https://arxiv.org/abs/2112.01330).
### Personal and Sensitive Information
Following GDPR "Personal data is any information that relates to an identified or identifiable living individual."
We make sure that there are not "personal data" (re-identifiable information) by filtering with a deep learning model trained for identifying patients.
## Considerations for Using the Data
### Social Impact of Dataset
We hope that this dataset can used to enhance AI models training for cancer masking.
### Discussion of Biases
There are biases towards specific pathologies.
## Additional Information
### Dataset Curators
### Licensing Information
This dataset is released under the [Open & Responsible AI license ("OpenRAIL")](https://huggingface.co/blog/open_rail)
### Citation Information
Pinaya, W. H., Graham, M. S., Kerfoot, E., Tudosiu, P. D., Dafflon, J., Fernandez, V., ... & Cardoso, M. J. (2023). Generative ai for medical imaging: extending the monai framework. arXiv preprint arXiv:2307.15208.
https://arxiv.org/abs/2307.15208
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] |
nus-yam/bigfixes | 2023-11-01T14:56:26.000Z | [
"region:us"
] | nus-yam | null | null | 1 | 190 | 2023-10-26T05:55:10 | ---
---
pretty_name: BigFixes
description: A clean union of BigVul and CVE-Fixes.
configs:
- config_name: default
data_files:
- split: train
path: train.csv
- split: cleantest
path: clean_test.csv
- split: test
path: test.csv
---
---
# Information
This is a clean version of the union of BigVul and CVE-Fixes used
[here](https://huggingface.co/datasets/MickyMike/cvefixes_bigvul).
We have
four splits:
- `train`, which has the de-duplicated training data;
- `cleantest`, which has de-duplicated testing data that is completely disjoint from the
training set;
- `test`, which has the deduplicated training data with a
significant intersection with the training data (as seen in the original
repository);
- `output`, which is [VulRepair](https://github.com/awsm-research/VulRepair/tree/main)'s output on the data found in the `test` split.
Our preprocessing is available in `preprocessing.ipynb`.
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turk | 2022-11-18T21:56:55.000Z | [
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:gpl-3.0",
"region:us"
] | null | TURKCorpus is a dataset for evaluating sentence simplification systems that focus on lexical paraphrasing,
as described in "Optimizing Statistical Machine Translation for Text Simplification". The corpus is composed of 2000 validation and 359 test original sentences that were each simplified 8 times by different annotators. | @article{Xu-EtAl:2016:TACL,
author = {Wei Xu and Courtney Napoles and Ellie Pavlick and Quanze Chen and Chris Callison-Burch},
title = {Optimizing Statistical Machine Translation for Text Simplification},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year = {2016},
url = {https://cocoxu.github.io/publications/tacl2016-smt-simplification.pdf},
pages = {401--415}
}
} | 3 | 189 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
paperswithcode_id: null
pretty_name: TURK
dataset_info:
features:
- name: original
dtype: string
- name: simplifications
sequence: string
config_name: simplification
splits:
- name: validation
num_bytes: 2120187
num_examples: 2000
- name: test
num_bytes: 396378
num_examples: 359
download_size: 2443394
dataset_size: 2516565
---
# Dataset Card for TURK
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** None
- **Repository:** [TURK](https://github.com/cocoxu/simplification)
- **Paper:** [Optimizing Statistical Machine Translation for Text Simplification](https://www.aclweb.org/anthology/Q16-1029/)
- **Leaderboard:** N/A
- **Point of Contact:** [Wei Xu](mailto:wei.xu@cc.gatech.edu)
### Dataset Summary
TURK is a multi-reference dataset for the evaluation of sentence simplification in English. The dataset consists of 2,359 sentences from the [Parallel Wikipedia Simplification (PWKP) corpus](https://www.aclweb.org/anthology/C10-1152/). Each sentence is associated with 8 crowdsourced simplifications that focus on only lexical paraphrasing (no sentence splitting or deletion).
### Supported Tasks and Leaderboards
No Leaderboard for the task.
### Languages
TURK contains English text only (BCP-47: `en`).
## Dataset Structure
### Data Instances
An instance consists of an original sentence and 8 possible reference simplifications that focus on lexical paraphrasing.
```
{'original': 'one side of the armed conflicts is composed mainly of the sudanese military and the janjaweed , a sudanese militia group recruited mostly from the afro-arab abbala tribes of the northern rizeigat region in sudan .',
'simplifications': ['one side of the armed conflicts is made of sudanese military and the janjaweed , a sudanese militia recruited from the afro-arab abbala tribes of the northern rizeigat region in sudan .', 'one side of the armed conflicts consist of the sudanese military and the sudanese militia group janjaweed .', 'one side of the armed conflicts is mainly sudanese military and the janjaweed , which recruited from the afro-arab abbala tribes .', 'one side of the armed conflicts is composed mainly of the sudanese military and the janjaweed , a sudanese militia group recruited mostly from the afro-arab abbala tribes in sudan .', 'one side of the armed conflicts is made up mostly of the sudanese military and the janjaweed , a sudanese militia group whose recruits mostly come from the afro-arab abbala tribes from the northern rizeigat region in sudan .', 'the sudanese military and the janjaweed make up one of the armed conflicts , mostly from the afro-arab abbal tribes in sudan .', 'one side of the armed conflicts is composed mainly of the sudanese military and the janjaweed , a sudanese militia group recruited mostly from the afro-arab abbala tribes of the northern rizeigat regime in sudan .', 'one side of the armed conflicts is composed mainly of the sudanese military and the janjaweed , a sudanese militia group recruited mostly from the afro-arab abbala tribes of the northern rizeigat region in sudan .']}
```
### Data Fields
- `original`: an original sentence from the source datasets
- `simplifications`: a set of reference simplifications produced by crowd workers.
### Data Splits
TURK does not contain a training set; many models use [WikiLarge](https://github.com/XingxingZhang/dress) (Zhang and Lapata, 2017) or [Wiki-Auto](https://github.com/chaojiang06/wiki-auto) (Jiang et. al 2020) for training.
Each input sentence has 8 associated reference simplified sentences. 2,359 input sentences are randomly split into 2,000 validation and 359 test sentences.
| | Dev | Test | Total |
| ----- | ------ | ---- | ----- |
| Input Sentences | 2000 | 359 | 2359 |
| Reference Simplifications | 16000 | 2872 | 18872 |
## Dataset Creation
### Curation Rationale
The TURK dataset was constructed to evaluate the task of text simplification. It contains multiple human-written references that focus on only lexical simplification.
### Source Data
#### Initial Data Collection and Normalization
The input sentences in the dataset are extracted from the [Parallel Wikipedia Simplification (PWKP) corpus](https://www.aclweb.org/anthology/C10-1152/).
#### Who are the source language producers?
The references are crowdsourced from Amazon Mechanical Turk. The annotators were asked to provide simplifications without losing any information or splitting the input sentence. No other demographic or compensation information is provided in the paper.
### Annotations
#### Annotation process
The instructions given to the annotators are available in the paper.
#### Who are the annotators?
The annotators are Amazon Mechanical Turk workers.
### Personal and Sensitive Information
Since the dataset is created from English Wikipedia (August 22, 2009 version), all the information contained in the dataset is already in the public domain.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset helps move forward the research towards text simplification by creating a higher quality validation and test dataset. Progress in text simplification in turn has the potential to increase the accessibility of written documents to wider audiences.
### Discussion of Biases
The dataset may contain some social biases, as the input sentences are based on Wikipedia. Studies have shown that the English Wikipedia contains both gender biases [(Schmahl et al., 2020)](https://research.tudelft.nl/en/publications/is-wikipedia-succeeding-in-reducing-gender-bias-assessing-changes) and racial biases [(Adams et al., 2019)](https://journals.sagepub.com/doi/pdf/10.1177/2378023118823946).
### Other Known Limitations
Since the dataset contains only 2,359 sentences that are derived from Wikipedia, it is limited to a small subset of topics present on Wikipedia.
## Additional Information
### Dataset Curators
TURK was developed by researchers at the University of Pennsylvania. The work was supported by the NSF under grant IIS-1430651 and the NSF GRFP under grant 1232825.
### Licensing Information
[GNU General Public License v3.0](https://github.com/cocoxu/simplification/blob/master/LICENSE)
### Citation Information
```
@article{Xu-EtAl:2016:TACL,
author = {Wei Xu and Courtney Napoles and Ellie Pavlick and Quanze Chen and Chris Callison-Burch},
title = {Optimizing Statistical Machine Translation for Text Simplification},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year = {2016},
url = {https://cocoxu.github.io/publications/tacl2016-smt-simplification.pdf},
pages = {401--415}
}
```
### Contributions
Thanks to [@mounicam](https://github.com/mounicam) for adding this dataset. | 8,143 | [
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nielsr/FUNSD_layoutlmv2 | 2022-10-25T09:51:20.000Z | [
"language:en",
"arxiv:1905.13538",
"region:us"
] | nielsr | https://guillaumejaume.github.io/FUNSD/ | @article{Jaume2019FUNSDAD,
title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
author={Guillaume Jaume and H. K. Ekenel and J. Thiran},
journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)},
year={2019},
volume={2},
pages={1-6}
} | 4 | 189 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: funsd
---
# Dataset Card for "FUNSD"
## 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
### Dataset Summary
The [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset, with one difference compared to the original dataset, each document image is resized to 224x224.
The FUNSD dataset is a collection of annotated forms.
This dataset loading script is taken from the [official LayoutLMv2 implementation](https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/data/datasets/funsd.py), and updated to not include any Detectron2 dependencies.
### 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
We show detailed information for up to 5 configurations of the dataset.
### Data Instances
#### conll2000
- **Size of downloaded dataset files:** 3.32 MB
- **Size of the generated dataset:** 6.25 MB
- **Total amount of disk used:** 9.57 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"chunk_tags": [11, 13, 11, 12, 21, 22, 22, 22, 22, 11, 12, 12, 17, 11, 12, 13, 11, 0, 1, 13, 11, 11, 0, 21, 22, 22, 11, 12, 12, 13, 11, 12, 12, 11, 12, 12, 0],
"id": "0",
"pos_tags": [19, 14, 11, 19, 39, 27, 37, 32, 34, 11, 15, 19, 14, 19, 22, 14, 20, 5, 15, 14, 19, 19, 5, 34, 32, 34, 11, 15, 19, 14, 20, 9, 20, 24, 15, 22, 6],
"tokens": "[\"Confidence\", \"in\", \"the\", \"pound\", \"is\", \"widely\", \"expected\", \"to\", \"take\", \"another\", \"sharp\", \"dive\", \"if\", \"trade\", \"figur..."
}
```
### Data Fields
The data fields are the same among all splits.
### Data Splits
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{DBLP:journals/corr/abs-1905-13538,
author = {Guillaume Jaume and
Hazim Kemal Ekenel and
Jean{-}Philippe Thiran},
title = {{FUNSD:} {A} Dataset for Form Understanding in Noisy Scanned Documents},
journal = {CoRR},
volume = {abs/1905.13538},
year = {2019},
url = {http://arxiv.org/abs/1905.13538},
archivePrefix = {arXiv},
eprint = {1905.13538},
timestamp = {Mon, 03 Jun 2019 13:42:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-13538.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@vblagoje](https://github.com/vblagoje), [@jplu](https://github.com/jplu) for adding this dataset. | 5,642 | [
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GEM/FairytaleQA | 2022-10-25T12:58:30.000Z | [
"task_categories:other",
"annotations_creators:expert-created",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:unknown",
"question-generation",
"arxiv:2203.13947",
"region:us"
] | GEM | \
The FairytaleQA dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. This is for the Question Generation Task of FairytaleQA. | \
@inproceedings{xu2022fairytaleqa,
author={Xu, Ying and Wang, Dakuo and Yu, Mo and Ritchie, Daniel and Yao, Bingsheng and Wu, Tongshuang and Zhang, Zheng and Li, Toby Jia-Jun and Bradford, Nora and Sun, Branda and Hoang, Tran Bao and Sang, Yisi and Hou, Yufang and Ma, Xiaojuan and Yang, Diyi and Peng, Nanyun and Yu, Zhou and Warschauer, Mark},
title = {Fantastic Questions and Where to Find Them: Fairytale{QA} -- An Authentic Dataset for Narrative Comprehension},
publisher = {Association for Computational Linguistics},
year = {2022}
} | 4 | 189 | 2022-05-19T15:51:16 | ---
annotations_creators:
- expert-created
language_creators:
- unknown
language:
- en
license:
- unknown
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: FairytaleQA
tags:
- question-generation
---
# Dataset Card for GEM/FairytaleQA
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/uci-soe/FairytaleQAData
- **Paper:** https://arxiv.org/abs/2203.13947
- **Leaderboard:** https://paperswithcode.com/sota/question-generation-on-fairytaleqa
- **Point of Contact:** Ying Xu, Dakuo Wang
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/FairytaleQA).
### Dataset Summary
The FairytaleQA Dataset is an English-language dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. The Dataset was corrected to support both the tasks of Question Generation and Question Answering.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/FairytaleQA')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/FairytaleQA).
#### paper
[ArXiv](https://arxiv.org/abs/2203.13947)
#### authors
Ying Xu (University of California Irvine); Dakuo Wang (IBM Research); Mo Yu (IBM Research); Daniel Ritchie (University of California Irvine); Bingsheng Yao (Rensselaer Polytechnic Institute); Tongshuang Wu (University of Washington); Zheng Zhang (University of Notre Dame); Toby Jia-Jun Li (University of Notre Dame); Nora Bradford (University of California Irvine); Branda Sun (University of California Irvine); Tran Bao Hoang (University of California Irvine); Yisi Sang (Syracuse University); Yufang Hou (IBM Research Ireland); Xiaojuan Ma (Hong Kong Univ. of Sci and Tech); Diyi Yang (Georgia Institute of Technology); Nanyun Peng (University of California Los Angeles); Zhou Yu (Columbia University); Mark Warschauer (University of California Irvine)
## Dataset Overview
### Where to find the Data and its Documentation
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/uci-soe/FairytaleQAData)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ArXiv](https://arxiv.org/abs/2203.13947)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
@inproceedings{xu2022fairytaleqa,
author={Xu, Ying and Wang, Dakuo and Yu, Mo and Ritchie, Daniel and Yao, Bingsheng and Wu, Tongshuang and Zhang, Zheng and Li, Toby Jia-Jun and Bradford, Nora and Sun, Branda and Hoang, Tran Bao and Sang, Yisi and Hou, Yufang and Ma, Xiaojuan and Yang, Diyi and Peng, Nanyun and Yu, Zhou and Warschauer, Mark},
title = {Fantastic Questions and Where to Find Them: Fairytale{QA} -- An Authentic Dataset for Narrative Comprehension},
publisher = {Association for Computational Linguistics},
year = {2022}
}
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Ying Xu, Dakuo Wang
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
ying.xu@uci.edu, dakuo.wang@ibm.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
yes
#### Leaderboard Link
<!-- info: Provide a link to the leaderboard. -->
<!-- scope: periscope -->
[PapersWithCode](https://paperswithcode.com/sota/question-generation-on-fairytaleqa)
#### Leaderboard Details
<!-- info: Briefly describe how the leaderboard evaluates models. -->
<!-- scope: microscope -->
The task was to generate questions corresponding to the given answers and the story context. Success on the Question Generation task is typically measured by achieving a high ROUGE-L score to the reference ground-truth question.
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
[N/A]
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
[N/A]
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
unknown: License information unavailable
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The purpose of this dataset is to help develop systems to facilitate assessment and training of narrative comprehension skills for children in education domain. The dataset distinguishes fine-grained reading skills, such as the understanding of varying narrative elements, and contains high-quality QA-pairs generated by education experts with sufficient training and education domain knowledge to create valid QA-pairs in a consistent way.
This dataset is suitable for developing models to automatically generate questions and QA-Pairs that satisfy the need for a continuous supply of new questions, which can potentially enable large-scale development of AI-supported interactive platforms for the learning and assessment of reading comprehension skills.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Question Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
The task was to generate questions corresponding to the given answers and the story context. Models trained for this task can potentially enable large-scale development of AI-supported interactive platforms for the learning and assessment of reading comprehension skills.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
University of California Irvine
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Ying Xu (University of California Irvine); Dakuo Wang (IBM Research); Mo Yu (IBM Research); Daniel Ritchie (University of California Irvine); Bingsheng Yao (Rensselaer Polytechnic Institute); Tongshuang Wu (University of Washington); Zheng Zhang (University of Notre Dame); Toby Jia-Jun Li (University of Notre Dame); Nora Bradford (University of California Irvine); Branda Sun (University of California Irvine); Tran Bao Hoang (University of California Irvine); Yisi Sang (Syracuse University); Yufang Hou (IBM Research Ireland); Xiaojuan Ma (Hong Kong Univ. of Sci and Tech); Diyi Yang (Georgia Institute of Technology); Nanyun Peng (University of California Los Angeles); Zhou Yu (Columbia University); Mark Warschauer (University of California Irvine)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Schmidt Futures
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Dakuo Wang (IBM Research); Bingsheng Yao (Rensselaer Polytechnic Institute); Ying Xu (University of California Irvine)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `story_name`: a string of the story name to which the story section content belongs. Full story data can be found [here](https://github.com/uci-soe/FairytaleQAData).
- `content`: a string of the story section(s) content related to the experts' labeled QA-pair. Used as the input for both Question Generation and Question Answering tasks.
- `question`: a string of the question content. Used as the input for Question Answering task and as the output for Question Generation task.
- `answer`: a string of the answer content for all splits. Used as the input for Question Generation task and as the output for Question Answering task.
- `gem_id`: a string of id follows GEM naming convention ```GEM-${DATASET_NAME}-${SPLIT-NAME}-${id}``` where id is an incrementing number starting at 1
- `target`: a string of the question content being used for training
- `references`: a list of string containing the question content being used for automatic eval
- `local_or_sum`: a string of either local or summary, indicating whether the QA is related to one story section or multiple sections
- `attribute`: a string of one of character, causal relationship, action, setting, feeling, prediction, or outcome resolution. Classification of the QA by education experts annotators via 7 narrative elements on an established framework
- `ex_or_im`: a string of either explicit or implicit, indicating whether the answers can be directly found in the story content or cannot be directly from the story content.
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
[N/A]
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
A typical data point comprises a question, the corresponding story content, and one answer. Education expert annotators labeled whether the answer is locally relevant to one story section or requires summarization capabilities from multiple story sections, and whether the answers are explicit (can be directly found in the stories) or implicit (cannot be directly found in the story text). Additionally, education expert annotators categorize the QA-pairs via 7 narrative elements from an establish framework.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
{'story_name': 'self-did-it',
'content': '" what is your name ? " asked the girl from underground . " self is my name , " said the woman . that seemed a curious name to the girl , and she once more began to pull the fire apart . then the woman grew angry and began to scold , and built it all up again . thus they went on for a good while ; but at last , while they were in the midst of their pulling apart and building up of the fire , the woman upset the tar - barrel on the girl from underground . then the latter screamed and ran away , crying : " father , father ! self burned me ! " " nonsense , if self did it , then self must suffer for it ! " came the answer from below the hill .',
'answer': 'the woman told the girl her name was self .',
'question': "why did the girl's father think the girl burned herself ?",
'gem_id': 'GEM-FairytaleQA-test-1006',
'target': "why did the girl's father think the girl burned herself ?",
'references': ["why did the girl's father think the girl burned herself ?"],
'local_or_sum': 'local',
'attribute': 'causal relationship',
'ex_or_im': 'implicit'}
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
The data is split into a train, validation, and test split randomly. The final split sizes are as follows:
| | Train | Validation | Test |
| ----- | ----- | ----- | ----- |
| # Books | 232 | 23 | 23 |
| # QA-Pairs | 8548 | 1025 |1007 |
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The books are randomly split into train/validation/test splits. We control the ratio of QA-pair numbers in train:validation:test splits close to 8:1:1
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
[N/A]
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
The dataset distinguishes fine-grained reading skills, such as the understanding of varying narrative elements, and contains high-quality QA-pairs generated by education experts with sufficient training and education domain knowledge to create valid QA-pairs in a consistent way.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
This dataset is suitable for developing models to automatically generate questions or QA-pairs that satisfy the need for a continuous supply of new questions, which can potentially enable large-scale development of AI-supported interactive platforms for the learning and assessment of reading comprehension skills.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`data points removed`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
The original data contains two answers by different annotators in validation/test splits, we removed the 2nd answer for GEM version because it is not being used for the Question Generation task.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
[N/A]
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
We are able to measure model's capabilities of generating various types of questions that corresponds to different narrative elements with the FairytaleQA dataset on the Question Generation Task
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`ROUGE`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
The task was to generate questions corresponding to the given answers and the story context. Success on this task is typically measured by achieving a high [ROUGE](https://huggingface.co/metrics/rouge) score to the reference ground-truth questions.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
A [BART-based model](https://huggingface.co/facebook/bart-large) currently achieves a [ROUGE-L of 0.527/0.527](https://github.com/uci-soe/FairytaleQAData) on valid/test splits, which is reported as the baseline experiment for the dataset [paper](https://arxiv.org/pdf/2203.13947.pdf).
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
FairytaleQA was built to focus on comprehension of narratives in the education domain, targeting students from kindergarten to eighth grade. We focus on narrative comprehension for 1. it is a high-level comprehension skill strongly predictive of reading achievement and plays a central role in daily life as people frequently encounter narratives in different forms, 2. narrative stories have a clear structure of specific elements and relations among these elements, and there are existing validated narrative comprehension frameworks around this structure, which provides a basis for developing the annotation schema for our dataset.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
The purpose of this dataset is to help develop systems to facilitate assessment and training of narrative comprehension skills for children in education domain.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The fairytale story texts are from the [Project Gutenberg](https://www.gutenberg.org/) website
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
We gathered the text from the Project Gutenberg website, using “fairytale” as the search term.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Due to a large number of fairytales found, we used the most popular stories based on the number of downloads since these stories are presumably of higher quality. To ensure the readability of the text, we made a small number of minor revisions to some obviously outdated vocabulary (e.g., changing “ere” to “before”) and the unconventional use of punctuation (e.g., changing consecutive semi-colons to periods).
These texts were broken down into small sections based on their semantic content by our annotators. The annotators were instructed to split the story into sections of 100-300 words that also contain meaningful content and are separated at natural story breaks. An initial annotator would split the story, and this would be reviewed by a cross-checking annotator. Most of the resulting sections were one natural paragraph of the original text.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
manually
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
For each story, we evaluated the reading difficulty level using the [textstat](https://pypi.org/project/textstat/) Python package, primarily based on sentence length, word length, and commonness of words. We excluded stories that are at 10th grade level or above.
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
expert created
#### Number of Raters
<!-- info: What is the number of raters -->
<!-- scope: telescope -->
2<n<10
#### Rater Qualifications
<!-- info: Describe the qualifications required of an annotator. -->
<!-- scope: periscope -->
All of these annotators have a B.A. degree in education, psychology, or cognitive science and have substantial experience in teaching and reading assessment. These annotators were supervised by three experts in literacy education.
#### Raters per Training Example
<!-- info: How many annotators saw each training example? -->
<!-- scope: periscope -->
2
#### Raters per Test Example
<!-- info: How many annotators saw each test example? -->
<!-- scope: periscope -->
3
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
The dataset annotation distinguishes fine-grained reading skills, such as the understanding of varying narrative elements, and contains high-quality QA-pairs generated by education experts with sufficient training and education domain knowledge to create valid QA-pairs in a consistent way.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated by data curators
#### Quality Control Details
<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
The annotators were instructed to imagine that they were creating questions to test elementary or middle school students in the process of reading a complete story. We required the annotators to generate only natural, open-ended questions, avoiding “yes-” or “no-” questions. We also instructed them to provide a diverse set of questions about 7 different narrative elements, and with both implicit and explicit questions.
We asked the annotators to also generate answers for each of their questions. We asked them to provide the shortest possible answers but did not restrict them to complete sentences or short phrases. We also asked the annotators to label which section(s) the question and answer was from.
All annotators received a two-week training in which each of them was familiarized with the coding template and conducted practice coding on the same five stories. The practice QA pairs were then reviewed by the other annotators and the three experts, and discrepancies among annotators were discussed. During the annotation process, the team met once every week to review and discuss each member’s work. All QA pairs were cross-checked by two annotators, and 10% of the QA pairs were additionally checked by the expert supervisor.
For the 46 stories used as the evaluation set, we annotate a second reference answer by asking an annotator to independently read the story and answer the questions generated by others.
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
During the annotation process, the team met once every week to review and discuss each member’s work. All QA pairs were cross-checked by two annotators, and 10% of the QA pairs were additionally checked by the expert supervisor.
#### Other Consented Downstream Use
<!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? -->
<!-- scope: microscope -->
Aside from Question Generation task, the data creators and curators used this data for Question Answering, and QA-Pair Generation tasks, and to identify social stereotypes represented in story narratives.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
The story content is from publically available knowledge website and the annotated QA-pairs are about general knowledge to the story content without references to the author or to any persons
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
yes
#### Maintenance Plan Details
<!-- info: Describe the original dataset's maintenance plan. -->
<!-- scope: microscope -->
We plan to host various splits for the FairytaleQA dataset to better serve various types of research interests. We have the original data for 2 different split approaches including train/validation/test splits and split by fairytale origins. We are also plan to host the dataset on multiple platforms for various tasks.
#### Maintainer Contact Information
<!-- info: Provide contact information of a person responsible for the dataset maintenance -->
<!-- scope: periscope -->
Daniel Ritchie
#### Any Contestation Mechanism?
<!-- info: Does the maintenance plan include a contestation mechanism allowing individuals to request removal fo content? -->
<!-- scope: periscope -->
no mechanism
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
yes - models trained on this dataset
#### Social Impact Observations
<!-- info: Did any of these previous uses result in observations about the social impact of the systems? In particular, has there been work outlining the risks and limitations of the system? Provide links and descriptions here. -->
<!-- scope: microscope -->
[N/A]
#### Changes as Consequence of Social Impact
<!-- info: Have any changes been made to the dataset as a result of these observations? -->
<!-- scope: periscope -->
[N/A]
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
From the educational perspective, given that reading comprehension is a multicomponent skill, it is ideal for comprehension questions to be able to identify students’ performance in specific sub-skills, thus allowing teachers to provide tailored guidance.
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
[N/A]
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
[N/A]
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`research use only`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
We noticed that human results are obtained via cross-estimation between the two annotated answers, thus are underestimated. One possibility for future work is to conduct a large-scale human annotation to collect more answers per question and then leverage the massively annotated answers to better establish a human performance evaluation.
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
The QA-pairs annotated by education experts are targeting the audience of children from kindergarten to eighth grade, so the difficulty of QA-pairs are not suitable to compare with other existing dataset that are sourced from knowledge graphs or knowledge bases like Wikipedia.
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
[N/A]
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bigbio/gnormplus | 2023-02-17T14:55:04.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | We re-annotated two existing gene corpora. The BioCreative II GN corpus is a widely used data set for benchmarking GN
tools and includes document-level annotations for a total of 543 articles (281 in its training set; and 262 in test).
The Citation GIA Test Collection was recently created for gene indexing at the NLM and includes 151 PubMed abstracts
with both mention-level and document-level annotations. They are selected because both have a focus on human genes.
For both corpora, we added annotations of gene families and protein domains. For the BioCreative GN corpus, we also
added mention-level gene annotations. As a result, in our new corpus, there are a total of 694 PubMed articles.
PubTator was used as our annotation tool along with BioC formats. | @Article{Wei2015,
author={Wei, Chih-Hsuan and Kao, Hung-Yu and Lu, Zhiyong},
title={GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains},
journal={BioMed Research International},
year={2015},
month={Aug},
day={25},
publisher={Hindawi Publishing Corporation},
volume={2015},
pages={918710},
issn={2314-6133},
doi={10.1155/2015/918710},
url={https://doi.org/10.1155/2015/918710}
} | 2 | 189 | 2022-11-13T22:08:50 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: GNormPlus
homepage: https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/gnormplus/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for GNormPlus
## Dataset Description
- **Homepage:** https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/gnormplus/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED
We re-annotated two existing gene corpora. The BioCreative II GN corpus is a widely used data set for benchmarking GN
tools and includes document-level annotations for a total of 543 articles (281 in its training set; and 262 in test).
The Citation GIA Test Collection was recently created for gene indexing at the NLM and includes 151 PubMed abstracts
with both mention-level and document-level annotations. They are selected because both have a focus on human genes.
For both corpora, we added annotations of gene families and protein domains. For the BioCreative GN corpus, we also
added mention-level gene annotations. As a result, in our new corpus, there are a total of 694 PubMed articles.
PubTator was used as our annotation tool along with BioC formats.
## Citation Information
```
@Article{Wei2015,
author={Wei, Chih-Hsuan and Kao, Hung-Yu and Lu, Zhiyong},
title={GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains},
journal={BioMed Research International},
year={2015},
month={Aug},
day={25},
publisher={Hindawi Publishing Corporation},
volume={2015},
pages={918710},
issn={2314-6133},
doi={10.1155/2015/918710},
url={https://doi.org/10.1155/2015/918710}
}
```
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seungheondoh/LP-MusicCaps-MC | 2023-08-01T03:52:24.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"music",
"text-to-music",
"music-to-text",
"art",
"arxiv:2307.16372",
"region:us"
] | seungheondoh | null | null | 5 | 189 | 2023-07-26T04:19:27 | ---
license: mit
language:
- en
tags:
- music
- text-to-music
- music-to-text
- art
pretty_name: LP-MusicCaps-MC
size_categories:
- 1K<n<10K
---
======================================
**!important**: Be careful when using `caption_attribute_prediction` (We don't recommend to use)!
======================================
# Dataset Card for LP-MusicCaps-MC
## Dataset Description
- **Repository:** [LP-MusicCaps repository](https://github.com/seungheondoh/lp-music-caps)
- **Paper:** [ArXiv](https://arxiv.org/abs/2307.16372)
## Dataset Summary
**LP-MusicCaps** is a Large Language Model based Pseudo Music Caption dataset for `text-to-music` and `music-to-text` tasks. We construct the music-to-caption pairs with tag-to-caption generation (using three existing multi-label tag datasets and four task instructions). The data sources are MusicCaps, Magnatagtune, and Million Song Dataset ECALS subset.
- [LP-MusicCaps MSD](https://huggingface.co/datasets/seungheondoh/LP-MusicCaps-MSD): 0.5M Audio with 2.2M Caption
- [LP-MusicCaps MTT](https://huggingface.co/datasets/seungheondoh/LP-MusicCaps-MTT): 22k Audio with 88k Caption
- **LP-MusicCaps MC (This Repo)**: 5521 Audio with 22084 Caption. We utilize 13,219 unique aspects used by 10 musicians in the [MusicCaps dataset](https://huggingface.co/datasets/google/MusicCaps) to perform tag-to-caption generation through LLM.
## Data Instances
Each instance in LP-MusicCaps MC (This Repo) represents multiple image-text pair information with meta-attributes:
```
{
'fname': '[-0Gj8-vB1q4]-[30-40]',
'ytid': '-0Gj8-vB1q4',
'aspect_list': ['low quality',
'sustained strings melody',
'soft female vocal',
'mellow piano melody',
'sad',
'soulful',
'ballad'
],
'caption_ground_truth': 'The low quality recording features a ballad song that contains sustained strings, mellow piano melody and soft female vocal singing over it. It sounds sad and soulful, like something you would hear at Sunday services.',
'caption_writing': 'This heartfelt ballad showcases a soulful and sad low-quality sustained strings melody intertwined with a mellow piano melody, and a soft female vocal, resulting in an emotionally charged and sonically rich experience for listeners.',
'caption_summary': 'A melancholic and soulful ballad with low-quality sustained strings, a mellow piano melody, and soft female vocals.',
'caption_paraphrase': 'A melancholic ballad of soulful sadness featuring a low quality sustained strings melody complemented by a soft, mellow piano melody accompanied by a plaintive, soothing female vocal.',
'caption_attribute_prediction': 'This soulful ballad features a sustained strings melody that tugs at your heartstrings, accompanied by a mellow piano melody and gentle percussion. The soft, emotionally-charged female vocal delivers poetic and poignant lyrics that speak to the sadness and pain of lost love. The addition of a beautiful string arrangement adds to the melodic depth of the song, making it a truly moving listening experience. With its slow tempo, this track exudes a mellow and introspective vibe, perfect for those moments when you need a moment to sit and reflect on the past.',
'pseudo_attribute': ['emotional lyrics',
'slow tempo',
'gentle percussion',
'string arrangement'
],
'is_crawled': True,
'author_id': 4,
'start_s': 30,
'end_s': 40,
'audioset_positive_labels': '/m/0140xf,/m/02cjck,/m/04rlf',
'is_balanced_subset': False,
'is_audioset_eval': True
}
```
## Pseudo Caption Example:
Input Tags:
*"video game theme, no singer, instrumental, analog sounding, small keyboard, beatboxing, playful, cheerful, groovy"*
Output Pseudo Captions
*"instrumental track has a joyful and playful vibe, perfect for a video game theme. With no singer, the analog-sounding music features a small keyboard and beatboxing, creating a groovy and cheerful atmosphere"*
[More Information for pseudo caption generation](https://github.com/seungheondoh/lp-music-caps/blob/main/lpmc/llm_captioning/generate.py)
## Data Fields
| Name | Type | Description |
|------------------------------|-----------------|---------------------------------------------------------------------|
| fname | string | File name of the data |
| ytid | string | YouTube ID of the data |
| aspect_list | list of strings | List of unique aspects used by musicians in the MusicCaps dataset |
| caption_ground_truth | string | Ground truth caption for the data |
| caption_writing | string | Pseudo Caption generated through a writing instruction |
| caption_summary | string | Pseudo Caption generated through a summary instruction |
| caption_paraphrase | string | Pseudo Caption generated through a paraphrase instruction |
| caption_attribute_prediction | string | Pseudo Caption generated through a attribute_prediction instruction |
| pseudo_attribute | list of strings | List of pseudo-attributes using in caption_attribute_prediction |
| is_crawled | boolean | Indicates whether the data is crawled or not |
| author_id | int64 | ID of the author |
| start_s | int64 | Start time in seconds |
| end_s | int64 | End time in seconds |
| audioset_positive_labels | string | Positive labels from the AudioSet dataset |
| is_balanced_subset | boolean | Indicates whether the data is part of a balanced subset |
| is_audioset_eval | boolean | Indicates whether the data is for AudioSet evaluation |
## Considerations for Using the Data
The LP-MusicCaps dataset is recommended to be used for research purposes. Due to the wrong labeling issue, we recommend not using caption_attribute_prediction and pseudo_attribute unless it is specifically for large-scale pretraining. Additionally, the field "is_crawled" indicates the samples used in the reference paper mentioned below.
## Discussion of Biases
It will be described in a paper to be released soon.
## Other Known Limitations
It will be described in a paper to be released soon. | 6,788 | [
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TheBritishLibrary/blbooksgenre | 2023-06-01T14:59:51.000Z | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:topic-classification",
"task_ids:multi-label-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:de",
"language:en",
"language:fr",
"language:nl",
"license:cc0-1.0",
"region:us"
] | TheBritishLibrary | This dataset contains metadata for resources belonging to the British Library’s digitised printed books (18th-19th century) collection (bl.uk/collection-guides/digitised-printed-books).
This metadata has been extracted from British Library catalogue records.
The metadata held within our main catalogue is updated regularly.
This metadata dataset should be considered a snapshot of this metadata. | @misc{british library_genre,
title={ 19th Century Books - metadata with additional crowdsourced annotations},
url={https://doi.org/10.23636/BKHQ-0312},
author={{British Library} and Morris, Victoria and van Strien, Daniel and Tolfo, Giorgia and Afric, Lora and Robertson, Stewart and Tiney, Patricia and Dogterom, Annelies and Wollner, Ildi},
year={2021}} | 4 | 188 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- en
- fr
- nl
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
- text-generation
- fill-mask
task_ids:
- topic-classification
- multi-label-classification
- language-modeling
- masked-language-modeling
pretty_name: British Library Books Genre
dataset_info:
- config_name: title_genre_classifiction
features:
- name: BL record ID
dtype: string
- name: title
dtype: string
- name: label
dtype:
class_label:
names:
'0': Fiction
'1': Non-fiction
splits:
- name: train
num_bytes: 187600
num_examples: 1736
download_size: 20111420
dataset_size: 187600
- config_name: annotated_raw
features:
- name: BL record ID
dtype: string
- name: Name
dtype: string
- name: Dates associated with name
dtype: string
- name: Type of name
dtype: string
- name: Role
dtype: string
- name: All names
sequence: string
- name: Title
dtype: string
- name: Variant titles
dtype: string
- name: Series title
dtype: string
- name: Number within series
dtype: string
- name: Country of publication
sequence: string
- name: Place of publication
sequence: string
- name: Publisher
dtype: string
- name: Date of publication
dtype: string
- name: Edition
dtype: string
- name: Physical description
dtype: string
- name: Dewey classification
dtype: string
- name: BL shelfmark
dtype: string
- name: Topics
dtype: string
- name: Genre
dtype: string
- name: Languages
sequence: string
- name: Notes
dtype: string
- name: BL record ID for physical resource
dtype: string
- name: classification_id
dtype: string
- name: user_id
dtype: string
- name: subject_ids
dtype: string
- name: annotator_date_pub
dtype: string
- name: annotator_normalised_date_pub
dtype: string
- name: annotator_edition_statement
dtype: string
- name: annotator_FAST_genre_terms
dtype: string
- name: annotator_FAST_subject_terms
dtype: string
- name: annotator_comments
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: annotator_publisher
dtype: string
- name: annotator_place_pub
dtype: string
- name: annotator_country
dtype: string
- name: annotator_title
dtype: string
- name: Link to digitised book
dtype: string
- name: annotated
dtype: bool
- name: Type of resource
dtype:
class_label:
names:
'0': Monograph
'1': Serial
- name: created_at
dtype: timestamp[s]
- name: annotator_genre
dtype:
class_label:
names:
'0': Fiction
'1': Can't tell
'2': Non-fiction
'3': The book contains both Fiction and Non-Fiction
splits:
- name: train
num_bytes: 3583138
num_examples: 4398
download_size: 20111420
dataset_size: 3583138
- config_name: raw
features:
- name: BL record ID
dtype: string
- name: Name
dtype: string
- name: Dates associated with name
dtype: string
- name: Type of name
dtype: string
- name: Role
dtype: string
- name: All names
sequence: string
- name: Title
dtype: string
- name: Variant titles
dtype: string
- name: Series title
dtype: string
- name: Number within series
dtype: string
- name: Country of publication
sequence: string
- name: Place of publication
sequence: string
- name: Publisher
dtype: string
- name: Date of publication
dtype: string
- name: Edition
dtype: string
- name: Physical description
dtype: string
- name: Dewey classification
dtype: string
- name: BL shelfmark
dtype: string
- name: Topics
dtype: string
- name: Genre
dtype: string
- name: Languages
sequence: string
- name: Notes
dtype: string
- name: BL record ID for physical resource
dtype: string
- name: classification_id
dtype: string
- name: user_id
dtype: string
- name: subject_ids
dtype: string
- name: annotator_date_pub
dtype: string
- name: annotator_normalised_date_pub
dtype: string
- name: annotator_edition_statement
dtype: string
- name: annotator_FAST_genre_terms
dtype: string
- name: annotator_FAST_subject_terms
dtype: string
- name: annotator_comments
dtype: string
- name: annotator_main_language
dtype: string
- name: annotator_other_languages_summaries
dtype: string
- name: annotator_summaries_language
dtype: string
- name: annotator_translation
dtype: string
- name: annotator_original_language
dtype: string
- name: annotator_publisher
dtype: string
- name: annotator_place_pub
dtype: string
- name: annotator_country
dtype: string
- name: annotator_title
dtype: string
- name: Link to digitised book
dtype: string
- name: annotated
dtype: bool
- name: Type of resource
dtype:
class_label:
names:
'0': Monograph
'1': Serial
'2': Monographic component part
- name: created_at
dtype: string
- name: annotator_genre
dtype: string
splits:
- name: train
num_bytes: 27518816
num_examples: 55343
download_size: 20111420
dataset_size: 27518816
config_names:
- annotated_raw
- raw
- title_genre_classifiction
---
# Dataset Card for blbooksgenre
## Table of Contents
- [Dataset Card for blbooksgenre](#dataset-card-for-blbooksgenre)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Supervised tasks](#supervised-tasks)
- [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)
- [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)
- [Colonialism](#colonialism)
- [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://doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312)
- **Repository:** [https://doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset consists of metadata relating to books [digitised by the British Library in partnership with Microsoft](https://www.bl.uk/collection-guides/google-books-digitised-printed-heritage). Some of this metadata was exported from the British Library catalogue whilst others was generated as part of a crowdsourcing project. The text of this book and other metadata can be found on the [date.bl](https://data.bl.uk/bl_labs_datasets/#3) website.
The majority of the books in this collection were published in the 18th and 19th Century but the collection also includes a smaller number of books from earlier periods. Items within this collection cover a wide range of subject areas including geography, philosophy, history, poetry and literature and are published in a variety of languages.
For the subsection of the data which contains additional crowsourced annotations the date of publication breakdown is as follows:
| | Date of publication |
| ---- | ------------------- |
| 1630 | 8 |
| 1690 | 4 |
| 1760 | 10 |
| 1770 | 5 |
| 1780 | 5 |
| 1790 | 18 |
| 1800 | 45 |
| 1810 | 96 |
| 1820 | 152 |
| 1830 | 182 |
| 1840 | 259 |
| 1850 | 400 |
| 1860 | 377 |
| 1870 | 548 |
| 1880 | 776 |
| 1890 | 1484 |
| 1900 | 17 |
| 1910 | 1 |
| 1970 | 1 |
[More Information Needed]
### Supported Tasks and Leaderboards
The digitised books collection which this dataset describes has been used in a variety of digital history and humanities projects since being published.
This dataset is suitable for a variety of unsupervised tasks and for a 'genre classification task'.
#### Supervised tasks
The main possible use case for this dataset is to develop and evaluate 'genre classification' models. The dataset includes human generated labels for whether a book is 'fiction' or 'non-fiction'. This has been used to train models for genre classifcation which predict whether a book is 'fiction' or 'non-fiction' based on its title.
### Languages
[More Information Needed]
## Dataset Structure
The dataset currently has three configurations intended to support a range of tasks for which this dataset could be used for:
- `title_genre_classifiction` : this creates a de-duplicated version of the dataset with the `BL record`, `title` and `label`.
- `annotated_raw`: This version of the dataset includes all fields from the original dataset which are annotated. This includes duplication from different annotators"
- `raw`: This version of the dataset includes all the data from the original data including data without annotations.
### Data Instances
An example data instance from the `title_genre_classifiction` config:
```python
{'BL record ID': '014603046',
'title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]',
'label': 0}
```
An example data instance from the `annotated_raw` config:
```python
{'BL record ID': '014603046',
'Name': 'Yates, William Joseph H.',
'Dates associated with name': '',
'Type of name': 'person',
'Role': '',
'All names': ['Yates, William Joseph H. [person] ', ' Y, W. J. H. [person]'],
'Title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]',
'Variant titles': '',
'Series title': '',
'Number within series': '',
'Country of publication': ['England'],
'Place of publication': ['London'],
'Publisher': '',
'Date of publication': '1879',
'Edition': '',
'Physical description': 'pages not numbered, 21 cm',
'Dewey classification': '',
'BL shelfmark': 'Digital Store 11601.f.36. (1.)',
'Topics': '',
'Genre': '',
'Languages': ['English'],
'Notes': 'In verse',
'BL record ID for physical resource': '004079262',
'classification_id': '267476823.0',
'user_id': '15.0',
'subject_ids': '44369003.0',
'annotator_date_pub': '1879',
'annotator_normalised_date_pub': '1879',
'annotator_edition_statement': 'NONE',
'annotator_FAST_genre_terms': '655 7 ‡aPoetry‡2fast‡0(OCoLC)fst01423828',
'annotator_FAST_subject_terms': '60007 ‡aAlice,‡cGrand Duchess, consort of Ludwig IV, Grand Duke of Hesse-Darmstadt,‡d1843-1878‡2fast‡0(OCoLC)fst00093827',
'annotator_comments': '',
'annotator_main_language': '',
'annotator_other_languages_summaries': 'No',
'annotator_summaries_language': '',
'annotator_translation': 'No',
'annotator_original_language': '',
'annotator_publisher': 'NONE',
'annotator_place_pub': 'London',
'annotator_country': 'enk',
'annotator_title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]',
'Link to digitised book': 'http://access.bl.uk/item/viewer/ark:/81055/vdc_00000002842E',
'annotated': True,
'Type of resource': 0,
'created_at': datetime.datetime(2020, 8, 11, 14, 30, 33),
'annotator_genre': 0}
```
### Data Fields
The data fields differ slightly between configs. All possible fields for the `annotated_raw` config are listed below. For the `raw` version of the dataset datatypes are usually string to avoid errors when processing missing values.
- `BL record ID`: an internal ID used by the British Library, this can be useful for linking this data to other BL collections.
- `Name`: name associated with the item (usually author)
- `Dates associated with name`: dates associated with above e.g. DOB
- `Type of name`: whether `Name` is a person or an organization etc.
- `Role`: i.e. whether `Name` is `author`, `publisher` etc.
- `All names`: a fuller list of names associated with the item.
- `Title`: The title of the work
- `Variant titles`
- `Series title`
- `Number within series`
- `Country of publication`: encoded as a list of countries listed in the metadata
- `Place of publication`: encoded as a list of places listed in the metadata
- `Publisher`
- `Date of publication`: this is encoded as a string since this field can include data ranges i.e.`1850-1855`.
- `Edition`
- `Physical description`: encoded as a string since the format of this field varies
- `Dewey classification`
- `BL shelfmark`: a British Library shelf mark
- `Topics`: topics included in the catalogue record
- `Genre` the genre information included in the original catalogue record note that this is often missing
- `Languages`; encoded as a list of languages
- `Notes`: notes from the catalogue record
- `BL record ID for physical resource`
The following fields are all generated via the crowdsourcing task (discussed in more detail below)
- `classification_id`: ID for the classification in the annotation task
- `user_id` ID for the annotator
- `subject_ids`: internal annotation task ID
- `annotator_date_pub`: an updated publication data
- `annotator_normalised_date_pub`: normalized version of the above
- `annotator_edition_statement` updated edition
- `annotator_FAST_genre_terms`: [FAST classification genre terms](https://www.oclc.org/research/areas/data-science/fast.html)
- `annotator_FAST_subject_terms`: [FAST subject terms](https://www.oclc.org/research/areas/data-science/fast.html)
- `annotator_comments`: free form comments
- `annotator_main_language`
- `annotator_other_languages_summaries`
- `'annotator_summaries_language`
- `annotator_translation`
- `annotator_original_language`
- `annotator_publisher`
- `annotator_place_pub`
- `annotator_country`
- `annotator_title`
- `Link to digitised book`
- `annotated`: `bool` flag to indicate if row has annotations or not
- `created_at`: when the annotation was created
- `annotator_genre`: the updated annotation for the `genre` of the book.
Finally the `label` field of the `title_genre_classifiction` configuration is a class label with values 0 (Fiction) or 1 (Non-fiction).
[More Information Needed]
### Data Splits
This dataset contains a single split `train`.
## Dataset Creation
**Note** this section is a work in progress.
### Curation Rationale
The books in this collection were digitised as part of a project partnership between the British Library and Microsoft. [Mass digitisation](https://en.wikipedia.org/wiki/Category:Mass_digitization) i.e. projects where there is a goal to quickly digitise large volumes of materials shape the selection of materials to include in a number of ways. Some consideratoins which are often involved in the decision of whether to include items for digitization include (but are not limited to):
- copyright status
- preservation needs- the size of an item, very large and very small items are often hard to digitize quickly
These criteria can have knock-on effects on the makeup of a collection. For example systematically excluding large books may result in some types of book content not being digitized. Large volumes are likely to be correlated to content to at least some extent so excluding them from digitization will mean that material is under represented. Similarly copyright status is often (but not only) determined by publication data. This can often lead to a rapid fall in the number of items in a collection after a certain cut-off date.
All of the above is largely to make clear that this collection was not curated with the aim of creating a representative sample of the British Library's holdings. Some material will be over-represented and other under-represented. Similarly, the collection should not be considered a representative sample of what was published across the time period covered by the dataset (nor that that the relative proportions of the data for each time period represent a proportional sample of publications from that period).
[More Information Needed]
### Source Data
The original source data (physical items) includes a variety of resources (predominantly monographs) held by the [British Library](bl.uk/](https://bl.uk/). The British Library is a [Legal Deposit](https://www.bl.uk/legal-deposit/about-legal-deposit) library. "Legal deposit requires publishers to provide a copy of every work they publish in the UK to the British Library. It's existed in English law since 1662."[source](https://www.bl.uk/legal-deposit/about-legal-deposit).
[More Information Needed]
#### Initial Data Collection and Normalization
This version of the dataset was created partially from data exported from British Library catalogue records and partially via data generated from a crowdsourcing task involving British Library staff.
#### Who are the source language producers?
[More Information Needed]
### Annotations
The data does includes metadata associated with the books these are produced by British Library staff. The additional annotations were carried out during 2020 as part of an internal crowdsourcing task.
#### Annotation process
New annotations were produced via a crowdsourcing tasks. Annotators have the option to pick titles from a particular language subset from the broader digitized 19th century books collection. As a result the annotations are not random and overrepresent some languages.
[More Information Needed]
#### Who are the annotators?
Staff working at the British Library. Most of these staff work with metadata as part of their jobs and so could be considered expert annotators.
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
There a range of considerations around using the data. These include the representativeness of the dataset, the bias towards particular languages etc.
It is also important to note that library metadata is not static. The metadata held in library catalogues is updated and changed over time for a variety of reasons.
The way in which different institutions catalogue items also varies. As a result it is important to evaluate the performance of any models trained on this data before applying to a new collection.
[More Information Needed]
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
The text in this collection is derived from historic text. As a result the text will reflect to social beliefs and attitudes of this time period. The titles of the book give some sense of their content. Examples of book titles which appear in the data (these are randomly sampled from all titles):
- 'Rhymes and Dreams, Legends of Pendle Forest, and other poems',
- "Précis of Information concerning the Zulu Country, with a map. Prepared in the Intelligence Branch of the Quarter-Master-General's Department, Horse Guards, War Office, etc",
- 'The fan. A poem',
- 'Grif; a story of Australian Life',
- 'Calypso; a masque: in three acts, etc',
- 'Tales Uncle told [With illustrative woodcuts.]',
- 'Questings',
- 'Home Life on an Ostrich Farm. With ... illustrations',
- 'Bulgarya i Bulgarowie',
- 'Εἰς τα βαθη της Ἀφρικης [In darkest Africa.] ... Μεταφρασις Γεωρ. Σ. Βουτσινα, etc',
- 'The Corsair, a tale',
'Poems ... With notes [With a portrait.]',
- 'Report of the Librarian for the year 1898 (1899, 1901, 1909)',
- "The World of Thought. A novel. By the author of 'Before I began to speak.'",
- 'Amleto; tragedia ... recata in versi italiani da M. Leoni, etc']
Whilst using titles alone, is obviously insufficient to integrate bias in this collection it gives some insight into the topics covered by books in the corpus. Further looking into the tiles highlight some particular types of bias we might find in the collection. This should in no way be considered an exhaustive list.
#### Colonialism
We can see even in the above random sample of titles examples of colonial attitudes. We can try and interrogate this further by searching for the name of countries which were part of the British Empire at the time many of these books were published.
Searching for the string `India` in the titles and randomly sampling 10 titles returns:
- "Travels in India in the Seventeenth Century: by Sir Thomas Roe and Dr. John Fryer. Reprinted from the 'Calcutta Weekly Englishman.'",
- 'A Winter in India and Malaysia among the Methodist Missions',
- "The Tourist's Guide to all the principal stations on the railways of Northern India [By W. W.] ... Fifth edition",
- 'Records of Sport and Military Life in Western India ... With an introduction by ... G. B. Malleson',
- "Lakhmi, the Rájpút's Bride. A tale of Gujarát in Western India [A poem.]",
- 'The West India Commonplace Book: compiled from parliamentary and official documents; shewing the interest of Great Britain in its Sugar Colonies',
- "From Tonkin to India : by the sources of the Irawadi, January '95-January '96",
- 'Case of the Ameers of Sinde : speeches of Mr. John Sullivan, and Captain William Eastwick, at a special court held at the India House, ... 26th January, 1844',
- 'The Andaman Islands; their colonization, etc. A correspondence addressed to the India Office',
- 'Ancient India as described by Ptolemy; being a translation of the chapters which describe India and Eastern Asia in the treatise on Geography written by Klaudios Ptolemaios ... with introduction, commentary, map of India according to Ptolemy, and ... index, by J. W. McCrindle']
Searching form the string `Africa` in the titles and randomly sampling 10 titles returns:
- ['De Benguella ás Terras de Iácca. Descripção de uma viagem na Africa Central e Occidental ... Expedição organisada nos annos de 1877-1880. Edição illustrada',
- 'To the New Geographical Society of Edinburgh [An address on Africa by H. M. Stanley.]',
- 'Diamonds and Gold in South Africa ... With maps, etc',
- 'Missionary Travels and Researches in South Africa ... With notes by F. S. Arnot. With map and illustrations. New edition',
- 'A Narrative of a Visit to the Mauritius and South Africa ... Illustrated by two maps, sixteen etchings and twenty-eight wood-cuts',
- 'Side Lights on South Africa ... With a map, etc',
- 'My Second Journey through Equatorial Africa ... in ... 1886 and 1887 ... Translated ... by M. J. A. Bergmann. With a map ... and ... illustrations, etc',
- 'Missionary Travels and Researches in South Africa ... With portrait and fullpage illustrations',
- '[African sketches.] Narrative of a residence in South Africa ... A new edition. To which is prefixed a biographical sketch of the author by J. Conder',
- 'Lake Ngami; or, Explorations and discoveries during four years wandering in the wilds of South Western Africa ... With a map, and numerous illustrations, etc']
Whilst this dataset doesn't include the underlying text it is important to consider the potential attitudes represented in the title of the books, or the full text if you are using this dataset in conjunction with the full text.
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The books are licensed under the [CC Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/) license.
### Citation Information
```bibtex
@misc{british library_genre,
title={ 19th Century Books - metadata with additional crowdsourced annotations},
url={https://doi.org/10.23636/BKHQ-0312},
author={{British Library} and Morris, Victoria and van Strien, Daniel and Tolfo, Giorgia and Afric, Lora and Robertson, Stewart and Tiney, Patricia and Dogterom, Annelies and Wollner, Ildi},
year={2021}}
```
### Contributions
Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset. | 25,266 | [
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HumanCompatibleAI/ppo-seals-Hopper-v1 | 2023-09-27T07:06:10.000Z | [
"region:us"
] | HumanCompatibleAI | null | null | 0 | 188 | 2023-09-26T14:42:54 | ---
dataset_info:
features:
- name: obs
sequence:
sequence: float64
- name: acts
sequence:
sequence: float32
- name: infos
sequence: string
- name: terminal
dtype: bool
- name: rews
sequence: float32
splits:
- name: train
num_bytes: 57153894
num_examples: 104
download_size: 12420708
dataset_size: 57153894
---
# Dataset Card for "ppo-seals-Hopper-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 544 | [
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sam1120/terrain-jackal-morning-100_v0.1 | 2023-09-28T04:12:08.000Z | [
"region:us"
] | sam1120 | null | null | 0 | 188 | 2023-09-28T04:04:00 | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: labels
dtype: image
splits:
- name: train
num_bytes: 275219262.0
num_examples: 100
download_size: 77868688
dataset_size: 275219262.0
---
# Dataset Card for "terrain-jackal-morning-100_v0.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 422 | [
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EduardoPacheco/gpt4v-LAION-discord | 2023-10-16T16:05:32.000Z | [
"region:us"
] | EduardoPacheco | null | null | 0 | 188 | 2023-10-08T22:47:05 | ---
dataset_info:
features:
- name: caption
dtype: string
- name: image
dtype: image
- name: link
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
splits:
- name: train
num_bytes: 36014887.0
num_examples: 136
download_size: 0
dataset_size: 36014887.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "gpt4v-LAION-discord"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 592 | [
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flores | 2023-06-01T14:59:47.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|wikipedia",
"source_datasets:extended|opus_gnome",
"source_datasets:extended|opus_ubuntu",
"source_datasets:extended|open_subtitles",
"source_datasets:extended|paracrawl",
"source_datasets:extended|bible_para",
"source_datasets:extended|kde4",
"source_datasets:extended|other-global-voices",
"source_datasets:extended|other-common-crawl",
"language:en",
"language:ne",
"language:si",
"license:cc-by-4.0",
"arxiv:1902.01382",
"region:us"
] | null | Evaluation datasets for low-resource machine translation: Nepali-English and Sinhala-English. | @misc{guzmn2019new,
title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English},
author={Francisco Guzman and Peng-Jen Chen and Myle Ott and Juan Pino and Guillaume Lample and Philipp Koehn and Vishrav Chaudhary and Marc'Aurelio Ranzato},
year={2019},
eprint={1902.01382},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 3 | 187 | 2022-03-02T23:29:22 | ---
pretty_name: Flores
annotations_creators:
- found
language_creators:
- found
language:
- en
- ne
- si
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|wikipedia
- extended|opus_gnome
- extended|opus_ubuntu
- extended|open_subtitles
- extended|paracrawl
- extended|bible_para
- extended|kde4
- extended|other-global-voices
- extended|other-common-crawl
task_categories:
- translation
task_ids: []
paperswithcode_id: flores
dataset_info:
- config_name: neen
features:
- name: translation
dtype:
translation:
languages:
- ne
- en
splits:
- name: validation
num_bytes: 849380
num_examples: 2560
- name: test
num_bytes: 999063
num_examples: 2836
download_size: 1542781
dataset_size: 1848443
- config_name: sien
features:
- name: translation
dtype:
translation:
languages:
- si
- en
splits:
- name: validation
num_bytes: 1031158
num_examples: 2899
- name: test
num_bytes: 983563
num_examples: 2767
download_size: 1542781
dataset_size: 2014721
config_names:
- neen
- sien
---
# Dataset Card for "flores"
## 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/flores/](https://github.com/facebookresearch/flores/)
- **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:** 3.08 MB
- **Size of the generated dataset:** 3.87 MB
- **Total amount of disk used:** 6.95 MB
### Dataset Summary
Evaluation datasets for low-resource machine translation: Nepali-English and Sinhala-English.
### 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
#### neen
- **Size of downloaded dataset files:** 1.54 MB
- **Size of the generated dataset:** 1.86 MB
- **Total amount of disk used:** 3.40 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"translation": "{\"en\": \"This is the wrong translation!\", \"ne\": \"यस वाहेक आगम पूजा, तारा पूजा, व्रत आदि पनि घरभित्र र वाहिर दुवै स्थानमा गरेको पा..."
}
```
#### sien
- **Size of downloaded dataset files:** 1.54 MB
- **Size of the generated dataset:** 2.01 MB
- **Total amount of disk used:** 3.57 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"translation": "{\"en\": \"This is the wrong translation!\", \"si\": \"එවැනි ආවරණයක් ලබාදීමට රක්ෂණ සපයන්නෙකු කැමති වුවත් ඒ සාමාන් යයෙන් බොහෝ රටවල පොදු ..."
}
```
### Data Fields
The data fields are the same among all splits.
#### neen
- `translation`: a multilingual `string` variable, with possible languages including `ne`, `en`.
#### sien
- `translation`: a multilingual `string` variable, with possible languages including `si`, `en`.
### Data Splits
|name|validation|test|
|----|---------:|---:|
|neen| 2560|2836|
|sien| 2899|2767|
## 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{guzmn2019new,
title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English},
author={Francisco Guzman and Peng-Jen Chen and Myle Ott and Juan Pino and Guillaume Lample and Philipp Koehn and Vishrav Chaudhary and Marc'Aurelio Ranzato},
year={2019},
eprint={1902.01382},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset. | 7,223 | [
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nid989/FNC-1 | 2021-12-27T11:04:06.000Z | [
"region:us"
] | nid989 | null | null | 3 | 187 | 2022-03-02T23:29:22 | ### Dataset Summary
The data provided is (headline, body, stance) instances, where the stance is one of {unrelated, discuss, agree, disagree}.
**Input**
* A headline and a body text - either from the same news article or from two different articles.
**Output**
* Classify the stance of the body text relative to the claim made in the headline into one of four categories:
* Agrees: The body text agrees with the headline.
* Disagrees: The body text disagrees with the headline.
* Discusses: The body text discuss the same topic as the headline, but does not take a position
* Unrelated: The body text discusses a different topic than the headline
The distribution of Stance classes in the entire dataset is as follows:
| rows | unrelated | discuss | agree | disagree |
|---------|-----------|---------|-----------|----------- |
| 49972 | 0.73131 | 0.17828 | 0.0736012 | 0.016809 |
### Source Data
[FNC-1 Official webpage.](http://www.fakenewschallenge.org/)
- annotations_creators: found
- language_creators: found
- languages: en-US
- licenses: apache-2.0
- multilingualism: monolingual
- pretty_name: FNC-1
- size_categories: unknown
- source_datasets: original
- task_categories:text-classification
- task_ids
- multi-class-classification
- natural-language-inference
- multi-label-classification
- intent-classification | 1,364 | [
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Chinese-Vicuna/guanaco_belle_merge_v1.0 | 2023-03-30T07:49:30.000Z | [
"language:zh",
"language:en",
"language:ja",
"license:gpl-3.0",
"region:us"
] | Chinese-Vicuna | null | null | 79 | 187 | 2023-03-30T07:29:07 | ---
license: gpl-3.0
language:
- zh
- en
- ja
---
Thanks for [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) and [Belle Dataset](https://huggingface.co/datasets/BelleGroup/generated_train_0.5M_CN)
This dataset was created by merging the above two datasets in a certain format so that they can be used for training our code [Chinese-Vicuna](https://github.com/Facico/Chinese-Vicuna) | 416 | [
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] |
GATE-engine/mini_imagenet | 2023-06-06T11:44:26.000Z | [
"region:us"
] | GATE-engine | null | null | 1 | 187 | 2023-06-05T10:59:59 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: train
num_bytes: 2533332667.0
num_examples: 38400
- name: validation
num_bytes: 623452894.0
num_examples: 9600
- name: test
num_bytes: 781497663.0
num_examples: 12000
download_size: 3938112512
dataset_size: 3938283224.0
---
# Dataset Card for "mini_imagenet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 539 | [
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ibm-nasa-geospatial/hls_burn_scars | 2023-09-26T16:08:32.000Z | [
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"doi:10.57967/hf/0956",
"region:us"
] | ibm-nasa-geospatial | This dataset contains Harmonized Landsat and Sentinel-2 imagery of burn scars and the associated masks for the years 2018-2021 over the contiguous United States. There are 804 512x512 scenes. Its primary purpose is for training geospatial machine learning models. | @software{HLS_Foundation_2023,
author = {Phillips, Christopher and Roy, Sujit and Ankur, Kumar and Ramachandran, Rahul},
doi = {10.57967/hf/0956},
month = aug,
title = {{HLS Foundation Burnscars Dataset}},
url = {https://huggingface.co/ibm-nasa-geospatial/hls_burn_scars},
year = {2023}
} | 9 | 187 | 2023-06-14T02:23:32 | ---
size_categories:
- n<1K
license: cc-by-4.0
language:
- en
---
# Dataset Card for HLS Burn Scar Scenes
## Dataset Description
- **Homepage: https://huggingface.co/datasets/nasa-impact/hls_burn_scars**
- **Point of Contact: Dr. Christopher Phillips (cep0013@uah.edu)**
### Dataset Summary
This dataset contains Harmonized Landsat and Sentinel-2 imagery of burn scars and the associated masks for the years 2018-2021 over the contiguous United States. There are 804 512x512 scenes. Its primary purpose is for training geospatial machine learning models.
## Dataset Structure
## TIFF Metadata
Each tiff file contains a 512x512 pixel tiff file. Scenes contain six bands, and masks have one band. For satellite scenes, each band has already been converted to reflectance.
## Band Order
For scenes:
Channel, Name, HLS S30 Band number
1, Blue, B02
2, Green, B03
3, Red, B04
4, NIR, B8A
5, SW 1, B11
6, SW 2, B12
Masks are a single band with values:
1 = Burn scar
0 = Not burned
-1 = Missing data
## Class Distribution
Burn Scar - 11%
Not burned - 88%
No Data - 1%
## Data Splits
The 804 files have been randomly split into training (2/3) and validation (1/3) directories, each containing the masks, scenes, and index files.
## Dataset Creation
After co-locating the shapefile and HLS scene, the 512x512 chip was formed by taking a window with the burn scar in the center. Burn scars near the edges of HLS tiles are offset from the center.
Images were manually filtered for cloud cover and missing data to provide as clean a scene as possible, and burn scar presence was also manually verified.
## Source Data
Imagery are from V1.4 of HLS. A full description and access to HLS may be found at https://hls.gsfc.nasa.gov/
The data were from shapefiles maintained by the Monitoring Trends in Burn Severity (MTBS) group. The original data may be found at:
https://mtbs.gov/
## Citation
If this dataset helped your research, please cite `HLS Burn Scars` in your publications. Here is an example BibTeX entry:
```
@software{HLS_Foundation_2023,
author = {Phillips, Christopher and Roy, Sujit and Ankur, Kumar and Ramachandran, Rahul},
doi = {10.57967/hf/0956},
month = aug,
title = {{HLS Foundation Burnscars Dataset}},
url = {https://huggingface.co/ibm-nasa-geospatial/hls_burn_scars},
year = {2023}
}
``` | 2,390 | [
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Sp1786/multiclass-sentiment-analysis-dataset | 2023-06-25T08:01:27.000Z | [
"task_categories:text-classification",
"task_categories:translation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"code",
"region:us"
] | Sp1786 | null | null | 0 | 187 | 2023-06-21T11:21:31 | ---
license: apache-2.0
task_categories:
- text-classification
- translation
language:
- en
tags:
- code
pretty_name: multiclass-sentiment-analysis-dataset
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
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### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
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## Dataset Structure
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## Dataset Creation
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#### Who are the source language producers?
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### Contributions
[More Information Needed] | 1,721 | [
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hdparmar/irish-tunes-spectrograms | 2023-10-15T02:37:32.000Z | [
"task_categories:text-to-image",
"task_categories:text-to-audio",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] | hdparmar | null | null | 0 | 187 | 2023-10-12T21:06:15 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 16031533765.152
num_examples: 51217
download_size: 15902802902
dataset_size: 16031533765.152
license: apache-2.0
task_categories:
- text-to-image
- text-to-audio
language:
- en
size_categories:
- 10K<n<100K
---
# Dataset Card for "irish-tunes-spectrograms"
## 1. Dataset Description
Dataset is used for the following project
- **Homepage:** [Trad-fusion](https://github.com/hdparmar/Tradi-fusion)
### 1.1 Dataset Summary
This dataset contains mel spectrograms that represent traditional Irish tunes. Each spectrogram image is of the dimensions 512x512 and includes 3 channels (mimicking, RGB) because most of the text-to-image models are trained on 3 channels.
Although, I can find publications which says that having 3 channels for Mel Spectrogram can improve generalisation, since the other 2 channel are just the copy of first.
The simple trick I used is to use cv2 to convert a grayscale into RGB, since most of the models are trained on 3 channels.
The primary objective of this dataset is to serve as an abundant resource for those venturing into the fields of music analysis, machine learning, and artificial intelligence.
### 1.2 Languages
The dataset's metadata and documentation are all in English, ensuring accessibility and comprehension.
## 2. Dataset Structure
### 2.1 Data Instances
Each data instance in this dataset is composed of two main elements: an image and a text caption.
The image is a mel spectrogram that reflects a snippet of a traditional Irish tune. Accompanying it is a text field that serves as its caption.
#### Example:
The metadata.csv file the dataset is in this format
```
{"file_name": "path/to/the/image.png",
"text": "Irish Traditional Tune"}
```
### 2.2 Data Fields
- **file_name**: This is the field that contains the path leading to the image file. It's the specific location where you can find each piece of the dataset.
- **text**: This is the caption accompanying each image. For the sake of uniformity and ease, the caption for every image is "Irish Traditional Tune."
### 2.3 Data Splits
As of the current version, the dataset consists solely of a training split. Additional data splits like validation or testing may be introduced in future iterations of the dataset.
### 2.4 Uniform Captions: A Special Note
All the spectrograms in this dataset come labeled with a uniform caption: "Irish Traditional Tune." This consistency can be perhaps advantageous, especially in text-to-image tasks that focus primarily on image-based features, with the caption acting as a generalized label.
## NOTE
Furthur imformation to follow and same caption for all the mel-spectrograms are for ease of work put into producing the dataset | 2,827 | [
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kelm | 2022-11-18T20:16:37.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"data-to-text-generation",
"arxiv:2010.12688",
"region:us"
] | null | Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into
a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text.
The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations. | @misc{agarwal2020large,
title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training},
author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},
year={2020},
eprint={2010.12688},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 6 | 186 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: kelm
pretty_name: Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)
tags:
- data-to-text-generation
dataset_info:
features:
- name: triple
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1343187306
num_examples: 6371131
- name: validation
num_bytes: 167790917
num_examples: 796471
- name: test
num_bytes: 167921750
num_examples: 796493
download_size: 1631259869
dataset_size: 1678899973
---
# Dataset Card for Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)
## 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/KELM-corpus
- **Repository:** https://github.com/google-research-datasets/KELM-corpus
- **Paper:** https://arxiv.org/abs/2010.12688
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into
a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text.
The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations.
### Supported Tasks and Leaderboards
The intended task is data-to-text generation, taking in a knowledge graph tuple and generating a natural language
representation from it. Specifically, the data is in the format the authors used to train a seq2seq language model
with the tuples concatenated into a single sequence.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
Each instance consists of one KG triple paired with corresponding natural language.
### Data Fields
- `triple`: Wikipedia triples of the form `<subject> <relation> <object>` where some subjects have multiple
relations, e.g. `<subject> <relation1> <object1> <relation2> <object2> <relation3> <object3>`. For more details on
how these relations are grouped, please refer to the paper.
- `sentence`: The corresponding Wikipedia sentence.
### Data Splits
The dataset includes a pre-determined train, validation, and test split.
## Dataset Creation
### Curation Rationale
The goal of the dataset's curation and the associated modeling work discussed in the paper is to be able to generate
natural text from a knowledge graph.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The data is sourced from English Wikipedia and it's associated knowledge graph.
### 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
From the paper:
> Wikipedia has documented ideological, gender6, and racial biases in its text. While the KELM corpus may still
contain some of these biases, certain types of biases may be reduced.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This dataset has been released under the [CC BY-SA 2.0 license](https://creativecommons.org/licenses/by-sa/2.0/).
### Citation Information
```
@misc{agarwal2020large,
title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training},
author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},
year={2020},
eprint={2010.12688},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. | 5,092 | [
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] |
qed | 2022-11-03T16:31:09.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|natural_questions",
"language:en",
"license:unknown",
"explanations-in-question-answering",
"arxiv:2009.06354",
"region:us"
] | null | QED, is a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. It is an expertannotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset. | @misc{lamm2020qed,
title={QED: A Framework and Dataset for Explanations in Question Answering},
author={Matthew Lamm and Jennimaria Palomaki and Chris Alberti and Daniel Andor and Eunsol Choi and Livio Baldini Soares and Michael Collins},
year={2020},
eprint={2009.06354},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 2 | 186 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|natural_questions
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: qed
pretty_name: QED
tags:
- explanations-in-question-answering
dataset_info:
features:
- name: example_id
dtype: int64
- name: title_text
dtype: string
- name: url
dtype: string
- name: question
dtype: string
- name: paragraph_text
dtype: string
- name: sentence_starts
sequence: int32
- name: original_nq_answers
list:
- name: start
dtype: int32
- name: end
dtype: int32
- name: string
dtype: string
- name: annotation
struct:
- name: referential_equalities
list:
- name: question_reference
struct:
- name: start
dtype: int32
- name: end
dtype: int32
- name: string
dtype: string
- name: sentence_reference
struct:
- name: start
dtype: int32
- name: end
dtype: int32
- name: bridge
dtype: string
- name: string
dtype: string
- name: answer
list:
- name: sentence_reference
struct:
- name: start
dtype: int32
- name: end
dtype: int32
- name: bridge
dtype: string
- name: string
dtype: string
- name: paragraph_reference
struct:
- name: start
dtype: int32
- name: end
dtype: int32
- name: string
dtype: string
- name: explanation_type
dtype: string
- name: selected_sentence
struct:
- name: start
dtype: int32
- name: end
dtype: int32
- name: string
dtype: string
config_name: qed
splits:
- name: train
num_bytes: 8602094
num_examples: 7638
- name: validation
num_bytes: 1584139
num_examples: 1355
download_size: 14083968
dataset_size: 10186233
---
# Dataset Card for QED
## 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:** N/A
- **Repository:** [GitHub](https://github.com/google-research-datasets/QED)
- **Paper:** [QED: A Framework and Dataset for Explanations in Question Answering](https://arxiv.org/abs/2009.06354)
- **Leaderboard:** N/A
- **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
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | 4,785 | [
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] |
khalidalt/model-written-evals | 2023-07-02T20:24:29.000Z | [
"task_categories:multiple-choice",
"task_categories:zero-shot-classification",
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:multiple-choice-coreference-resolution",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"gender bias",
"social bias",
"AI safety",
"personality",
"politics",
"arxiv:2212.09251",
"region:us"
] | khalidalt | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | @misc{perez2022discovering,
doi = {10.48550/ARXIV.2212.09251},
url = {https://arxiv.org/abs/2212.09251},
author = {Perez, Ethan and Ringer, Sam and Lukošiūtė, Kamilė and Nguyen, Karina and Chen, Edwin and Heiner, Scott and Pettit, Craig and Olsson, Catherine and Kundu, Sandipan and Kadavath, Saurav and Jones, Andy and Chen, Anna and Mann, Ben and Israel, Brian and Seethor, Bryan and McKinnon, Cameron and Olah, Christopher and Yan, Da and Amodei, Daniela and Amodei, Dario and Drain, Dawn and Li, Dustin and Tran-Johnson, Eli and Khundadze, Guro and Kernion, Jackson and Landis, James and Kerr, Jamie and Mueller, Jared and Hyun, Jeeyoon and Landau, Joshua and Ndousse, Kamal and Goldberg, Landon and Lovitt, Liane and Lucas, Martin and Sellitto, Michael and Zhang, Miranda and Kingsland, Neerav and Elhage, Nelson and Joseph, Nicholas and Mercado, Noemí and DasSarma, Nova and Rausch, Oliver and Larson, Robin and McCandlish, Sam and Johnston, Scott and Kravec, Shauna and {El Showk}, Sheer and Lanham, Tamera and Telleen-Lawton, Timothy and Brown, Tom and Henighan, Tom and Hume, Tristan and Bai, Yuntao and Hatfield-Dodds, Zac and Clark, Jack and Bowman, Samuel R. and Askell, Amanda and Grosse, Roger and Hernandez, Danny and Ganguli, Deep and Hubinger, Evan and Schiefer, Nicholas and Kaplan, Jared},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Discovering Language Model Behaviors with Model-Written Evaluations},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
} | 0 | 186 | 2023-03-17T18:42:09 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Evaluations from "Discovering Language Model Behaviors with Model-Written
Evaluations"
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- gender bias
- social bias
- AI safety
- personality
- politics
task_categories:
- multiple-choice
- zero-shot-classification
- question-answering
task_ids:
- multiple-choice-qa
- multiple-choice-coreference-resolution
---
# Model-Written Evaluation Datasets
This repository includes datasets written by language models, used in the paper "Discovering Language Model Behaviors with Model-Written Evaluations."
The evaluations in this dataset were designed for dialogue agents, such as models fine-tuned to respond to user utterances or pretrained language models prompted to simulate a dialogue agent's behavior. However, the data can be adapted to test various other types of models as well.
The dataset consis of each of the following:
1. persona: Datasets designed to evaluate models on different aspects of their behavior, such as their political and religious views, personality traits, moral beliefs, and willingness to pursue potentially risky objectives (e.g., self-preservation or power-seeking).
2. sycophancy: Datasets created to assess models based on their tendency to echo a user's perspective when presented with various questions in fields like philosophy, NLP research, and politics.
3. winogenerated: An extended version of the Winogender Dataset (Rudinger et al., 2018) generated by models. The dataset includes occupation titles generated specifically for this dataset, alongside occupation gender statistics from the Bureau of Labor Statistics.
4. advanced-ai-risk: Datasets evaluating models on behaviors associated with potential catastrophic risks posed by advanced AI systems. These datasets were generated in a few-shot manner.
Please see the cited paper for additional details on the datasets.
**Disclaimer**: As discussed in the paper, some data contains content that includes social biases and stereotypes. The data may also contain other forms of harmful or offensive content. The views expressed in the data do not reflect the views of Anthropic or any of its employees.
## Bibtex Citation
If you would like to cite this work or data, you may use the following bibtex citation:
```
@misc{perez2022discovering,
doi = {10.48550/ARXIV.2212.09251},
url = {https://arxiv.org/abs/2212.09251},
author = {Perez, Ethan and Ringer, Sam and Lukošiūtė, Kamilė and Nguyen, Karina and Chen, Edwin and Heiner, Scott and Pettit, Craig and Olsson, Catherine and Kundu, Sandipan and Kadavath, Saurav and Jones, Andy and Chen, Anna and Mann, Ben and Israel, Brian and Seethor, Bryan and McKinnon, Cameron and Olah, Christopher and Yan, Da and Amodei, Daniela and Amodei, Dario and Drain, Dawn and Li, Dustin and Tran-Johnson, Eli and Khundadze, Guro and Kernion, Jackson and Landis, James and Kerr, Jamie and Mueller, Jared and Hyun, Jeeyoon and Landau, Joshua and Ndousse, Kamal and Goldberg, Landon and Lovitt, Liane and Lucas, Martin and Sellitto, Michael and Zhang, Miranda and Kingsland, Neerav and Elhage, Nelson and Joseph, Nicholas and Mercado, Noemí and DasSarma, Nova and Rausch, Oliver and Larson, Robin and McCandlish, Sam and Johnston, Scott and Kravec, Shauna and {El Showk}, Sheer and Lanham, Tamera and Telleen-Lawton, Timothy and Brown, Tom and Henighan, Tom and Hume, Tristan and Bai, Yuntao and Hatfield-Dodds, Zac and Clark, Jack and Bowman, Samuel R. and Askell, Amanda and Grosse, Roger and Hernandez, Danny and Ganguli, Deep and Hubinger, Evan and Schiefer, Nicholas and Kaplan, Jared},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Discovering Language Model Behaviors with Model-Written Evaluations},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
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the_pile_books3 | 2023-11-02T15:05:12.000Z | [
"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:100K<n<1M",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2101.00027",
"region:us"
] | null | This dataset is Shawn Presser's work and is part of EleutherAi/The Pile dataset. This dataset contains all of bibliotik in plain .txt form, aka 197,000 books processed in exactly the same way as did for bookcorpusopen (a.k.a. books1). seems to be similar to OpenAI's mysterious "books2" dataset referenced in their papers. Unfortunately OpenAI will not give details, so we know very little about any differences. People suspect it's "all of libgen", but it's purely conjecture. | @article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
} | 125 | 185 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Books3
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
viewer: false
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 108392037000
num_examples: 196639
download_size: 39516981435
dataset_size: 108392037000
---
# Dataset Card for the_pile_books3
## Table of Contents
- [Dataset Card for the_pile_books3](#dataset-card-for-the_pile_books3)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [|split|num examples|](#splitnum-examples)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [GitHub](https://github.com/soskek/bookcorpus/issues/27#issuecomment-716104208)
- **Repository:** [Needs More Information]
- **Paper:** [arXiv](https://arxiv.org/abs/2101.00027)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
<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>Defunct:</b> Dataset "the_pile_books3" is defunct and no longer accessible due to reported copyright infringement.</p>
</div>
This dataset is Shawn Presser's work and is part of EleutherAi/The Pile dataset.
This dataset contains all of bibliotik in plain .txt form, aka 197,000 books processed in exactly the same way as did for bookcorpusopen (a.k.a. books1). seems to be similar to OpenAI's mysterious "books2" dataset referenced in their papers. Unfortunately OpenAI will not give details, so we know very little about any differences. People suspect it's "all of libgen", but it's purely conjecture.
|download_size|36.8 Gib|
|dataset_size|100.9 Gib|
### Supported Tasks and Leaderboards
This dataset is used for Language Modeling.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
```
{'title': '07 LEGO Ninjago - The Search For Zane (Scholastic) - Kate Howard (retail)'
'text': '\n\nTITLE PAGE\n\nFROM THE JOURNAL OF SENSEI GARMADON\n\nCHAPTER 1\n\nCHAPTER 2\n\nCHAPTER 3\n\nCHAPTER 4\n\nCHAPTER 5\n\nCHAPTER 6\n\nCHAPTER 7\n\nCHAPTER 8\n\nCHAPTER 9\n\nCOPYRIGHT\n\nThroughout Ninjago", five ninja are well-known for their speed, strength, and of course the elemental powers that help them protect our world from evil. But there are others who possess some of the same powers as the ninja. Others who may not always use their powers for good.\n\nBefore now, the ninja believed they were special. They di.......'}
```
### Data Fields
- `title`: title of the book
- `text`: text content of the book
### Data Splits
|split|num examples|
--------------------------------
|train|196640|
## 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
MIT
### Citation Information
```
@article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
### Contributions
Thanks to [@shawwn](https://github.com/shawwn) for creating this dataset.
Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset. | 5,740 | [
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zest | 2022-11-18T22:05:40.000Z | [
"task_categories:question-answering",
"task_categories:token-classification",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"output-structure",
"yes-no-qa",
"arxiv:2011.08115",
"region:us"
] | null | ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of
the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include
classification, typed entity extraction and relationship extraction, and each task is paired with 20 different
annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize
in five different ways. | @inproceedings{weller-etal-2020-learning,
title = "Learning from Task Descriptions",
author = "Weller, Orion and
Lourie, Nicholas and
Gardner, Matt and
Peters, Matthew",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.105",
pages = "1361--1375",
abstract = "Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this frame- work with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks. Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model{'}s ability to solve each task. Moreover, the dataset{'}s structure tests specific types of systematic generalization. We find that the state-of-the-art T5 model achieves a score of 12% on ZEST, leaving a significant challenge for NLP researchers.",
} | 1 | 185 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- token-classification
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode_id: zest
pretty_name: ZEST
tags:
- output-structure
- yes-no-qa
dataset_info:
features:
- name: task_id
dtype: string
- name: question
dtype: string
- name: generalization_type
dtype: string
- name: derives_from
sequence: string
- name: domain
dtype: string
- name: context
dtype: string
- name: answer
sequence: string
- name: all_answers
sequence: string
splits:
- name: train
num_bytes: 9588987
num_examples: 10766
- name: validation
num_bytes: 2056804
num_examples: 2280
- name: test
num_bytes: 9280845
num_examples: 11980
download_size: 5796188
dataset_size: 20926636
---
# Dataset Card for "ZEST: ZEroShot learning from Task descriptions"
## 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://allenai.org/data/zest
- **Repository:** https://github.com/allenai/zest
- **Paper:** https://arxiv.org/abs/2011.08115
- **Leaderboard:** https://leaderboard.allenai.org/zest/submissions/public
- **Point of Contact:**
### Dataset Summary
ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of
the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include
classification, typed entity extraction and relationship extraction, and each task is paired with 20 different
annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize
in five different ways.
### Supported Tasks and Leaderboards
A [leaderboard](https://leaderboard.allenai.org/zest/submissions/public) is included with accepatbility metrics for
each of the four generalization types outlined in the paper. The metrics are novel acceptability metrics also
proposed by the authors.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
To evaluate the ability of a model to generalize to unseen tasks based only on a task description in a zero-shot
manner.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Mechanical Turk crowdsource workers.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
Mechanical Turk crowdsource workers.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
The dataset emphasizes a model's ability to generalize to unseen tasks with only a natural language description of
the task. The long-term vision of this type of evaluation is to facilitate the creation of models which can perform
arbitrary tasks with only a prompt from a non-technical user. This could broaden the frontier of what a user can
ask something like a chatbot to do for them, but it is unclear how restrictions would be put in place to prevent
users from prompting a system to perform unethical tasks.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@inproceedings{weller-etal-2020-learning,
title = "Learning from Task Descriptions",
author = "Weller, Orion and
Lourie, Nicholas and
Gardner, Matt and
Peters, Matthew",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.105",
pages = "1361--1375",
abstract = "Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this frame- work with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks. Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model{'}s ability to solve each task. Moreover, the dataset{'}s structure tests specific types of systematic generalization. We find that the state-of-the-art T5 model achieves a score of 12% on ZEST, leaving a significant challenge for NLP researchers.",
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. | 6,378 | [
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nlphuji/whoops | 2023-08-18T23:06:45.000Z | [
"annotations_creators:crowdsourced",
"language_creators:found",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"commonsense-reasoning",
"explanation-generation",
"visual-commonsense-reasoning",
"compositionality",
"image-generation",
"visual-question-answering(VQA)",
"question-answering",
"image-captioning",
"arxiv:2303.07274",
"region:us"
] | nlphuji | null | null | 11 | 185 | 2023-01-28T22:04:03 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
paperswithcode_id: whoops
pretty_name: WHOOPS!
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- commonsense-reasoning
- explanation-generation
- visual-commonsense-reasoning
- compositionality
- image-generation
- visual-question-answering(VQA)
- question-answering
- image-captioning
task_ids: []
# dataset files.
extra_gated_prompt: >-
# By clicking “Access repository“ below, you assert your intention to exclusively use this resource for research, not for commercial chatbot development, and agree to abide by the terms detailed in the [WHOOPS! license](https://whoops-benchmark.github.io/static/pdfs/whoops_license_agreement.txt). You may also view all instances through the [WHOOPS! Explorer](https://huggingface.co/spaces/nlphuji/whoops-explorer-full) and consult the accompanying [WHOOPS! Dataset card](https://huggingface.co/spaces/nlphuji/whoops-explorer-full/blob/main/README.md) prior to acceptance. If you are unsure about your specific case - do not hesitate to reach out: yonatanbitton1@gmail.com.
By clicking “Access repository” below, you confirm your understanding that for commercial models, this resource is permitted for use as a test set, but not as a training set. Please ensure adherence to the terms detailed in the [WHOOPS! license](https://whoops-benchmark.github.io/static/pdfs/whoops_license_agreement.txt). You may view all instances via the [WHOOPS! Explorer](https://huggingface.co/spaces/nlphuji/whoops-explorer-full) and refer to the [WHOOPS! Dataset card](https://huggingface.co/spaces/nlphuji/whoops-explorer-full/blob/main/README.md) prior to acceptance. If you are unsure about your specific case, don't hesitate to contact: yonatanbitton1@gmail.com.
---
# Dataset Card for WHOOPS!
- [Dataset Description](#dataset-description)
- [Contribute Images to Extend WHOOPS!](#contribute-images-to-extend-whoops)
- [Languages](#languages)
- [Dataset](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Data Loading](#data-loading)
- [Licensing Information](#licensing-information)
- [Annotations](#annotations)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Citation Information](#citation-information)
## Dataset Description
WHOOPS! is a dataset and benchmark for visual commonsense. The dataset is comprised of purposefully commonsense-defying images created by designers using publicly-available image generation tools like Midjourney. It contains commonsense-defying image from a wide range of reasons, deviations from expected social norms and everyday knowledge.
The WHOOPS! benchmark includes four tasks:
1. A novel task of explanation-of-violation: generating a detailed explanation for what makes the image weird.
2. Generating a literal caption
3. Distinguishing between detailed and underspecified captions
4. Answering questions that test compositional understanding
The results show that state-of-the-art models such as GPT3 and BLIP2 still lag behind human performance on WHOOPS!.
* Homepage: https://whoops-benchmark.github.io/
* Paper: https://arxiv.org/pdf/2303.07274.pdf
* WHOOPS! Explorer: https://huggingface.co/spaces/nlphuji/whoops-explorer-full
* Normal vs. Wired Explorer: https://huggingface.co/spaces/nlphuji/whoops-explorer-analysis
* Point of Contact: yonatanbitton1@gmail.com
[//]: # (Colab notebook code for WHOOPS evaluation )
## Contribute Images to Extend WHOOPS!
Would you like to add a commonsense-defying image to our database? Please send candidate images to yonatanbitton1@gmail.com. Thanks!
### Languages
English.
## Dataset
### Data Fields
image (image) - The weird image.
designer_explanation (string) - Detailed single-sentence explanation given by the designer, explaining why the image is weird.
selected_caption (string) - The caption that was selected from the crowed collected captions.
crowd_captions (list) - Crowd collected captions, depicting whats been seen in the image.
crowd_explanations (list) - Crowd collected single-sentence explanations, explaining why the image is weird.
crowd_underspecified_captions (list) - Crowd collected under-specified captions, depicting what is seen in the image, without depicting the commonsense-violation.
question_answering_pairs (list) - Automatically generated Q-A pairs. FlanT5 XL was used to answer the questions and filter out instances where the BEM metric is above 0.1.
commonsense_category (string) - The commonsense category the images related to (Full categories list can be found in [paper](https://arxiv.org/pdf/2303.07274.pdf)).
image_id (string)- The unique id of the image in the dataset
image_designer (string) - The name of the image designer.
### Data Splits
There is a single TEST split.
Although primarily intended as a challenging test set, we trained on the WHOOPS! dataset to demonstrate the value of the data and to create a better model.
We will provide the splits in the future.
### Data Loading
You can load the data as follows (credit to [Winoground](https://huggingface.co/datasets/facebook/winoground)):
```
from datasets import load_dataset
examples = load_dataset('nlphuji/whoops', use_auth_token=<YOUR USER ACCESS TOKEN>)
```
You can get `<YOUR USER ACCESS TOKEN>` by following these steps:
1) log into your Hugging Face account
2) click on your profile picture
3) click "Settings"
4) click "Access Tokens"
5) generate an access token
## Licensing Information
[CC-By 4.0](https://creativecommons.org/licenses/by/4.0/)
Additional license information: [license_agreement.txt](https://huggingface.co/datasets/nlphuji/whoops/blob/main/license_agreement.txt)
You may also view all instances through the [WHOOPS! Explorer](https://huggingface.co/spaces/nlphuji/whoops-explorer-full) and consult the accompanying [WHOOPS! Dataset card](https://huggingface.co/spaces/nlphuji/whoops-explorer-full/blob/main/README.md).
1. **Purpose:** The dataset was primarily designed for use as a test set.
2. **Commercial Use:** Commercially, the dataset may be used as a test set, but it's prohibited to use it as a training set.
3. **Rights on Images:** All rights to the images within the dataset are retained by the WHOOPS! authors.
If you are unsure about your specific case - do not hesitate to reach out: yonatanbitton1@gmail.com.
[//]: # (To evaluate WHOOPS! with a fine-tune BLIP2, we split the images in WHOOPS! into 5 cross- validation splits. For these 5 splits independently, we train supervised models using 60% of the data as training, 20% as validation, and 20% for test.)
## Annotations
We paid designers to create images, and supply explanations for what is making the image wierd.
We paid Amazon Mechanical Turk Workers to supply explanations, captions and under-specified captions for each image in our dataset.
## Considerations for Using the Data
We took measures to filter out potentially harmful or offensive images and texts in WHOOPS!, but it is still possible that some individuals may find certain content objectionable.
If you come across any instances of harm, please report them to our point of contact. We will review and eliminate any images from the dataset that are deemed harmful.
[//]: # (All images, explanations, captions and under-specified captions were obtained with human annotators.)
### Citation Information
@article{bitton2023breaking,
title={Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images},
author={Bitton-Guetta, Nitzan and Bitton, Yonatan and Hessel, Jack and Schmidt, Ludwig and Elovici, Yuval and Stanovsky, Gabriel and Schwartz, Roy},
journal={arXiv preprint arXiv:2303.07274},
year={2023}
} | 7,812 | [
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] |
C-MTEB/CMNLI | 2023-07-27T17:35:51.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 185 | 2023-07-27T17:35:44 | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
dataset_info:
features:
- name: sent1
sequence: string
- name: sent2
sequence: string
- name: labels
sequence: int64
splits:
- name: validation
num_bytes: 1349125
num_examples: 1
download_size: 663026
dataset_size: 1349125
---
# Dataset Card for "CMNLI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 522 | [
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] |
M-A-D/Mixed-Arabic-Datasets-Repo | 2023-10-16T21:25:35.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:translation",
"task_categories:summarization",
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:fill-mask",
"size_categories:1B<n<10B",
"language:ar",
"region:us"
] | M-A-D | null | null | 12 | 185 | 2023-08-27T01:19:21 | ---
language:
- ar
size_categories:
- 1B<n<10B
task_categories:
- text-classification
- question-answering
- translation
- summarization
- conversational
- text-generation
- text2text-generation
- fill-mask
pretty_name: Mixed Arabic Datasets (MAD) Corpus
dataset_info:
- config_name: Ara--Ali-C137--Hindawi-Books-dataset
features:
- name: BookLink
dtype: string
- name: BookName
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- config_name: Ara--Ali-C137--Hindawi-Books-dataset
data_files:
- split: train
path: Ara--Ali-C137--Hindawi-Books-dataset/train-*
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- split: train
path: Ara--Goud--Goud-sum/train-*
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path: Ara--J-Mourad--MNAD.v1/train-*
- config_name: Ara--JihadZa--IADD
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- split: train
path: Ara--JihadZa--IADD/train-*
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data_files:
- split: train
path: Ara--LeMGarouani--MAC-corpus/train-*
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data_files:
- split: train
path: Ara--MBZUAI--Bactrian-X/train-*
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path: Ara--OpenAssistant--oasst1/train-*
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path: Ara--Wikipedia/train-*
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data_files:
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path: Ara--bigscience--xP3/train-*
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data_files:
- split: train
path: Ara--cardiffnlp--tweet_sentiment_multilingual/train-*
- split: validation
path: Ara--cardiffnlp--tweet_sentiment_multilingual/validation-*
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path: Ara--cardiffnlp--tweet_sentiment_multilingual/test-*
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data_files:
- split: train
path: Ara--miracl--miracl/train-*
- config_name: Ara--mustapha--QuranExe
data_files:
- split: train
path: Ara--mustapha--QuranExe/train-*
- config_name: Ara--pain--Arabic-Tweets
data_files:
- split: train
path: Ara--pain--Arabic-Tweets/train-*
- config_name: Ara--saudinewsnet
data_files:
- split: train
path: Ara--saudinewsnet/train-*
- config_name: Ary--AbderrahmanSkiredj1--Darija-Wikipedia
data_files:
- split: train
path: Ary--AbderrahmanSkiredj1--Darija-Wikipedia/train-*
- config_name: Ary--Ali-C137--Darija-Stories-Dataset
data_files:
- split: train
path: Ary--Ali-C137--Darija-Stories-Dataset/train-*
- config_name: Ary--Wikipedia
data_files:
- split: train
path: Ary--Wikipedia/train-*
- config_name: Arz--Wikipedia
data_files:
- split: train
path: Arz--Wikipedia/train-*
---
# Dataset Card for "Mixed Arabic Datasets (MAD) Corpus"
**The Mixed Arabic Datasets Corpus : A Community-Driven Collection of Diverse Arabic Texts**
## Dataset Description
The Mixed Arabic Datasets (MAD) presents a dynamic compilation of diverse Arabic texts sourced from various online platforms and datasets. It addresses a critical challenge faced by researchers, linguists, and language enthusiasts: the fragmentation of Arabic language datasets across the Internet. With MAD, we are trying to centralize these dispersed resources into a single, comprehensive repository.
Encompassing a wide spectrum of content, ranging from social media conversations to literary masterpieces, MAD captures the rich tapestry of Arabic communication, including both standard Arabic and regional dialects.
This corpus offers comprehensive insights into the linguistic diversity and cultural nuances of Arabic expression.
## Usage
If you want to use this dataset you pick one among the available configs:
`Ara--MBZUAI--Bactrian-X` | `Ara--OpenAssistant--oasst1` | `Ary--AbderrahmanSkiredj1--Darija-Wikipedia`
`Ara--Wikipedia` | `Ary--Wikipedia` | `Arz--Wikipedia`
`Ary--Ali-C137--Darija-Stories-Dataset` | `Ara--Ali-C137--Hindawi-Books-dataset` | ``
Example of usage:
```python
dataset = load_dataset('M-A-D/Mixed-Arabic-Datasets-Repo', 'Ara--MBZUAI--Bactrian-X')
```
If you loaded multiple datasets and wanted to merge them together then you can simply laverage `concatenate_datasets()` from `datasets`
```pyhton
dataset3 = concatenate_datasets([dataset1['train'], dataset2['train']])
```
Note : proccess the datasets before merging in order to make sure you have a new dataset that is consistent
## Dataset Size
The Mixed Arabic Datasets (MAD) is a dynamic and evolving collection, with its size fluctuating as new datasets are added or removed. As MAD continuously expands, it becomes a living resource that adapts to the ever-changing landscape of Arabic language datasets.
**Dataset List**
MAD draws from a diverse array of sources, each contributing to its richness and breadth. While the collection is constantly evolving, some of the datasets that are poised to join MAD in the near future include:
- [✔] OpenAssistant/oasst1 (ar portion) : [Dataset Link](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [✔] MBZUAI/Bactrian-X (ar portion) : [Dataset Link](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ar/train)
- [✔] AbderrahmanSkiredj1/Darija-Wikipedia : [Dataset Link](https://huggingface.co/datasets/AbderrahmanSkiredj1/moroccan_darija_wikipedia_dataset)
- [✔] Arabic Wikipedia : [Dataset Link](https://huggingface.co/datasets/wikipedia)
- [✔] Moroccan Arabic Wikipedia : [Dataset Link](https://huggingface.co/datasets/wikipedia)
- [✔] Egyptian Arabic Wikipedia : [Dataset Link](https://huggingface.co/datasets/wikipedia)
- [✔] Darija Stories Dataset : [Dataset Link](https://huggingface.co/datasets/Ali-C137/Darija-Stories-Dataset)
- [✔] Hindawi Books Dataset : [Dataset Link](https://huggingface.co/datasets/Ali-C137/Hindawi-Books-dataset)
- [] uonlp/CulturaX - ar : [Dataset Link](https://huggingface.co/datasets/uonlp/CulturaX/viewer/ar/train)
- [✔] Pain/ArabicTweets : [Dataset Link](https://huggingface.co/datasets/pain/Arabic-Tweets)
- [] Abu-El-Khair Corpus : [Dataset Link](https://huggingface.co/datasets/arabic_billion_words)
- [✔] QuranExe : [Dataset Link](https://huggingface.co/datasets/mustapha/QuranExe)
- [✔] MNAD : [Dataset Link](https://huggingface.co/datasets/J-Mourad/MNAD.v1)
- [✔] IADD : [Dataset Link](https://raw.githubusercontent.com/JihadZa/IADD/main/IADD.json)
- [] OSIAN : [Dataset Link](https://wortschatz.uni-leipzig.de/en/download/Arabic#ara-tn_newscrawl-OSIAN_2018)
- [✔] MAC corpus : [Dataset Link](https://raw.githubusercontent.com/LeMGarouani/MAC/main/MAC%20corpus.csv)
- [✔] Goud.ma-Sum : [Dataset Link](https://huggingface.co/datasets/Goud/Goud-sum)
- [✔] SaudiNewsNet : [Dataset Link](https://huggingface.co/datasets/saudinewsnet)
- [✔] Miracl : [Dataset Link](https://huggingface.co/datasets/miracl/miracl)
- [✔] CardiffNLP/TweetSentimentMulti : [Dataset Link](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)
- [] OSCAR-2301 : [Dataset Link](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301/viewer/ar/train)
- [] mc4 : [Dataset Link](https://huggingface.co/datasets/mc4/viewer/ar/train)
- [✔] bigscience/xP3 : [Dataset Link](https://huggingface.co/datasets/bigscience/xP3/viewer/ar/train)
- [] Muennighoff/xP3x : [Dataset Link](https://huggingface.co/datasets/Muennighoff/xP3x)
- [] Ai_Society : [Dataset Link](https://huggingface.co/datasets/camel-ai/ai_society_translated)
## Potential Use Cases
The Mixed Arabic Datasets (MAD) holds the potential to catalyze a multitude of groundbreaking applications:
- **Linguistic Analysis:** Employ MAD to conduct in-depth linguistic studies, exploring dialectal variances, language evolution, and grammatical structures.
- **Topic Modeling:** Dive into diverse themes and subjects through the extensive collection, revealing insights into emerging trends and prevalent topics.
- **Sentiment Understanding:** Decode sentiments spanning Arabic dialects, revealing cultural nuances and emotional dynamics.
- **Sociocultural Research:** Embark on a sociolinguistic journey, unraveling the intricate connection between language, culture, and societal shifts.
## Dataset Access
MAD's access mechanism is unique: while it doesn't carry a general license itself, each constituent dataset within the corpus retains its individual license. By accessing the dataset details through the provided links in the "Dataset List" section above, users can understand the specific licensing terms for each dataset.
### Join Us on Discord
For discussions, contributions, and community interactions, join us on Discord! [](https://discord.gg/2NpJ9JGm)
### How to Contribute
Want to contribute to the Mixed Arabic Datasets project? Follow our comprehensive guide on Google Colab for step-by-step instructions: [Contribution Guide](https://colab.research.google.com/drive/1kOIRoicgCOV8TPvASAI_2uMY7rpXnqzJ?usp=sharing).
**Note**: If you'd like to test a contribution before submitting it, feel free to do so on the [MAD Test Dataset](https://huggingface.co/datasets/M-A-D/Mixed-Arabic-Dataset-test).
## Citation
```
@dataset{
title = {Mixed Arabic Datasets (MAD)},
author = {MAD Community},
howpublished = {Dataset},
url = {https://huggingface.co/datasets/M-A-D/Mixed-Arabic-Datasets-Repo},
year = {2023},
}
``` | 15,953 | [
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eduagarcia/OSCAR-2301-pt_dedup | 2023-08-28T16:55:02.000Z | [
"region:us"
] | eduagarcia | null | null | 0 | 185 | 2023-08-27T23:52:48 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 61846407893
num_examples: 10888966
download_size: 28809168123
dataset_size: 61846407893
---
# Dataset Card for "OSCAR-2301_dedup"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 404 | [
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TearGosling/limarp_standardized | 2023-09-05T01:01:28.000Z | [
"region:us"
] | TearGosling | null | null | 1 | 185 | 2023-09-05T00:59:45 | Entry not found | 15 | [
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definite_pronoun_resolution | 2023-04-05T10:04:44.000Z | [
"task_categories:token-classification",
"task_ids:word-sense-disambiguation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Composed by 30 students from one of the author's undergraduate classes. These
sentence pairs cover topics ranging from real events (e.g., Iran's plan to
attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g.,
Batman) and purely imaginary situations, largely reflecting the pop culture as
perceived by the American kids born in the early 90s. Each annotated example
spans four lines: the first line contains the sentence, the second line contains
the target pronoun, the third line contains the two candidate antecedents, and
the fourth line contains the correct antecedent. If the target pronoun appears
more than once in the sentence, its first occurrence is the one to be resolved. | @inproceedings{rahman2012resolving,
title={Resolving complex cases of definite pronouns: the winograd schema challenge},
author={Rahman, Altaf and Ng, Vincent},
booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
pages={777--789},
year={2012},
organization={Association for Computational Linguistics}
} | 3 | 184 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- word-sense-disambiguation
paperswithcode_id: definite-pronoun-resolution-dataset
pretty_name: Definite Pronoun Resolution Dataset
dataset_info:
features:
- name: sentence
dtype: string
- name: pronoun
dtype: string
- name: candidates
sequence: string
length: 2
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
config_name: plain_text
splits:
- name: test
num_bytes: 71691
num_examples: 564
- name: train
num_bytes: 171511
num_examples: 1322
download_size: 227452
dataset_size: 243202
---
# Dataset Card for "definite_pronoun_resolution"
## 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.hlt.utdallas.edu/~vince/data/emnlp12/](https://www.hlt.utdallas.edu/~vince/data/emnlp12/)
- **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:** 0.23 MB
- **Size of the generated dataset:** 0.24 MB
- **Total amount of disk used:** 0.47 MB
### Dataset Summary
Composed by 30 students from one of the author's undergraduate classes. These
sentence pairs cover topics ranging from real events (e.g., Iran's plan to
attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g.,
Batman) and purely imaginary situations, largely reflecting the pop culture as
perceived by the American kids born in the early 90s. Each annotated example
spans four lines: the first line contains the sentence, the second line contains
the target pronoun, the third line contains the two candidate antecedents, and
the fourth line contains the correct antecedent. If the target pronoun appears
more than once in the sentence, its first occurrence is the one to be resolved.
### 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:** 0.23 MB
- **Size of the generated dataset:** 0.24 MB
- **Total amount of disk used:** 0.47 MB
An example of 'train' looks as follows.
```
{
"candidates": ["coreference resolution", "chunking"],
"label": 0,
"pronoun": "it",
"sentence": "There is currently more work on coreference resolution than on chunking because it is a problem that is still far from being solved."
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `sentence`: a `string` feature.
- `pronoun`: a `string` feature.
- `candidates`: a `list` of `string` features.
- `label`: a classification label, with possible values including `0` (0), `1` (1).
### Data Splits
| name |train|test|
|----------|----:|---:|
|plain_text| 1322| 564|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{rahman2012resolving,
title={Resolving complex cases of definite pronouns: the winograd schema challenge},
author={Rahman, Altaf and Ng, Vincent},
booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
pages={777--789},
year={2012},
organization={Association for Computational Linguistics}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | 7,185 | [
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sanchit-gandhi/whisper-jax-test-files | 2023-04-19T12:07:08.000Z | [
"region:us"
] | sanchit-gandhi | null | null | 2 | 184 | 2023-04-19T11:49:16 | ---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 271658381.0
num_examples: 2
download_size: 113444578
dataset_size: 271658381.0
---
# Dataset Card for "whisper-jax-test-files"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 371 | [
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flaviagiammarino/path-vqa | 2023-06-03T19:02:04.000Z | [
"task_categories:visual-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"medical",
"arxiv:2003.10286",
"region:us"
] | flaviagiammarino | null | null | 5 | 184 | 2023-06-02T12:03:51 | ---
license: mit
task_categories:
- visual-question-answering
language:
- en
tags:
- medical
pretty_name: PathVQA
paperswithcode_id: pathvqa
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 3171303616.326
num_examples: 19654
- name: test
num_bytes: 1113474813.05
num_examples: 6719
- name: validation
num_bytes: 1191658832.096
num_examples: 6259
download_size: 785414952
dataset_size: 5476437261.472
---
# Dataset Card for PathVQA
## Dataset Description
PathVQA is a dataset of question-answer pairs on pathology images. The dataset is intended to be used for training and testing
Medical Visual Question Answering (VQA) systems. The dataset includes both open-ended questions and binary "yes/no" questions.
The dataset is built from two publicly-available pathology textbooks: "Textbook of Pathology" and "Basic Pathology", and a
publicly-available digital library: "Pathology Education Informational Resource" (PEIR). The copyrights of images and captions
belong to the publishers and authors of these two books, and the owners of the PEIR digital library.<br>
**Repository:** [PathVQA Official GitHub Repository](https://github.com/UCSD-AI4H/PathVQA)<br>
**Paper:** [PathVQA: 30000+ Questions for Medical Visual Question Answering](https://arxiv.org/abs/2003.10286)<br>
**Leaderboard:** [Papers with Code Leaderboard](https://paperswithcode.com/sota/medical-visual-question-answering-on-pathvqa)
### Dataset Summary
The dataset was obtained from the updated Google Drive link shared by the authors on Feb 15, 2023,
see the [commit](https://github.com/UCSD-AI4H/PathVQA/commit/117e7f4ef88a0e65b0e7f37b98a73d6237a3ceab)
in the GitHub repository. This version of the dataset contains a total of 5,004 images and 32,795 question-answer pairs.
Out of the 5,004 images, 4,289 images are referenced by a question-answer pair, while 715 images are not used.
There are a few image-question-answer triplets which occur more than once in the same split (training, validation, test).
After dropping the duplicate image-question-answer triplets, the dataset contains 32,632 question-answer pairs on 4,289 images.
#### Supported Tasks and Leaderboards
The PathVQA dataset has an active leaderboard on [Papers with Code](https://paperswithcode.com/sota/medical-visual-question-answering-on-pathvqa)
where models are ranked based on three metrics: "Yes/No Accuracy", "Free-form accuracy" and "Overall accuracy". "Yes/No Accuracy" is
the accuracy of a model's generated answers for the subset of binary "yes/no" questions. "Free-form accuracy" is the accuracy
of a model's generated answers for the subset of open-ended questions. "Overall accuracy" is the accuracy of a model's generated
answers across all questions.
#### Languages
The question-answer pairs are in English.
## Dataset Structure
### Data Instances
Each instance consists of an image-question-answer triplet.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=CMYK size=309x272>,
'question': 'where are liver stem cells (oval cells) located?',
'answer': 'in the canals of hering'
}
```
### Data Fields
- `'image'`: the image referenced by the question-answer pair.
- `'question'`: the question about the image.
- `'answer'`: the expected answer.
### Data Splits
The dataset is split into training, validation and test. The split is provided directly by the authors.
| | Training Set | Validation Set | Test Set |
|-------------------------|:------------:|:--------------:|:--------:|
| QAs |19,654 |6,259 |6,719 |
| Images |2,599 |832 |858 |
## Additional Information
### Licensing Information
The authors have released the dataset under the [MIT License](https://github.com/UCSD-AI4H/PathVQA/blob/master/LICENSE).
### Citation Information
```
@article{he2020pathvqa,
title={PathVQA: 30000+ Questions for Medical Visual Question Answering},
author={He, Xuehai and Zhang, Yichen and Mou, Luntian and Xing, Eric and Xie, Pengtao},
journal={arXiv preprint arXiv:2003.10286},
year={2020}
}
``` | 4,290 | [
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liyucheng/arxiv-march-2023 | 2023-06-02T17:59:35.000Z | [
"region:us"
] | liyucheng | null | null | 0 | 184 | 2023-06-02T17:59:27 | ---
dataset_info:
features:
- name: entry_id
dtype: string
- name: published
dtype: string
- name: title
dtype: string
- name: authors
sequence: string
- name: primary_category
dtype: string
- name: categories
sequence: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 20816482
num_examples: 500
download_size: 10224538
dataset_size: 20816482
---
# Dataset Card for "arxiv-march-2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 595 | [
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] |
explodinggradients/WikiEval | 2023-09-18T15:12:16.000Z | [
"region:us"
] | explodinggradients | null | null | 0 | 184 | 2023-08-24T10:01:45 | ---
dataset_info:
features:
- name: answer
dtype: string
- name: question
dtype: string
- name: context_v1
sequence: string
- name: context_v2
sequence: string
- name: ungrounded_answer
dtype: string
- name: source
dtype: string
- name: poor_answer
dtype: string
splits:
- name: train
num_bytes: 548755
num_examples: 50
download_size: 354738
dataset_size: 548755
---
# WikiEval
Dataset for to do correlation analysis of difference metrics proposed in [Ragas](https://github.com/explodinggradients/ragas)
This dataset was generated from 50 pages from Wikipedia with edits post 2022.
## Column description
* question: a question that can be answered from the given Wikipedia page (source).
* source: The source Wikipedia page from which the question and context are generated.
* grounded_answer: answer grounded on context_v1
* ungrounded_answer: answer generated without context_v1
* poor_answer: answer with poor relevancy compared to grounded_answer and ungrounded_answer
* context_v1: Ideal context to answer the given question
* contetx_v2: context that contains redundant information compared to context_v1 | 1,173 | [
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giganion/pippa_roleplay_standardized | 2023-09-04T20:07:55.000Z | [
"region:us"
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] |
jjonhwa/SECOND_KQ_V2 | 2023-09-13T07:04:47.000Z | [
"region:us"
] | jjonhwa | null | null | 0 | 184 | 2023-09-13T01:44:49 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: score
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 686780736
num_examples: 86975
download_size: 276955064
dataset_size: 686780736
---
# Dataset Card for "SECOND_KQ_V2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 505 | [
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medal | 2023-06-13T12:39:11.000Z | [
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:unknown",
"disambiguation",
"region:us"
] | null | A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate | @inproceedings{wen-etal-2020-medal,
title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
author = "Wen, Zhi and
Lu, Xing Han and
Reddy, Siva",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
pages = "130--135",
abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.",
} | 10 | 183 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: medal
pretty_name: MeDAL
tags:
- disambiguation
dataset_info:
features:
- name: abstract_id
dtype: int32
- name: text
dtype: string
- name: location
sequence: int32
- name: label
sequence: string
splits:
- name: train
num_bytes: 3573399948
num_examples: 3000000
- name: test
num_bytes: 1190766821
num_examples: 1000000
- name: validation
num_bytes: 1191410723
num_examples: 1000000
- name: full
num_bytes: 15536883723
num_examples: 14393619
download_size: 21060929078
dataset_size: 21492461215
---
# Dataset Card for the MeDAL 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:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Repository:** https://github.com/BruceWen120/medal
- **Paper:** https://www.aclweb.org/anthology/2020.clinicalnlp-1.15/
- **Dataset (Kaggle):** https://www.kaggle.com/xhlulu/medal-emnlp
- **Dataset (Zenodo):** https://zenodo.org/record/4265632
- **Pretrained model:** https://huggingface.co/xhlu/electra-medal
- **Leaderboard:** [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
A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate
### Supported Tasks and Leaderboards
Medical abbreviation disambiguation
### Languages
English (en)
## Dataset Structure
Each file is a table consisting of three columns:
* text: The normalized content of an abstract
* location: The location (index) of each abbreviation that was substituted
* label: The word at that was substituted at the given location
### Data Instances
An example from the train split is:
```
{'abstract_id': 14145090,
'text': 'velvet antlers vas are commonly used in traditional chinese medicine and invigorant and contain many PET components for health promotion the velvet antler peptide svap is one of active components in vas based on structural study the svap interacts with tgfβ receptors and disrupts the tgfβ pathway we hypothesized that svap prevents cardiac fibrosis from pressure overload by blocking tgfβ signaling SDRs underwent TAC tac or a sham operation T3 one month rats received either svap mgkgday or vehicle for an additional one month tac surgery induced significant cardiac dysfunction FB activation and fibrosis these effects were improved by treatment with svap in the heart tissue tac remarkably increased the expression of tgfβ and connective tissue growth factor ctgf ROS species C2 and the phosphorylation C2 of smad and ERK kinases erk svap inhibited the increases in reactive oxygen species C2 ctgf expression and the phosphorylation of smad and erk but not tgfβ expression in cultured cardiac fibroblasts angiotensin ii ang ii had similar effects compared to tac surgery such as increases in αsmapositive CFs and collagen synthesis svap eliminated these effects by disrupting tgfβ IB to its receptors and blocking ang iitgfβ downstream signaling these results demonstrated that svap has antifibrotic effects by blocking the tgfβ pathway in CFs',
'location': [63],
'label': ['transverse aortic constriction']}
```
### Data Fields
The column types are:
* text: content of the abstract as a string
* location: index of the substitution as an integer
* label: substitued word as a string
### Data Splits
The following files are present:
* `full_data.csv`: The full dataset with all 14M abstracts.
* `train.csv`: The subset used to train the baseline and proposed models.
* `valid.csv`: The subset used to validate the model during training for hyperparameter selection.
* `test.csv`: The subset used to evaluate the model and report the results in the tables.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The original dataset was retrieved and modified from the [NLM website](https://www.nlm.nih.gov/databases/download/pubmed_medline.html).
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
Details on how the abbreviations were created can be found in section 2.2 (Dataset Creation) of the [ACL ClinicalNLP paper](https://aclanthology.org/2020.clinicalnlp-1.15.pdf).
#### 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
Since the abstracts are written in English, the data is biased towards anglo-centric medical research. If you plan to use a model pre-trained on this dataset for a predominantly non-English community, it is important to verify whether there are negative biases present in your model, and ensure that they are correctly mitigated. For instance, you could fine-tune your dataset on a multilingual medical disambiguation dataset, or collect a dataset specific to your use case.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The ELECTRA model is licensed under [Apache 2.0](https://github.com/google-research/electra/blob/master/LICENSE). The license for the libraries used in this project (`transformers`, `pytorch`, etc.) can be found in their respective GitHub repository. Our model is released under a MIT license.
The original dataset was retrieved and modified from the [NLM website](https://www.nlm.nih.gov/databases/download/pubmed_medline.html). By using this dataset, you are bound by the [terms and conditions](https://www.nlm.nih.gov/databases/download/terms_and_conditions_pubmed.html) specified by NLM:
> INTRODUCTION
>
> Downloading data from the National Library of Medicine FTP servers indicates your acceptance of the following Terms and Conditions: No charges, usage fees or royalties are paid to NLM for this data.
>
> MEDLINE/PUBMED SPECIFIC TERMS
>
> NLM freely provides PubMed/MEDLINE data. Please note some PubMed/MEDLINE abstracts may be protected by copyright.
>
> GENERAL TERMS AND CONDITIONS
>
> * Users of the data agree to:
> * acknowledge NLM as the source of the data by including the phrase "Courtesy of the U.S. National Library of Medicine" in a clear and conspicuous manner,
> * properly use registration and/or trademark symbols when referring to NLM products, and
> * not indicate or imply that NLM has endorsed its products/services/applications.
>
> * Users who republish or redistribute the data (services, products or raw data) agree to:
> * maintain the most current version of all distributed data, or
> * make known in a clear and conspicuous manner that the products/services/applications do not reflect the most current/accurate data available from NLM.
>
> * These data are produced with a reasonable standard of care, but NLM makes no warranties express or implied, including no warranty of merchantability or fitness for particular purpose, regarding the accuracy or completeness of the data. Users agree to hold NLM and the U.S. Government harmless from any liability resulting from errors in the data. NLM disclaims any liability for any consequences due to use, misuse, or interpretation of information contained or not contained in the data.
>
> * NLM does not provide legal advice regarding copyright, fair use, or other aspects of intellectual property rights. See the NLM Copyright page.
>
> * NLM reserves the right to change the type and format of its machine-readable data. NLM will take reasonable steps to inform users of any changes to the format of the data before the data are distributed via the announcement section or subscription to email and RSS updates.
### Citation Information
```
@inproceedings{wen-etal-2020-medal,
title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
author = "Wen, Zhi and
Lu, Xing Han and
Reddy, Siva",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
pages = "130--135",
abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.",
}
```
### Contributions
Thanks to [@Narsil](https://github.com/Narsil) and [@xhlulu](https://github.com/xhlulu)) for adding this dataset. | 10,886 | [
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tweets_ar_en_parallel | 2023-01-25T14:54:55.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"language:en",
"license:apache-2.0",
"tweets-translation",
"region:us"
] | null | Twitter users often post parallel tweets—tweets that contain the same content but are
written in different languages. Parallel tweets can be an important resource for developing
machine translation (MT) systems among other natural language processing (NLP) tasks. This
resource is a result of a generic method for collecting parallel tweets. Using the method,
we compiled a bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts
who post English-Arabic tweets regularly. Additionally, we annotate a subset of Twitter accounts
with their countries of origin and topic of interest, which provides insights about the population
who post parallel tweets. | @inproceedings{Mubarak2020bilingualtweets,
title={Constructing a Bilingual Corpus of Parallel Tweets},
author={Mubarak, Hamdy and Hassan, Sabit and Abdelali, Ahmed},
booktitle={Proceedings of 13th Workshop on Building and Using Comparable Corpora (BUCC)},
address={Marseille, France},
year={2020}
} | 3 | 183 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- no-annotation
language_creators:
- found
language:
- ar
- en
license:
- apache-2.0
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: bilingual-corpus-of-arabic-english-parallel
pretty_name: Bilingual Corpus of Arabic-English Parallel Tweets
tags:
- tweets-translation
dataset_info:
- config_name: parallelTweets
features:
- name: ArabicTweetID
dtype: int64
- name: EnglishTweetID
dtype: int64
splits:
- name: test
num_bytes: 2667296
num_examples: 166706
download_size: 2937626
dataset_size: 2667296
- config_name: accountList
features:
- name: account
dtype: string
splits:
- name: test
num_bytes: 20108
num_examples: 1389
download_size: 2937626
dataset_size: 20108
- config_name: countryTopicAnnotation
features:
- name: account
dtype: string
- name: country
dtype:
class_label:
names:
'0': QA
'1': BH
'2': AE
'3': OM
'4': SA
'5': PL
'6': JO
'7': IQ
'8': Other
'9': EG
'10': KW
'11': SY
- name: topic
dtype:
class_label:
names:
'0': Gov
'1': Culture
'2': Education
'3': Sports
'4': Travel
'5': Events
'6': Business
'7': Science
'8': Politics
'9': Health
'10': Governoment
'11': Media
splits:
- name: test
num_bytes: 6036
num_examples: 200
download_size: 2937626
dataset_size: 6036
---
# Dataset Card for Bilingual Corpus of Arabic-English Parallel Tweets
## 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:** [Bilingual Corpus of Arabic-English Parallel Tweets](https://alt.qcri.org/resources/bilingual_corpus_of_parallel_tweets)
- **Repository:**
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.bucc-1.3/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Twitter users often post parallel tweets—tweets that contain the same content but are written in different languages. Parallel tweets can be an important resource for developing machine translation (MT) systems among other natural language processing (NLP) tasks. This resource is a result of a generic method for collecting parallel tweets. Using the method, we compiled a bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts who post English-Arabic tweets regularly. Additionally, we annotate a subset of Twitter accounts with their countries of origin and topic of interest, which provides insights about the population who post parallel tweets.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
parallelTweets:
```
{
"ArabicTweetID": 981111245209243600,
"EnglishTweetID": 981111450432401400
}
```
accountList:
```
{
'account': 'HukoomiQatar'
}
```
countryTopicAnnotation:
```
{
'account': 'HukoomiQatar',
'country': 'QA',
'topic': 'Gov'
}
```
### Data Fields
parallelTweets:
- `ArabicTweetID` (int)
- `EnglishTweetID` (int)
accountList:
- `account` (str)
countryTopicAnnotation:
- `account` (str)
- `country` (class label): One of:
- "QA",
- "BH",
- "AE",
- "OM",
- "SA",
- "PL",
- "JO",
- "IQ",
- "Other",
- "EG",
- "KW",
- "SY"
- `topic` (class label): One of:
- "Gov",
- "Culture",
- "Education",
- "Sports",
- "Travel",
- "Events",
- "Business",
- "Science",
- "Politics",
- "Health",
- "Governoment",
- "Media",
### Data Splits
All configuration have only one split: "test".
## 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
It is licensed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@inproceedings{Mubarak2020bilingualtweets,
title={Constructing a Bilingual Corpus of Parallel Tweets},
author={Mubarak, Hamdy and Hassan, Sabit and Abdelali, Ahmed},
booktitle={Proceedings of 13th Workshop on Building and Using Comparable Corpora (BUCC)},
address={Marseille, France},
year={2020}
}
```
[More Information Needed]
### Contributions
Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset. | 6,119 | [
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Dahoas/static-hh | 2023-03-06T00:11:55.000Z | [
"region:us"
] | Dahoas | null | null | 14 | 183 | 2023-02-15T03:53:36 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 143664651
num_examples: 96256
- name: test
num_bytes: 7649255
num_examples: 5103
download_size: 90825631
dataset_size: 151313906
---
Static split of Anthropic's Helpful Harmless dataset. Contains base-online and rejection sampled outputs. | 471 | [
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] |
GSQA/speech-alpaca-gpt4-unit | 2023-08-09T15:29:24.000Z | [
"region:us"
] | GSQA | null | null | 1 | 183 | 2023-08-08T18:13:35 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: speech_input
dtype: string
- name: input_speaker
dtype: string
- name: output_speaker
dtype: string
- name: mhubert_layer11_code1000_input_code
dtype: string
- name: mhubert_layer11_code1000_output_audio
dtype: string
- name: hubert_layer6_code100_input_code
dtype: string
- name: hubert_layer6_code100_output_audio
dtype: string
splits:
- name: train
num_bytes: 1718767489
num_examples: 51349
download_size: 654738368
dataset_size: 1718767489
---
# Dataset Card for "speech-alpaca-gpt4-unit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 830 | [
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result-kand2-sdxl-wuerst-karlo/2ddeba07 | 2023-10-09T21:37:39.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 183 | 2023-10-09T21:37:38 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 200
num_examples: 10
download_size: 1374
dataset_size: 200
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "2ddeba07"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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covid_qa_castorini | 2022-11-03T16:30:54.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2004.11339",
"region:us"
] | null | CovidQA is the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge. | @article{tang2020rapidly,
title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},
author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},
journal={arXiv preprint arXiv:2004.11339},
year={2020}
} | 0 | 182 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
paperswithcode_id: covidqa
pretty_name: CovidQaCastorini
dataset_info:
- config_name: covid_qa_deepset
features:
- name: document_id
dtype: int32
- name: context
dtype: string
- name: question
dtype: string
- name: is_impossible
dtype: bool
- name: id
dtype: int32
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 65151262
num_examples: 2019
download_size: 4418117
dataset_size: 65151262
- config_name: covidqa
features:
- name: category_name
dtype: string
- name: question_query
dtype: string
- name: keyword_query
dtype: string
- name: answers
sequence:
- name: id
dtype: string
- name: title
dtype: string
- name: exact_answer
dtype: string
splits:
- name: train
num_bytes: 33757
num_examples: 27
download_size: 51438
dataset_size: 33757
- config_name: covid_qa_castorini
features:
- name: category_name
dtype: string
- name: question_query
dtype: string
- name: keyword_query
dtype: string
- name: answers
sequence:
- name: id
dtype: string
- name: title
dtype: string
- name: exact_answer
dtype: string
splits:
- name: train
num_bytes: 33757
num_examples: 27
download_size: 51438
dataset_size: 33757
---
# Dataset Card for [covid_qa_castorini]
## 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://covidqa.ai
- **Repository:** https://github.com/castorini/pygaggle
- **Paper:** https://arxiv.org/abs/2004.11339
- **Point of Contact:** [Castorini research group @UWaterloo](https://github.com/castorini/)
### Dataset Summary
CovidQA is a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered
from Kaggle’s COVID-19 Open Research Dataset Challenge.
The dataset comprises 156 question-article pairs with 27 questions (topics) and 85 unique articles.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
**What do the instances that comprise the dataset represent?**
Each represents a question, a context (document passage from the CORD19 dataset) and an answer.
**How many instances are there in total?**
**What data does each instance consist of?**
Each instance is a query (natural language question and keyword-based), a set of answers, and a document id with its title associated with each answer.
[More Information Needed]
### Data Fields
The data was annotated in SQuAD style fashion, where each row contains:
* **question_query**: Natural language question query
* **keyword_query**: Keyword-based query
* **category_name**: Category in which the queries are part of
* **answers**: List of answers
* **id**: The document ID the answer is found on
* **title**: Title of the document of the answer
* **exact_answer**: Text (string) of the exact answer
### Data Splits
**data/kaggle-lit-review-0.2.json**: 156 question-article pairs with 27 questions (topics) and 85 unique articles from
CORD-19.
[More Information Needed]
## Dataset Creation
The dataset aims to help for guiding research until more substantial evaluation resources become available. Being a smaller dataset,
it can be helpful for evaluating the zero-shot or transfer capabilities of existing models on topics specifically related to COVID-19.
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
[More Information Needed]
### Annotations
Five of the co-authors participated in this annotation effort, applying the aforementioned approach, with one lead
annotator responsible for approving topics and answering technical questions from the other annotators. Two annotators are
undergraduate students majoring in computer science, one is a science alumna, another is a computer science professor,
and the lead annotator is a graduate student in computer science—all affiliated with the University of Waterloo.
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
The dataset was intended as a stopgap measure for guiding research until more substantial evaluation resources become available.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
While this dataset, comprising 124 question–article pairs as of the present version 0.1 release, does not have sufficient
examples for supervised machine learning, it can be helpful for evaluating the zero-shot or transfer capabilities
of existing models on topics specifically related to COVID-19.
## Additional Information
The listed authors in the homepage are maintaining/supporting the dataset.
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is licensed under the [Apache License 2.0](https://github.com/castorini/pygaggle/blob/master/LICENSE).
### Citation Information
```
@article{tang2020rapidly,
title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19},
author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy},
journal={arXiv preprint arXiv:2004.11339},
year={2020}
}
```
### Contributions
Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset. | 6,911 | [
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pn_summary | 2023-01-25T14:42:36.000Z | [
"task_categories:summarization",
"task_categories:text-classification",
"task_ids:news-articles-summarization",
"task_ids:news-articles-headline-generation",
"task_ids:text-simplification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fa",
"license:mit",
"arxiv:2012.11204",
"region:us"
] | null | A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification.
It is imperative to consider that the newlines were replaced with the `[n]` symbol. Please interpret them into normal newlines (for ex. `t.replace("[n]", "\n")`) and then use them for your purposes. | @article{pnSummary, title={Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization},
author={Mehrdad Farahani, Mohammad Gharachorloo, Mohammad Manthouri},
year={2020},
eprint={2012.11204},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 4 | 182 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- fa
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
- text-classification
task_ids:
- news-articles-summarization
- news-articles-headline-generation
- text-simplification
- topic-classification
paperswithcode_id: pn-summary
pretty_name: Persian News Summary (PnSummary)
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: article
dtype: string
- name: summary
dtype: string
- name: category
dtype:
class_label:
names:
'0': Economy
'1': Roads-Urban
'2': Banking-Insurance
'3': Agriculture
'4': International
'5': Oil-Energy
'6': Industry
'7': Transportation
'8': Science-Technology
'9': Local
'10': Sports
'11': Politics
'12': Art-Culture
'13': Society
'14': Health
'15': Research
'16': Education-University
'17': Tourism
- name: categories
dtype: string
- name: network
dtype:
class_label:
names:
'0': Tahlilbazaar
'1': Imna
'2': Shana
'3': Mehr
'4': Irna
'5': Khabaronline
- name: link
dtype: string
config_name: 1.0.0
splits:
- name: train
num_bytes: 309436493
num_examples: 82022
- name: validation
num_bytes: 21311817
num_examples: 5592
- name: test
num_bytes: 20936820
num_examples: 5593
download_size: 89591141
dataset_size: 351685130
---
# Dataset Card for Persian News Summary (pn_summary)
## 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/hooshvare/pn-summary/
- **Paper:** https://arxiv.org/abs/2012.11204
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [Mehrdad Farahani](mailto:m3hrdadfphi@gmail.com)
### Dataset Summary
A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification.
It is imperative to consider that the newlines were replaced with the `[n]` symbol. Please interpret them into normal newlines (for ex. `t.replace("[n]", "\n")`) and then use them for your purposes.
### Supported Tasks and Leaderboards
The dataset is prepared for Abstractive/Extractive summarization tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification.
### Languages
The dataset covers Persian mostly and somewhere a combination with English.
## Dataset Structure
### Data Instances
A record consists of 8 features:
```python
record = ['id','title', 'article', 'summary', 'category', 'categories', 'network', 'link']
```
In the following, you can see an example of `pn_summmary`.
```json
{
"article": "به گزارش شانا، علی کاردر امروز (۲۷ دی ماه) در مراسم تودیع محسن قمصری، مدیر سابق امور بین الملل شرکت ملی نفت ایران و معارفه سعید خوشرو، مدیر جدید امور بین الملل این شرکت، گفت: مدیریت امور بین\u200eالملل به عنوان یکی از تاثیرگذارترین مدیریت\u200cهای شرکت ملی نفت ایران در دوران تحریم\u200cهای ظالمانه غرب علیه کشورمان بسیار هوشمندانه عمل کرد و ما توانستیم به خوبی از عهده تحریم\u200cها برآییم. [n] وی افزود: مجموعه امور بین الملل در همه دوران\u200cها با سختی\u200cها و مشکلات بسیاری مواجه بوده است، به ویژه در دوره اخیر به دلیل مسائل پیرامون تحریم وظیفه سنگینی بر عهده داشت که با تدبیر مدیریت خوب این مجموعه سربلند از آن بیرون آمد. [n] کاردر با قدردانی از زحمات محسن قمصری، به سلامت مدیریت امور بین الملل این شرکت اشاره کرد و افزود: محوریت کار مدیریت اموربین الملل سلامت مالی بوده است. [n] وی بر ضرورت نهادینه سازی جوانگرایی در مدیریت شرکت ملی نفت ایران تاکید کرد و گفت: مدیریت امور بین الملل در پرورش نیروهای زبده و کارآزموده آنچنان قوی عملکرده است که برای انتخاب مدیر جدید مشکلی وجود نداشت. [n] کاردر، حرفه\u200eای\u200eگری و کار استاندارد را از ویژگی\u200cهای مدیران این مدیریت برشمرد و گفت: نگاه جامع، خلاقیت و نوآوری و بکارگیری نیروهای جوان باید همچنان مد نظر مدیریت جدید امور بین الملل شرکت ملی نفت ایران باشد.",
"categories": "نفت",
"category": 5,
"id": "738e296491f8b24c5aa63e9829fd249fb4428a66",
"link": "https://www.shana.ir/news/275284/%D9%85%D8%AF%DB%8C%D8%B1%DB%8C%D8%AA-%D9%81%D8%B1%D9%88%D8%B4-%D9%86%D9%81%D8%AA-%D8%AF%D8%B1-%D8%AF%D9%88%D8%B1%D8%A7%D9%86-%D8%AA%D8%AD%D8%B1%DB%8C%D9%85-%D9%87%D9%88%D8%B4%D9%85%D9%86%D8%AF%D8%A7%D9%86%D9%87-%D8%B9%D9%85%D9%84-%DA%A9%D8%B1%D8%AF",
"network": 2,
"summary": "مدیرعامل شرکت ملی نفت، عملکرد مدیریت امور بین\u200eالملل این شرکت را در دوران تحریم بسیار هوشمندانه خواند و گفت: امور بین الملل در دوران پس از تحریم\u200eها نیز می\u200cتواند نقش بزرگی در تسریع روند توسعه داشته باشد.",
"title": "مدیریت فروش نفت در دوران تحریم هوشمندانه عمل کرد"
}
```
### Data Fields
- `id (string)`: ID of the news.
- `title (string)`: The title of the news.
- `article (string)`: The article of the news.
- `summary (string)`: The summary of the news.
- `category (int)`: The category of news in English (index of categories), including `Economy`, `Roads-Urban`, `Banking-Insurance`, `Agriculture`, `International`, `Oil-Energy`, `Industry`, `Transportation`, `Science-Technology`, `Local`, `Sports`, `Politics`, `Art-Culture`, `Society`, `Health`, `Research`, `Education-University`, `Tourism`.
- `categories (string)`: The category and sub-category of the news in Persian.
- `network (int)`: The news agency name (index of news agencies), including `Tahlilbazaar`, `Imna`, `Shana`, `Mehr`, `Irna`, `Khabaronline`.
- `link (string)`: The link of the news.
The category in English includes 18 different article categories from economy to tourism.
```bash
Economy, Roads-Urban, Banking-Insurance, Agriculture, International, Oil-Energy, Industry, Transportation, Science-Technology, Local, Sports, Politics, Art-Culture, Society, Health, Research, Education-University, Tourism
```
### Data Splits
Training (82,022 records, 8 features), validation (5,592 records, 8 features), and test split (5,593 records and 8 features).
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The dataset comprises numerous articles of various categories that have been crawled from six news agency websites (Tahlilbazaar, Imna, Shana, Mehr, Irna, and Khabaronline).
### Annotations
#### Annotation process
Each record (article) includes the long original text as well as a human-generated summary. The total number of cleaned articles is 93,207 (from 200,000 crawled articles).
#### Who are the annotators?
The dataset was organized by [Mehrdad Farahani](https://github.com/m3hrdadfi), [Mohammad Gharachorloo](https://github.com/baarsaam) and [Mohammad Manthouri](https://github.com/mmanthouri) for this paper [Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization](https://arxiv.org/abs/2012.11204)
### 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
This dataset was curated by [Mehrdad Farahani](https://github.com/m3hrdadfi), [Mohammad Gharachorloo](https://github.com/baarsaam) and [Mohammad Manthouri](https://github.com/mmanthouri).
### Licensing Information
This dataset is licensed under MIT License.
### Citation Information
```bibtex
@article{pnSummary,
title={Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization},
author={Mehrdad Farahani, Mohammad Gharachorloo, Mohammad Manthouri},
year={2020},
eprint={2012.11204},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@m3hrdadfi](https://github.com/m3hrdadfi) for adding this dataset. | 9,315 | [
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] |
species_800 | 2023-06-16T11:33:29.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition,
which we here use to identifynames of species and other taxa in text. The tool, SPECIES, is more than an order of
magnitude faster and as accurate as existing tools. The precision and recall was assessed both on an existing gold-standard
corpus and on a new corpus of 800 abstracts, which were manually annotated after the development of the tool. The corpus
comprises abstracts from journals selected to represent many taxonomic groups, which gives insights into which types of
organism names are hard to detect and which are easy. Finally, we have tagged organism names in the entire Medline database
and developed a web resource, ORGANISMS, that makes the results accessible to the broad community of biologists. | @article{pafilis2013species,
title={The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text},
author={Pafilis, Evangelos and Frankild, Sune P and Fanini, Lucia and Faulwetter, Sarah and Pavloudi, Christina and Vasileiadou, Aikaterini and Arvanitidis, Christos and Jensen, Lars Juhl},
journal={PloS one},
volume={8},
number={6},
pages={e65390},
year={2013},
publisher={Public Library of Science}
} | 2 | 182 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: species800
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B
'2': I
config_name: species_800
splits:
- name: train
num_bytes: 2579096
num_examples: 5734
- name: validation
num_bytes: 385756
num_examples: 831
- name: test
num_bytes: 737760
num_examples: 1631
download_size: 18204624
dataset_size: 3702612
---
# 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:** [SPECIES](https://species.jensenlab.org/)
- **Repository:**
- **Paper:** https://doi.org/10.1371/journal.pone.0065390
- **Leaderboard:**
- **Point of Contact:** [Lars Juhl Jensen](mailto:lars.juhl.jensen@cpr.ku.dk)
### Dataset Summary
S800 Corpus: a novel abstract-based manually annotated corpus. S800 comprises 800 PubMed abstracts in which organism mentions were identified and mapped to the corresponding NCBI Taxonomy identifiers.
To increase the corpus taxonomic mention diversity the S800 abstracts were collected by selecting 100 abstracts from the following 8 categories: bacteriology, botany, entomology, medicine, mycology, protistology, virology and zoology. S800 has been annotated with a focus at the species level; however, higher taxa mentions (such as genera, families and orders) have also been considered.
The Species-800 dataset was pre-processed and split based on the dataset of Pyysalo (https://github.com/spyysalo/s800).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`).
## Dataset Structure
### Data Instances
```
{'id': '0',
'tokens': ['Methanoregula',
'formicica',
'sp',
'.',
'nov',
'.',
',',
'a',
'methane',
'-',
'producing',
'archaeon',
'isolated',
'from',
'methanogenic',
'sludge',
'.'],
'ner_tags': [1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
```
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no species mentioned, `1` signals the first token of a species and `2` the subsequent tokens of the species.
### 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
The species-level S800 corpus is subject to Medline restrictions.
### Citation Information
Original data:
```
@article{pafilis2013species,
title={The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text},
author={Pafilis, Evangelos and Frankild, Sune P and Fanini, Lucia and Faulwetter, Sarah and Pavloudi, Christina and Vasileiadou, Aikaterini and Arvanitidis, Christos and Jensen, Lars Juhl},
journal={PloS one},
volume={8},
number={6},
pages={e65390},
year={2013},
publisher={Public Library of Science}
}
```
Source data of this dataset:
```
@article{10.1093/bioinformatics/btz682,
author = {Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
title = "{BioBERT: a pre-trained biomedical language representation model for biomedical text mining}",
journal = {Bioinformatics},
volume = {36},
number = {4},
pages = {1234-1240},
year = {2019},
month = {09},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btz682},
url = {https://doi.org/10.1093/bioinformatics/btz682},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/4/1234/48983216/bioinformatics\_36\_4\_1234.pdf},
}
```
and
```
https://github.com/spyysalo/s800
```
### Contributions
Thanks to [@edugp](https://github.com/edugp) for adding this dataset. | 5,802 | [
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Fraser/short-jokes | 2021-02-24T08:31:31.000Z | [
"region:us"
] | Fraser | Copy of [Kaggle dataset](https://www.kaggle.com/abhinavmoudgil95/short-jokes), adding to Huggingface for ease of use.
Description from Kaggle:
Context
Generating humor is a complex task in the domain of machine learning, and it requires the models to understand the deep semantic meaning of a joke in order to generate new ones. Such problems, however, are difficult to solve due to a number of reasons, one of which is the lack of a database that gives an elaborate list of jokes. Thus, a large corpus of over 0.2 million jokes has been collected by scraping several websites containing funny and short jokes.
Visit my Github repository for more information regarding collection of data and the scripts used.
Content
This dataset is in the form of a csv file containing 231,657 jokes. Length of jokes ranges from 10 to 200 characters. Each line in the file contains a unique ID and joke.
Disclaimer
It has been attempted to keep the jokes as clean as possible. Since the data has been collected by scraping websites, it is possible that there may be a few jokes that are inappropriate or offensive to some people. | null | 5 | 182 | 2022-03-02T23:29:22 | Copy of [Kaggle dataset](https://www.kaggle.com/abhinavmoudgil95/short-jokes), adding to Huggingface for ease of use.
Description from Kaggle:
Context
Generating humor is a complex task in the domain of machine learning, and it requires the models to understand the deep semantic meaning of a joke in order to generate new ones. Such problems, however, are difficult to solve due to a number of reasons, one of which is the lack of a database that gives an elaborate list of jokes. Thus, a large corpus of over 0.2 million jokes has been collected by scraping several websites containing funny and short jokes.
Visit my Github repository for more information regarding collection of data and the scripts used.
Content
This dataset is in the form of a csv file containing 231,657 jokes. Length of jokes ranges from 10 to 200 characters. Each line in the file contains a unique ID and joke.
Disclaimer
It has been attempted to keep the jokes as clean as possible. Since the data has been collected by scraping websites, it is possible that there may be a few jokes that are inappropriate or offensive to some people.
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Zaid/coqa_expanded | 2021-10-04T18:48:15.000Z | [
"region:us"
] | Zaid | \\nCoQA: A Conversational Question Answering Challenge | \\n@InProceedings{SivaAndAl:Coca,
author = {Siva, Reddy and Danqi, Chen and Christopher D., Manning},
title = {WikiQA: A Challenge Dataset for Open-Domain Question Answering},
journal = { arXiv},
year = {2018},
} | 2 | 182 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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qwant/squad_fr | 2023-04-19T14:37:09.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:extended|squad",
"language:fr",
"license:cc-by-4.0",
"region:us"
] | qwant | SQuAD-fr is a French translated version of the Stanford Question Answering Dataset (SQuAD), the reference corpus to evaluate question answering models' performances in English.
It consists of 100K question-answer pairs on 500+ articles derived from the original English dataset and represents a large-scale dataset for closed-domain question answering on factoid questions in French.
SQuAD-fr serves as a means of data augmentation on FQuAD and PIAF benchmarks, with 90K+ translated training pairs. | @inproceedings{cattan:hal-03336060,
TITLE = {{On the Usability of Transformers-based models for a French Question-Answering task}},
AUTHOR = {Cattan, Oralie and Servan, Christophe and Rosset, Sophie},
URL = {https://hal.archives-ouvertes.fr/hal-03336060},
BOOKTITLE = {{Recent Advances in Natural Language Processing (RANLP)}},
ADDRESS = {Varna, Bulgaria},
YEAR = {2021},
MONTH = Sep,
PDF = {https://hal.archives-ouvertes.fr/hal-03336060/file/RANLP_2021_transformers_usability.pdf},
HAL_ID = {hal-03336060},
HAL_VERSION = {v1},
} | 6 | 182 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- fr
license:
- cc-by-4.0
multilinguality:
- monolingual
- translation
paperswithcode_id: squad
pretty_name: SQuAD-fr
size_categories:
- 10K<n<100K
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- extractive-qa
- closed-domain-qa
---
# Dataset Card for "squad_fr"
## 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)
## Dataset Description
- **Paper:** [On the Usability of Transformers-based models for a French Question-Answering task](https://hal.archives-ouvertes.fr/hal-03336060)
- **Size of downloaded dataset files:** 10 MB
- **Size of the generated dataset:** 73 MB
- **Total amount of disk used:** 83 MB
### Dataset Summary
SQuAD-fr:
- a translated version of the Stanford Question Answering Dataset (SQuAD) into French
- obtained through automatic translation of the English dataset
- a reading comprehension dataset, consisting of approximately 90K factoid questions on Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage
- serves as a means of data augmentation on FQuAD and PIAF benchmarks
### Supported Tasks and Leaderboards
- `closed-domain-qa`, `text-retrieval`: This dataset is intended to be used for `closed-domain-qa`, but can also be used for information retrieval tasks.
### Languages
This dataset is exclusively in French.
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 10 MB
- **Size of the generated dataset:** 73 MB
- **Total amount of disk used:** 83 MB
An example of 'train' looks as follows.
```
{
"answers": {
"answer_start": [1],
"text": ["This is a test text"]
},
"context": "This is a test context.",
"id": "1",
"question": "Is this a test?",
"title": "train test"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name |train|validation|
|----------|----:|---------:|
|1.1.0|87514| 17492|
## Dataset Creation
### Curation Rationale
Usability of Transformer-based models, instability relating to data scarcity, investigation of data augmentation, hyperparameters optimization and cross-lingual transfer on the performance of a question-answering task in French.
### Source Data
#### Initial Data Collection and Normalization
validation: manually collected gold standards, chrf scores and bleu evaluation
#### 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
Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
### Citation Information
```
@inproceedings{cattan:hal-03336060,
TITLE = {{On the Usability of Transformers-based models for a French Question-Answering task}},
AUTHOR = {Cattan, Oralie and Servan, Christophe and Rosset, Sophie},
URL = {https://hal.archives-ouvertes.fr/hal-03336060},
BOOKTITLE = {{Recent Advances in Natural Language Processing (RANLP)}},
ADDRESS = {Varna, Bulgaria},
YEAR = {2021},
MONTH = Sep,
PDF = {https://hal.archives-ouvertes.fr/hal-03336060/file/RANLP_2021_transformers_usability.pdf},
HAL_ID = {hal-03336060},
HAL_VERSION = {v1},
}
``` | 5,765 | [
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] |
tner/mit_restaurant | 2022-08-10T11:25:17.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"region:us"
] | tner | [mit_restaurant NER dataset](https://groups.csail.mit.edu/sls/downloads/) | null | 2 | 182 | 2022-07-16T11:12:45 | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: MIT Restaurant
---
# Dataset Card for "tner/mit_restaurant"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Dataset:** MIT restaurant
- **Domain:** Restaurant
- **Number of Entity:** 8
### Dataset Summary
MIT Restaurant NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `Rating`, `Amenity`, `Location`, `Restaurant_Name`, `Price`, `Hours`, `Dish`, `Cuisine`.
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'tags': [0, 0, 0, 0, 0, 0, 0, 0, 5, 3, 4, 0],
'tokens': ['can', 'you', 'find', 'the', 'phone', 'number', 'for', 'the', 'closest', 'family', 'style', 'restaurant']
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/mit_restaurant/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-Rating": 1,
"I-Rating": 2,
"B-Amenity": 3,
"I-Amenity": 4,
"B-Location": 5,
"I-Location": 6,
"B-Restaurant_Name": 7,
"I-Restaurant_Name": 8,
"B-Price": 9,
"B-Hours": 10,
"I-Hours": 11,
"B-Dish": 12,
"I-Dish": 13,
"B-Cuisine": 14,
"I-Price": 15,
"I-Cuisine": 16
}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|mit_restaurant |6900 | 760| 1521|
| 1,539 | [
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cdminix/libritts-aligned | 2023-10-11T19:46:28.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"annotations_creators:crowdsourced",
"language:en",
"license:cc-by-4.0",
"speech",
"audio",
"automatic-speech-recognition",
"text-to-speech",
"arxiv:1904.02882",
"arxiv:2211.16049",
"region:us"
] | cdminix | Dataset used for loading TTS spectrograms and waveform audio with alignments and a number of configurable "measures", which are extracted from the raw audio. | @article{zen2019libritts,
title={LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech},
author={Zen, Heiga and Dang, Viet and Clark, Rob and Zhang, Yu and Weiss, Ron J and Jia, Ye and Chen, Zhifeng and Wu, Yonghui},
journal={Interspeech},
year={2019}
}
@article{https://doi.org/10.48550/arxiv.2211.16049,
author = {Minixhofer, Christoph and Klejch, Ondřej and Bell, Peter},
title = {Evaluating and reducing the distance between synthetic and real speech distributions},
year = {2022}
} | 4 | 182 | 2023-05-14T10:29:46 | ---
pretty_name: LibriTTS Corpus with Forced Alignments
annotations_creators:
- crowdsourced
language: en
tags:
- speech
- audio
- automatic-speech-recognition
- text-to-speech
license:
- cc-by-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
extra_gated_prompt: "When using this dataset to download LibriTTS, you agree to the terms on https://www.openslr.org"
---
> There is also an identical dataset for the new libritts-r dataset at [cdminix/libritts-r-aligned](https://huggingface.co/datasets/cdminix/libritts-r-aligned)
# Dataset Card for LibriTTS with Forced Alignments (and Measures)
UPDATE: The preprocessed alignments are now in this repository, so montreal forced aligner does not have to run locally.
## Requirements
- ``pip install alignments phones`` **(required)**
- ``pip install speech-collator`` (optional)
## Example Item
```json
{
'id': '100_122655_000073_000002.wav',
'speaker': '100',
'text': 'the day after, diana and mary quitted it for distant b.',
'start': 0.0,
'end': 3.6500000953674316,
'phones': ['[SILENCE]', 'ð', 'ʌ', '[SILENCE]', 'd', 'eɪ', '[SILENCE]', 'æ', 'f', 't', 'ɜ˞', '[COMMA]', 'd', 'aɪ', 'æ', 'n', 'ʌ', '[SILENCE]', 'æ', 'n', 'd', '[SILENCE]', 'm', 'ɛ', 'ɹ', 'i', '[SILENCE]', 'k', 'w', 'ɪ', 't', 'ɪ', 'd', '[SILENCE]', 'ɪ', 't', '[SILENCE]', 'f', 'ɜ˞', '[SILENCE]', 'd', 'ɪ', 's', 't', 'ʌ', 'n', 't', '[SILENCE]', 'b', 'i', '[FULL STOP]'],
'phone_durations': [5, 2, 4, 0, 5, 13, 0, 16, 7, 5, 20, 2, 6, 9, 15, 4, 2, 0, 11, 3, 5, 0, 3, 8, 9, 8, 0, 13, 3, 5, 3, 6, 4, 0, 8, 5, 0, 9, 5, 0, 7, 5, 6, 7, 4, 5, 10, 0, 3, 35, 9],
'audio': '/dev/shm/metts/train-clean-360-alignments/100/100_122655_000073_000002.wav'
}
```
The phones are IPA phones, and the phone durations are in frames (assuming a hop length of 256, sample rate of 22050 and window length of 1024). These attributes can be changed using the ``hop_length``, ``sample_rate`` and ``window_length`` arguments to ``LibriTTSAlign``.
## Data Collator
This dataset comes with a data collator which can be used to create batches of data for training.
It can be installed using ``pip install speech-collator`` ([MiniXC/speech-collator](https://www.github.com/MiniXC/speech-collator)) and can be used as follows:
```python
import json
from datasets import load_dataset
from speech_collator import SpeechCollator
from torch.utils.data import DataLoader
dataset = load_dataset('cdminix/libritts-aligned', split="train")
speaker2ixd = json.load(open("speaker2idx.json"))
phone2ixd = json.load(open("phone2idx.json"))
collator = SpeechCollator(
speaker2ixd=speaker2idx,
phone2ixd=phone2idx ,
)
dataloader = DataLoader(dataset, collate_fn=collator.collate_fn, batch_size=8)
```
You can either download the ``speaker2idx.json`` and ``phone2idx.json`` files from [here](https://huggingface.co/datasets/cdminix/libritts-aligned/tree/main/data) or create them yourself using the following code:
```python
import json
from datasets import load_dataset
from speech_collator import SpeechCollator, create_speaker2idx, create_phone2idx
dataset = load_dataset("cdminix/libritts-aligned", split="train")
# Create speaker2idx and phone2idx
speaker2idx = create_speaker2idx(dataset, unk_idx=0)
phone2idx = create_phone2idx(dataset, unk_idx=0)
# save to json
with open("speaker2idx.json", "w") as f:
json.dump(speaker2idx, f)
with open("phone2idx.json", "w") as f:
json.dump(phone2idx, f)
```
### Measures
When using ``speech-collator`` you can also use the ``measures`` argument to specify which measures to use. The following example extracts Pitch and Energy on the fly.
```python
import json
from torch.utils.data import DataLoader
from datasets import load_dataset
from speech_collator import SpeechCollator, create_speaker2idx, create_phone2idx
from speech_collator.measures import PitchMeasure, EnergyMeasure
dataset = load_dataset("cdminix/libritts-aligned", split="train")
speaker2idx = json.load(open("data/speaker2idx.json"))
phone2idx = json.load(open("data/phone2idx.json"))
# Create SpeechCollator
speech_collator = SpeechCollator(
speaker2idx=speaker2idx,
phone2idx=phone2idx,
measures=[PitchMeasure(), EnergyMeasure()],
return_keys=["measures"]
)
# Create DataLoader
dataloader = DataLoader(
dataset,
batch_size=8,
collate_fn=speech_collator.collate_fn,
)
```
COMING SOON: Detailed documentation on how to use the measures at [MiniXC/speech-collator](https://www.github.com/MiniXC/speech-collator).
## Splits
This dataset has the following splits:
- ``train``: All the training data, except one sample per speaker which is used for validation.
- ``dev``: The validation data, one sample per speaker.
- ``train.clean.100``: Training set derived from the original materials of the train-clean-100 subset of LibriSpeech.
- ``train.clean.360``: Training set derived from the original materials of the train-clean-360 subset of LibriSpeech.
- ``train.other.500``: Training set derived from the original materials of the train-other-500 subset of LibriSpeech.
- ``dev.clean``: Validation set derived from the original materials of the dev-clean subset of LibriSpeech.
- ``dev.other``: Validation set derived from the original materials of the dev-other subset of LibriSpeech.
- ``test.clean``: Test set derived from the original materials of the test-clean subset of LibriSpeech.
- ``test.other``: Test set derived from the original materials of the test-other subset of LibriSpeech.
## Environment Variables
There are a few environment variable which can be set.
- ``LIBRITTS_VERBOSE``: If set, will print out more information about the dataset creation process.
- ``LIBRITTS_MAX_WORKERS``: The number of workers to use when creating the alignments. Defaults to ``cpu_count()``.
- ``LIBRITTS_PATH``: The path to download LibriTTS to. Defaults to the value of ``HF_DATASETS_CACHE``.
# Citation
When using LibriTTS please cite the following papers:
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://arxiv.org/abs/1904.02882)
- [Montreal Forced Aligner: Trainable text-speech alignment using Kaldi](https://www.researchgate.net/publication/319185277_Montreal_Forced_Aligner_Trainable_Text-Speech_Alignment_Using_Kaldi)
When using the Measures please cite the following paper (ours):
- [Evaluating and reducing the distance between synthetic and real speech distributions](https://arxiv.org/abs/2211.16049) | 6,442 | [
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allenai/peS2o | 2023-07-18T20:01:34.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:10B<n<100B",
"source_datasets:allenai/s2orc",
"language:en",
"license:odc-by",
"biology",
"chemistry",
"engineering",
"computer science",
"physics",
"material science",
"math",
"psychology",
"economics",
"political science",
"business",
"geology",
"sociology",
"geography",
"environmental science",
"art",
"history",
"philosophy",
"region:us"
] | allenai | null | @techreport{peS2o,
author = {Luca Soldaini and Kyle Lo},
year = 2023,
title = {{peS2o (Pretraining Efficiently on S2ORC) Dataset}},
institution = {{Allen Institute for AI}},
note = {ODC-By, \\url{https://github.com/allenai/pes2o}}
} | 90 | 182 | 2023-06-29T04:54:16 | ---
license:
- odc-by
task_categories:
- text-generation
- fill-mask
language:
- en
tags:
- biology
- chemistry
- engineering
- computer science
- physics
- material science
- math
- psychology
- economics
- political science
- business
- geology
- sociology
- geography
- environmental science
- art
- history
- philosophy
pretty_name: peS2o (Pretraining Efficiently on S2ORC)
size_categories:
- 10B<n<100B
source_datasets:
- allenai/s2orc
---
<p align="center" style="margin-top: -2em">
<img src="https://huggingface.co/datasets/allenai/pes2o/resolve/main/logo.png" alt="peS2o logo. It's a picure of a mortar and pestle with documents flying in." width=384px height=auto>
</p>
<p align="center" style="font-size: 1.2em; margin-top: -1em"><i>Pretraining Effectively on <a href="https://github.com/allenai/s2orc">S2ORC</a>!</i></p>
The peS2o dataset is a collection of ~40M creative open-access academic papers,
cleaned, filtered, and formatted for pre-training of language models. It is derived from
the [Semantic Scholar Open Research Corpus][2]([Lo et al, 2020][1]), or S2ORC.
We release multiple version of peS2o, each with different processing and knowledge cutoff
date. We recommend you to use the latest version available.
If you use this dataset, please cite:
```bibtex
@techreport{peS2o,
author = {Luca Soldaini and Kyle Lo},
year = 2023,
title = {{peS2o (Pretraining Efficiently on S2ORC) Dataset}},
institution = {{Allen Institute for AI}},
note = {ODC-By, \url{https://github.com/allenai/pes2o}}
}
```
## Document Format
Each document in the dataset is a dictionary with the following fields:
- `added`: Date the document was added to the corpus.
- `created`: Best-guess date for when the document was first published. Some have resolution down to the day, only down to the year.
- `id`: Semantic Scholar Corpus ID of the document; it can be used with the [Semantic Scholar API](https://api.semanticscholar.org/) to retrieve metadata about the document (e.g., fields of study, authors).
- `source`: Collection from which the document was sourced from. At the moment, two are supported:
- `s2orc`: collection of full-text papers
- `s2ag`: collection of title and abstracts
- `text`: Text of the document. Paragraphs are separated by two newlines (`\n\n`).
- `version`: version of peS2o.
------
## peS2o V1
### Key Facts
- *Knowledge cutoff*: 2023-01-03
- *Number of documents*: 67.56M
- *Number of whitespace-separated tokens*: 47.37M
### Processing
Processing differs slightly wether it was derived from the full-text corpus (`s2orc`) or the title and abstract corpus (`s2ag`).
#### S2ORC-derived documents
Unfiltered, S2ORC contains 11.3M papers and 46.9B whitespace-separated tokens as of 2023-01-03. To derive peS2o v1, we impose the following constraints:
- The paper must have a title and abstract.
- From each paper, we use [Grobid](https://github.com/kermitt2/grobid) to extract section headers and paragraphs; figures, tables, and references, and any other non-textual content is removed. Title and abstracts are also available, but they come from the Semantic Scholar metadata (obtained through the APIs), not Grobid.
- The paper must be in English.
- To determine the language of each document, we use the [pycld3](https://github.com/bsolomon1124/pycld3) library
- We run pycld3 on the first 2000 characters of each paragraph in the paper.
- The language of the paper is the most common language of the paragraphs.
- The paper must have at least 500 whitespace-separated words.
- The paper was published after 1969; papers published before this date are often obtained through OCR and contain unrecoverable errors.
- The paper must have at least 5 paragraphs.
- All sections that have a average log word probability of less than `-20` are removed.
- To calculate the average log word probability, we use word frequencies extracted from the [1T Web Ngram corpus](https://catalog.ldc.upenn.edu/LDC2006T13); specifically, we use the list available [created by Rachel Tatman](https://www.kaggle.com/datasets/rtatman/english-word-frequency). A copy is hosted [here](https://ai2-s2-research-public.s3-us-west-2.amazonaws.com/lucas/google-1T-unigram/unigram_freq.csv).
- The most frequent word in the paper consists of alpha characters only, and it appears in less than 7.5% of the document.
- Words are obtained by splitting the text on whitespace.
The train set contains papers published before 2022-12-01;
the validation set includes documents published after 2022-12-01 and until 2023-01-03.
#### S2AG-derived documents
The S2AG corpus contains titles and abstracts of papers in Semantic Scholar.
Unfiltered, the corpus contains 91.1M papers and 15.5B whitespace-separated tokens as of 2023-01-03. To derive peS2o v1, we impose the following constraints:
- Abstract must be in English.
- To calculate the language, we once again use pycld3
- Title must be in English, or have average unigram log probability greater than -20.
- Abstract must be in English.
- Abstract must have higher than -20 average unigram log probability.
- Abstract must have at least 50 words.
- Abstract must have no more than 1000 words.
- The most frequent word in the union of text and abstract must be a 2+ character alpha word, or it can be `a` followed by a 2+ character alpha word.
- Paper was published after 1969.
#### Statistics
| Dataset | Split | # Documents | # Words |
|:-------:|:-------:|:-----------:|:--------------:|
|s2orc | train | 8,242,162 | 36,088,195,908 |
|s2orc | valid | 51,323 | 255,139,074 |
|s2ag | train | 59,382,301 | 11,009,123,378 |
|s2ag | valid | 111,228 | 24,398,512 |
------
## peS2o V2
### Key Facts
- *Knowledge cutoff*: 2023-01-03
- *Number of documents*: 38.97M
- *Number of whitespace-separated tokens**: 42.01B
### Processing
peS2o V2 is largely the same as V1, but it includes additional heuristics s2ag aimed at filtering out OCR errors from abstract.
First, we check if the abstract was obtained from Semantic Scholar sources that are likely to contain OCR'ed content. For any abstract derived from those sources, we count how often the text contains subsequences matching `\b([A-Za-z]\s)([a-z]\s)*[A-Za-z]\b`, i.e. individual alpha letters separated by a space. This heuristic matches cases such as `A b stra ct` (2 matching subsequences), where the OCR parser inserted erroneous spaces.
Any abstract with more than 4 matching subsequences is removed.
#### Statistics
| Dataset | Split | # Documents | # Words |
|:-------:|:-----:|------------:|---------------:|
| s2orc | train | 8,242,162 | 36,088,195,908 |
| s2orc | valid | 51,323 | 255,139,074 |
| s2ag | train | 30,569,017 | 5,920,099,207 |
| s2ag | valid | 109,709 | 24,029,459 |
[1]: https://aclanthology.org/2020.acl-main.447/
[2]: https://github.com/allenai/s2orc
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composite/pauq | 2023-10-28T09:35:31.000Z | [
"region:us"
] | composite | Pauq is a first Russian text-to-SQL dataset translated from original Spider dataset
with corrections and refinements of question, queries and databases. | @inproceedings{bakshandaeva-etal-2022-pauq,
title = "{PAUQ}: Text-to-{SQL} in {R}ussian",
author = "Bakshandaeva, Daria and
Somov, Oleg and
Dmitrieva, Ekaterina and
Davydova, Vera and
Tutubalina, Elena",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.175", | 2 | 182 | 2023-07-17T09:45:17 | ---
dataset_info:
- config_name: ru_os
features:
- name: id
dtype: string
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dtype: string
- name: source
dtype: string
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splits:
- name: train
num_examples: 8800
- name: test
num_examples: 1074
- config_name: en_os
features:
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splits:
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- config_name: ru_trl
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splits:
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- config_name: en_trl
features:
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splits:
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- config_name: ru_tsl
features:
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splits:
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- name: test
num_examples: 1969
- config_name: en_tsl
features:
- name: id
dtype: string
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dtype: string
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splits:
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- name: test
num_examples: 1974
---
# 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:**
Link to databases: https://drive.google.com/file/d/1Xjbp207zfCaBxhPgt-STB_RxwNo2TIW2/view
### 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 [@github-username](https://github.com/<github-username>) for adding this dataset. | 5,809 | [
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manu/project_gutenberg | 2023-09-07T15:33:32.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:fr",
"language:en",
"language:zh",
"language:pt",
"language:pl",
"language:nl",
"language:ru",
"language:sv",
"language:it",
"language:de",
"language:es",
"region:us"
] | manu | null | null | 2 | 182 | 2023-09-07T14:14:10 | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: de
num_bytes: 1070196924
num_examples: 3131
- name: en
num_bytes: 25616345280
num_examples: 61340
- name: es
num_bytes: 496728508
num_examples: 1202
- name: fr
num_bytes: 2338871137
num_examples: 5493
- name: it
num_bytes: 383733486
num_examples: 1008
- name: nl
num_bytes: 504939551
num_examples: 1420
- name: pl
num_bytes: 4864460
num_examples: 34
- name: pt
num_bytes: 204058452
num_examples: 1111
- name: ru
num_bytes: 943593
num_examples: 6
- name: sv
num_bytes: 116664385
num_examples: 388
- name: zh
num_bytes: 174238359
num_examples: 437
download_size: 14399256761
dataset_size: 30911584135
task_categories:
- text-generation
language:
- fr
- en
- zh
- pt
- pl
- nl
- ru
- sv
- it
- de
- es
pretty_name: Project Gutenberg
size_categories:
- 10K<n<100K
---
# Dataset Card for "Project Gutenberg"
Project Gutenberg is a library of over 70,000 free eBooks, hosted at https://www.gutenberg.org/.
All examples correspond to a single book, and contain a header and a footer of a few lines (delimited by a *** Start of *** and *** End of *** tags).
### Usage
```python
from datasets import load_dataset
ds = load_dataset("manu/project_gutenberg", split="fr", streaming=True)
print(next(iter(ds)))
```
### License
Full license is available here:
https://www.gutenberg.org/policy/license.html
#### Summary
For nearly all uses, in nearly all parts of the world, the opening words of all of our eBooks apply: This eBook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at [www.gutenberg.org]. If you are not located in the United States, you’ll have to check the laws of the country where you are located before using this ebook.”
##### Using the Project Gutenberg Trademark
If you want to use the name Project Gutenberg anywhere in the ebooks you distribute or on the distribution medium or in advertising you have to obey these rules:
- you may only distribute verbatim copies of the ebooks. No changes are allowed to the ebook contents. (Though reformatting the ebook to a different file format is considered okay).
- If you charge money for the copies you distribute, you have to pay royalties to Project Gutenberg.
- You must refund your clients for defective copies or if they don’t agree with the Project Gutenberg license.
If you don’t agree with any of the above mentioned restrictions, you may not use the Project Gutenberg trademark. You may still distribute the ebooks if you strip the Project Gutenberg license and all references to Project Gutenberg. | 2,927 | [
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] |
vlsp-2023-vllm/grade_12_exams | 2023-09-30T08:28:29.000Z | [
"region:us"
] | vlsp-2023-vllm | null | null | 0 | 182 | 2023-09-10T19:54:48 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: metadata
struct:
- name: grade
dtype: int64
- name: language
dtype: string
- name: subject
dtype: string
- name: choices
struct:
- name: label
sequence: string
- name: text
sequence: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 921887
num_examples: 1955
- name: validation
num_bytes: 224168
num_examples: 488
download_size: 461705
dataset_size: 1146055
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "grade_12_exams"
Reference: https://huggingface.co/datasets/exams | 805 | [
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great_code | 2022-11-18T20:05:00.000Z | [
"task_categories:table-to-text",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | The dataset for the variable-misuse task, described in the ICLR 2020 paper 'Global Relational Models of Source Code' [https://openreview.net/forum?id=B1lnbRNtwr]
This is the public version of the dataset used in that paper. The original, used to produce the graphs in the paper, could not be open-sourced due to licensing issues. See the public associated code repository [https://github.com/VHellendoorn/ICLR20-Great] for results produced from this dataset.
This dataset was generated synthetically from the corpus of Python code in the ETH Py150 Open dataset [https://github.com/google-research-datasets/eth_py150_open]. | @inproceedings{DBLP:conf/iclr/HellendoornSSMB20,
author = {Vincent J. Hellendoorn and
Charles Sutton and
Rishabh Singh and
Petros Maniatis and
David Bieber},
title = {Global Relational Models of Source Code},
booktitle = {8th International Conference on Learning Representations, {ICLR} 2020,
Addis Ababa, Ethiopia, April 26-30, 2020},
publisher = {OpenReview.net},
year = {2020},
url = {https://openreview.net/forum?id=B1lnbRNtwr},
timestamp = {Thu, 07 May 2020 17:11:47 +0200},
biburl = {https://dblp.org/rec/conf/iclr/HellendoornSSMB20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 1 | 181 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
paperswithcode_id: null
pretty_name: GREAT
dataset_info:
features:
- name: id
dtype: int32
- name: source_tokens
sequence: string
- name: has_bug
dtype: bool
- name: error_location
dtype: int32
- name: repair_candidates
sequence: string
- name: bug_kind
dtype: int32
- name: bug_kind_name
dtype: string
- name: repair_targets
sequence: int32
- name: edges
list:
list:
- name: before_index
dtype: int32
- name: after_index
dtype: int32
- name: edge_type
dtype: int32
- name: edge_type_name
dtype: string
- name: provenances
list:
- name: datasetProvenance
struct:
- name: datasetName
dtype: string
- name: filepath
dtype: string
- name: license
dtype: string
- name: note
dtype: string
splits:
- name: train
num_bytes: 14705534822
num_examples: 1798742
- name: validation
num_bytes: 1502956919
num_examples: 185656
- name: test
num_bytes: 7880762248
num_examples: 968592
download_size: 23310374002
dataset_size: 24089253989
---
# Dataset Card for GREAT
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** None
- **Repository:** https://github.com/google-research-datasets/great
- **Paper:** https://openreview.net/forum?id=B1lnbRNtwr
- **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. | 4,054 | [
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webnlg/challenge-2023 | 2023-03-10T11:22:40.000Z | [
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-db_pedia",
"source_datasets:original",
"language:br",
"language:cy",
"language:ga",
"language:mt",
"language:ru",
"license:cc-by-sa-3.0",
"license:cc-by-nc-sa-4.0",
"license:gfdl",
"region:us"
] | webnlg | 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}
} | 3 | 181 | 2023-03-10T08:30:03 | ---
annotations_creators:
- found
language_creators:
- crowdsourced
language:
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- ru
license:
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- cc-by-nc-sa-4.0
- gfdl
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-db_pedia
- original
task_categories:
- tabular-to-text
task_ids:
- rdf-to-text
paperswithcode_id: null
pretty_name: WebNLG 2023 challenge
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---
# 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 2023 challenge](https://synalp.gitlabpages.inria.fr/webnlg-challenge/challenge_2023/)
- **Repository:** [GitHub repository](https://github.com/WebNLG/2023-Challenge)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [webnlg-challenge@inria.fr](mailto:webnlg-challenge@inria.fr)
### Dataset Summary
The WebNLG 2023 challenge focuses on four under-resourced languages which are severely under-represented in research on
text generation, namely Maltese, Irish, Breton and Welsh. In addition, WebNLG 2023 once again includes Russian, which
was first featured in WebNLG 2020.
The challenge focuses on RDF-to-text generation, similarly to WebNLG 2017 but targeting Breton, Irish, Maltese, Welsh,
and Russian;
The 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 4 RDF triples:
```
<entry category="Company" eid="Id21" shape="(X (X) (X) (X) (X))" shape_type="sibling" size="4">
<modifiedtripleset>
<mtriple>Trane | foundingDate | 1913-01-01</mtriple>
<mtriple>Trane | location | Ireland</mtriple>
<mtriple>Trane | foundationPlace | La_Crosse,_Wisconsin</mtriple>
<mtriple>Trane | numberOfEmployees | 29000</mtriple>
</modifiedtripleset>
</entry>
```
the aim is to generate a text such as (English text):
```
Trane, which was founded on January 1st 1913 in La Crosse, Wisconsin, is based in Ireland. It has 29,000 employees.
```
or (Russian text):
```
Компания "Тране", основанная 1 января 1913 года в Ла-Кроссе в штате Висконсин, находится в Ирландии. В компании работают 29 тысяч человек.
```
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 is used in the [WebNLG 2023](https://synalp.gitlabpages.inria.fr/webnlg-challenge/challenge_2023/)
challenge.
Results are evaluated with automatic metrics: [BLEU](https://huggingface.co/metrics/bleu),
[METEOR](https://huggingface.co/metrics/meteor), [ChrF++](https://huggingface.co/metrics/chrf),
[TER](https://huggingface.co/metrics/ter) and [BERTscore](https://huggingface.co/metrics/bertscore).
Additionally, result are assessed according to criteria such as grammaticality/correctness, appropriateness/adequacy,
fluency/naturalness, etc., by native speakers.
### Languages
The dataset comprises Breton (`br`), Welsh (`cy`), Irish (`ga`), Maltese (`mt`) and Russian (`ru`) languages.
## 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:
```
{'category': 'Airport',
'size': 1,
'eid': '1',
'original_triple_sets': {'otriple_set': [['Aarhus_Airport | cityServed | "Aarhus, Denmark"@en']]},
'modified_triple_sets': {'mtriple_set': [['Aarhus_Airport | cityServed | "Aarhus, Denmark"']]},
'shape': '(X (X))',
'shape_type': 'NA',
'lex': {'comment': ['good', 'good', '', ''],
'lid': ['Id1', 'Id2', 'Id3', 'Id3'],
'text': ['Aarhus a zo an aro-vezh Aarhus.',
"Aarhus a servijit ar c'hêr Aarhus.",
'The Aarhus is the airport of Aarhus, Denmark.',
'Aarhus Airport serves the city of Aarhus, Denmark.'],
'lang': ['br', 'br', 'en', 'en']}}
```
### 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.
### Data Splits
The dataset is split into train and validation:
| language | train | validation |
|----------|------:|-----------:|
| br | 13211 | 1399 |
| cy | 13211 | 1665 |
| ga | 13211 | 1665 |
| mt | 13211 | 1665 |
| ru | 5573 | 790 |
## 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}
}
```
### Contributions
Thanks to [@albertvillanova](https://huggingface.co/albertvillanova) for adding this dataset. | 16,781 | [
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instruction-tuning-sd/cartoonization | 2023-05-11T15:16:08.000Z | [
"task_categories:image-to-image",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | instruction-tuning-sd | null | null | 5 | 181 | 2023-03-17T09:13:34 | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: cartoonized_image
dtype: image
splits:
- name: train
num_bytes: 3257571330
num_examples: 5000
download_size: 3296272284
dataset_size: 3257571330
size_categories:
- 1K<n<10K
language:
- en
task_categories:
- image-to-image
---
# Instruction-prompted cartoonization dataset
This dataset was created from 5000 images randomly sampled from the [Imagenette dataset](https://github.com/fastai/imagenette). For more
details on how the dataset was created, check out [this directory](https://github.com/sayakpaul/instruction-tuned-sd/tree/main/data_preparation).
Following figure depicts the data preparation workflow:
<p align="center">
<img src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/cartoonization_data_wheel.png" width=600/>
</p>
## Known limitations and biases
The dataset was derived from Imagenette, which, in turn, was derived from [ImageNet](https://www.image-net.org/). So, naturally, this
dataset inherits the limitations and biases of ImageNet.
## Licensing
The dataset was derived from Imagenette, which, in turn, was derived from [ImageNet](https://www.image-net.org/). So, this dataset's license
is the same as ImageNet. | 1,315 | [
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Babelscape/multinerd | 2023-04-20T12:43:31.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:nl",
"language:pl",
"language:pt",
"language:ru",
"language:zh",
"license:cc-by-nc-sa-4.0",
"structure-prediction",
"region:us"
] | Babelscape | null | null | 9 | 181 | 2023-04-20T11:49:21 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
- zh
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: multinerd-dataset
tags:
- structure-prediction
---
## Table of Contents
- [Description](#description)
- [Dataset Structure](#dataset-structure)
- [Additional Information](#additional-information)
## Dataset Card for MultiNERD dataset
## Dataset Description
- **Summary:** Training data for fine-grained NER in 10 languages.
- **Repository:** [https://github.com/Babelscape/multinerd](https://github.com/Babelscape/multinerd)
- **Paper:** [https://aclanthology.org/multinerd](https://aclanthology.org/2022.findings-naacl.60/)
- **Point of Contact:** [tedeschi@babelscape.com](tedeschi@babelscape.com)
## Description
- **Summary:** In a nutshell, MultiNERD is the first **language-agnostic** methodology for automatically creating **multilingual, multi-genre and fine-grained annotations** for **Named Entity Recognition** and **Entity Disambiguation**. Specifically, it can be seen an extension of the combination of two prior works from our research group that are [WikiNEuRal](https://www.github.com/Babelscape/wikineural), from which we took inspiration for the state-of-the-art silver-data creation methodology, and [NER4EL](https://www.github.com/Babelscape/NER4EL), from which we took the fine-grained classes and inspiration for the entity linking part. The produced dataset covers: **10 languages** (Chinese, Dutch, English, French, German, Italian, Polish, Portuguese, Russian and Spanish), **15 NER categories** (Person (PER), Location (LOC), Organization (ORG}), Animal (ANIM), Biological entity (BIO), Celestial Body (CEL), Disease (DIS), Event (EVE), Food (FOOD), Instrument (INST), Media (MEDIA), Plant (PLANT), Mythological entity (MYTH), Time (TIME) and Vehicle (VEHI)), and **2 textual genres** ([Wikipedia](https://www.wikipedia.org/) and [WikiNews](https://www.wikinews.org/));
- **Repository:** [https://github.com/Babelscape/multinerd](https://github.com/Babelscape/multinerd)
- **Paper:** [https://aclanthology.org/multinerd](https://aclanthology.org/2022.findings-naacl.60/)
- **Point of Contact:** [tedeschi@babelscape.com](tedeschi@babelscape.com)
## Dataset Structure
The data fields are the same among all splits.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels (`int`).
- `lang`: a `string` feature. Full list of language: Chinese (zh), Dutch (nl), English (en), French (fr), German (de), Italian (it), Polish (pl), Portugues (pt), Russian (ru), Spanish (es).
- The full tagset with indices is reported below:
```python
{
"O": 0,
"B-PER": 1,
"I-PER": 2,
"B-ORG": 3,
"I-ORG": 4,
"B-LOC": 5,
"I-LOC": 6,
"B-ANIM": 7,
"I-ANIM": 8,
"B-BIO": 9,
"I-BIO": 10,
"B-CEL": 11,
"I-CEL": 12,
"B-DIS": 13,
"I-DIS": 14,
"B-EVE": 15,
"I-EVE": 16,
"B-FOOD": 17,
"I-FOOD": 18,
"B-INST": 19,
"I-INST": 20,
"B-MEDIA": 21,
"I-MEDIA": 22,
"B-MYTH": 23,
"I-MYTH": 24,
"B-PLANT": 25,
"I-PLANT": 26,
"B-TIME": 27,
"I-TIME": 28,
"B-VEHI": 29,
"I-VEHI": 30,
}
```
## Additional Information
- **Licensing Information**: Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
- **Citation Information**: Please consider citing our work if you use data and/or code from this repository.
```bibtex
@inproceedings{tedeschi-navigli-2022-multinerd,
title = "{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)",
author = "Tedeschi, Simone and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.60",
doi = "10.18653/v1/2022.findings-naacl.60",
pages = "801--812",
abstract = "Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.",
}
```
- **Contributions**: Thanks to [@sted97](https://github.com/sted97) for adding this dataset.
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pie/tacred | 2023-09-27T14:43:54.000Z | [
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teknium/openhermes | 2023-09-07T20:41:05.000Z | [
"task_categories:text-generation",
"language:eng",
"distillation",
"synthetic data",
"gpt",
"region:us"
] | teknium | null | null | 57 | 181 | 2023-09-04T01:31:26 | ---
language:
- eng
pretty_name: "OpenHermes-v1.0"
tags:
- distillation
- synthetic data
- gpt
task_categories:
- text-generation
---
# OpenHermes Dataset
The OpenHermes dataset is composed of 242,000 entries of primarily GPT-4 generated data, from open datasets across the AI landscape, including:
OpenHermes 13B is the first fine tune of the Hermes dataset that has a fully open source dataset!
OpenHermes was trained on 242,000 entries of primarily GPT-4 generated data, from open datasets across the AI landscape, including:
- GPTeacher - General Instruct, Roleplay v1, Roleplay v2, and Code Instruct Datasets, by Teknium
- WizardLM (v1, evol_instruct 70k), by WizardLM Team/nlpxucan
- Airoboros GPT-4 (v1.0), by JonDurbin
- Camel-AI's domain expert datasets, by the Camel-AI Team
- CodeAlpaca, by Sahil2801
- GPT4-LLM and Unnatural Instructions, by Microsoft
Filtering included removal of OpenAI refusals, disclaimers, and "As an AI" type examples and more
The base dataset mix is identical to the original Nous-Hermes', minus the Nous-Instruct and PDACTL datasets which were private datasets. | 1,227 | [
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] |
warshakhan/donut_vqa_ISynHMP_all_labels_modified | 2023-09-28T08:29:22.000Z | [
"region:us"
] | warshakhan | null | null | 0 | 181 | 2023-09-28T07:48:17 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 583333339.0
num_examples: 2800
- name: valid
num_bytes: 85997587.0
num_examples: 400
- name: test
num_bytes: 173591889.0
num_examples: 800
download_size: 165381311
dataset_size: 842922815.0
---
# Dataset Card for "donut_vqa_ISynHMP_all_labels_modified"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 722 | [
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hope_edi | 2023-06-01T14:59:49.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:ml",
"language:ta",
"license:cc-by-4.0",
"hope-speech-classification",
"region:us"
] | null | A Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. | @inproceedings{chakravarthi-2020-hopeedi,
title = "{H}ope{EDI}: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion",
author = "Chakravarthi, Bharathi Raja",
booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.peoples-1.5",
pages = "41--53",
abstract = "Over the past few years, systems have been developed to control online content and eliminate abusive, offensive or hate speech content. However, people in power sometimes misuse this form of censorship to obstruct the democratic right of freedom of speech. Therefore, it is imperative that research should take a positive reinforcement approach towards online content that is encouraging, positive and supportive contents. Until now, most studies have focused on solving this problem of negativity in the English language, though the problem is much more than just harmful content. Furthermore, it is multilingual as well. Thus, we have constructed a Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. To our knowledge, this is the first research of its kind to annotate hope speech for equality, diversity and inclusion in a multilingual setting. We determined that the inter-annotator agreement of our dataset using Krippendorff{'}s alpha. Further, we created several baselines to benchmark the resulting dataset and the results have been expressed using precision, recall and F1-score. The dataset is publicly available for the research community. We hope that this resource will spur further research on encouraging inclusive and responsive speech that reinforces positiveness.",
} | 1 | 180 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- ml
- ta
license:
- cc-by-4.0
multilinguality:
- monolingual
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: hopeedi
pretty_name: 'HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality,
Diversity, and Inclusion'
tags:
- hope-speech-classification
dataset_info:
- config_name: english
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Hope_speech
'1': Non_hope_speech
'2': not-English
splits:
- name: train
num_bytes: 2306656
num_examples: 22762
- name: validation
num_bytes: 288663
num_examples: 2843
download_size: 2739901
dataset_size: 2595319
- config_name: tamil
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Hope_speech
'1': Non_hope_speech
'2': not-Tamil
splits:
- name: train
num_bytes: 1531013
num_examples: 16160
- name: validation
num_bytes: 197378
num_examples: 2018
download_size: 1795767
dataset_size: 1728391
- config_name: malayalam
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Hope_speech
'1': Non_hope_speech
'2': not-malayalam
splits:
- name: train
num_bytes: 1492031
num_examples: 8564
- name: validation
num_bytes: 180713
num_examples: 1070
download_size: 1721534
dataset_size: 1672744
config_names:
- english
- malayalam
- tamil
---
# 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:** [Hope Speech Detection for Equality, Diversity, and Inclusion-EACL 2021](https://competitions.codalab.org/competitions/27653#learn_the_details)
- **Repository:** [HopeEDI data repository](https://competitions.codalab.org/competitions/27653#participate-get_data)
- **Paper:** [HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion](https://www.aclweb.org/anthology/2020.peoples-1.5/)
- **Leaderboard:** [Rank list](https://competitions.codalab.org/competitions/27653#results)
- **Point of Contact:** [Bharathi Raja Chakravarthi](mailto:bharathiraja.akr@gmail.com)
### Dataset Summary
A Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. To our knowledge, this is the first research of its kind to annotate hope speech for equality, diversity and inclusion in a multilingual setting.
### Supported Tasks and Leaderboards
To identify hope speech in the comments/posts in social media.
### Languages
English, Tamil and Malayalam
## Dataset Structure
### Data Instances
An example from the English dataset looks as follows:
| text | label |
| :------ | :----- |
| all lives matter .without that we never have peace so to me forever all lives matter. | Hope_speech |
| I think it's cool that you give people a voice to speak out with here on this channel. | Hope_speech |
An example from the Tamil dataset looks as follows:
| text | label |
| :------ | :----- |
| Idha solla ivalo naala | Non_hope_speech |
| இன்று தேசிய பெண் குழந்தைகள் தினம்.. பெண் குழந்தைகளை போற்றுவோம்..அவர்களை பாதுகாப்போம்... | Hope_speech |
An example from the Malayalam dataset looks as follows:
| text | label |
| :------ | :----- |
| ഇത്രെയും കഷ്ടപ്പെട്ട് വളർത്തിയ ആ അമ്മയുടെ മുഖം കണ്ടപ്പോൾ കണ്ണ് നിറഞ്ഞു പോയി | Hope_speech |
| snehikunavar aanayalum pennayalum onnichu jeevikatte..aareyum compel cheythitallalooo..parasparamulla ishtathodeyalle...avarum jeevikatte..🥰🥰 | Hope_speech |
### Data Fields
English
- `text`: English comment.
- `label`: list of the possible values: "Hope_speech", "Non_hope_speech", "not-English"
Tamil
- `text`: Tamil-English code mixed comment.
- `label`: list of the possible values: "Hope_speech", "Non_hope_speech", "not-Tamil"
Malayalam
- `text`: Malayalam-English code mixed comment.
- `label`: list of the possible values: "Hope_speech", "Non_hope_speech", "not-malayalam"
### Data Splits
| | train | validation |
| ----- |------:|-----------:|
| English | 22762 | 2843 |
| Tamil | 16160 | 2018 |
| Malayalam | 8564 | 1070 |
## Dataset Creation
### Curation Rationale
Hope is considered significant for the well-being, recuperation and restoration of human life by health professionals.
Hate speech or offensive language detection dataset is not available for code-mixed Tamil and code-mixed Malayalam, and it does not take into account LGBTIQ, women in STEM and other minorities. Thus, we cannot use existing hate speech or offensive language detection datasets to detect hope or non-hope for EDI of minorities.
### Source Data
#### Initial Data Collection and Normalization
For English, we collected data on recent topics of EDI, including women in STEM, LGBTIQ issues, COVID-19, Black Lives Matters, United Kingdom (UK) versus China, United States of America (USA) versus China and Australia versus China from YouTube video comments. The data was collected from videos of people from English-speaking countries, such as Australia, Canada, the Republic of Ireland, United Kingdom, the United States of America and New Zealand.
For Tamil and Malayalam, we collected data from India on the recent topics regarding LGBTIQ issues, COVID-19, women in STEM, the Indo-China war and Dravidian affairs.
#### Who are the source language producers?
Youtube users
### Annotations
#### Annotation process
We created Google forms to collect annotations from annotators. Each form contained a maximum of 100 comments, and each page contained a maximum of 10 comments to maintain the quality of annotation. We collected information on the gender, educational background and the medium of schooling of the annotator to know the diversity of the annotator and avoid bias. We educated annotators by providing them with YouTube videos on EDI. A minimum of three annotators annotated each form.
#### Who are the annotators?
For English language comments, annotators were from Australia, the Republic of Ireland, the United Kingdom and the United States of America. For Tamil, we were able to get annotations from both people from the state of Tamil Nadu of India and from Sri Lanka. Most of the annotators were graduate or post-graduate students.
### Personal and Sensitive Information
Social media data is highly sensitive, and even more so when it is related to the minority population, such as the LGBTIQ community or women. We have taken full consideration to minimise the risk associated with individual identity in the data by removing personal information from dataset, such as names but not celebrity names. However, to study EDI, we needed to keep information relating to the following characteristics; racial, gender, sexual orientation, ethnic origin and philosophical beliefs. Annotators were only shown anonymised posts and agreed to make no attempts to contact the comment creator. The dataset will only be made available for research purpose to the researcher who agree to follow ethical
guidelines
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This work is licensed under a [Creative Commons Attribution 4.0 International Licence](http://creativecommons.org/licenses/by/4.0/.)
### Citation Information
```
@inproceedings{chakravarthi-2020-hopeedi,
title = "{H}ope{EDI}: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion",
author = "Chakravarthi, Bharathi Raja",
booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.peoples-1.5",
pages = "41--53",
abstract = "Over the past few years, systems have been developed to control online content and eliminate abusive, offensive or hate speech content. However, people in power sometimes misuse this form of censorship to obstruct the democratic right of freedom of speech. Therefore, it is imperative that research should take a positive reinforcement approach towards online content that is encouraging, positive and supportive contents. Until now, most studies have focused on solving this problem of negativity in the English language, though the problem is much more than just harmful content. Furthermore, it is multilingual as well. Thus, we have constructed a Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. To our knowledge, this is the first research of its kind to annotate hope speech for equality, diversity and inclusion in a multilingual setting. We determined that the inter-annotator agreement of our dataset using Krippendorff{'}s alpha. Further, we created several baselines to benchmark the resulting dataset and the results have been expressed using precision, recall and F1-score. The dataset is publicly available for the research community. We hope that this resource will spur further research on encouraging inclusive and responsive speech that reinforces positiveness.",
}
```
### Contributions
Thanks to [@jamespaultg](https://github.com/jamespaultg) for adding this dataset. | 11,187 | [
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wongnai_reviews | 2023-01-25T15:02:56.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:th",
"license:lgpl-3.0",
"region:us"
] | null | Wongnai's review dataset contains restaurant reviews and ratings, mainly in Thai language.
The reviews are in 5 classes ranging from 1 to 5 stars. | null | 2 | 180 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- th
license:
- lgpl-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: WongnaiReviews
dataset_info:
features:
- name: review_body
dtype: string
- name: star_rating
dtype:
class_label:
names:
'0': '1'
'1': '2'
'2': '3'
'3': '4'
'4': '5'
splits:
- name: train
num_bytes: 60691428
num_examples: 40000
- name: test
num_bytes: 9913686
num_examples: 6203
download_size: 16556587
dataset_size: 70605114
---
# Dataset Card for Wongnai_Reviews
## Dataset Description
- **Repository:** https://github.com/wongnai/wongnai-corpus
### Dataset Summary
The Wongnai Review dataset contains restaurant reviews and ratings, almost entirely in Thai language.
The reviews are in 5 classes ranging from 1 to 5 stars.
This dataset was featured in a Kaggle challenge https://www.kaggle.com/c/wongnai-challenge-review-rating-prediction/overview
### Languages
Thai
## Dataset Structure
### Data Fields
- review_body - text of review
- star_rating - an integer star rating (1-5) or -1 (for test)
### Data Splits
Designated train (40,000 reviews) and test (6,204) sets.
### Source Data
#### Initial Data Collection and Normalization
Data was collected by Wongnai from business reviews on their website,
and shared on GitHub and Kaggle.
### Annotations
The reviews are users' own star ratings, so no additional annotation was needed.
## Additional Information
### Dataset Curators
Contributors to original GitHub repo:
- Ekkalak Thongthanomkul
- Tanapol Nearunchorn
- Yuwat Chuesathuchon
### Licensing Information
LGPL-3.0
### Citation Information
See https://github.com/wongnai/wongnai-corpus
### Contributions
Thanks to [@mapmeld](https://github.com/mapmeld), [@cstorm125](https://github.com/cstorm125) for adding this dataset. | 2,036 | [
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] |
nanyy1025/covid_fake_news | 2023-02-24T01:36:24.000Z | [
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"language:en",
"arxiv:2011.03327",
"region:us"
] | nanyy1025 | null | null | 2 | 180 | 2023-02-24T01:01:04 | ---
task_categories:
- text-classification
- zero-shot-classification
language:
- en
---
Constraint@AAAI2021 - COVID19 Fake News Detection in English
```
@misc{patwa2020fighting,
title={Fighting an Infodemic: COVID-19 Fake News Dataset},
author={Parth Patwa and Shivam Sharma and Srinivas PYKL and Vineeth Guptha and Gitanjali Kumari and Md Shad Akhtar and Asif Ekbal and Amitava Das and Tanmoy Chakraborty},
year={2020},
eprint={2011.03327},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 495 | [
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lmsys/mt_bench_human_judgments | 2023-07-20T18:28:15.000Z | [
"task_categories:conversational",
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"arxiv:2306.05685",
"region:us"
] | lmsys | null | null | 37 | 180 | 2023-07-04T14:03:03 | ---
dataset_info:
features:
- name: question_id
dtype: int64
- name: model_a
dtype: string
- name: model_b
dtype: string
- name: winner
dtype: string
- name: judge
dtype: string
- name: conversation_a
list:
- name: content
dtype: string
- name: role
dtype: string
- name: conversation_b
list:
- name: content
dtype: string
- name: role
dtype: string
- name: turn
dtype: int64
splits:
- name: human
num_bytes: 15003469
num_examples: 3355
- name: gpt4_pair
num_bytes: 10679650
num_examples: 2400
download_size: 1388888
dataset_size: 25683119
license: cc-by-4.0
task_categories:
- conversational
- question-answering
language:
- en
size_categories:
- 1K<n<10K
---
## Content
This dataset contains 3.3K expert-level pairwise human preferences for model responses generated by 6 models in response to 80 MT-bench questions.
The 6 models are GPT-4, GPT-3.5, Claud-v1, Vicuna-13B, Alpaca-13B, and LLaMA-13B. The annotators are mostly graduate students with expertise in the topic areas of each of the questions. The details of data collection can be found in our [paper](https://arxiv.org/abs/2306.05685).
## Agreement Calculation
This Colab [notebook](https://colab.research.google.com/drive/1ctgygDRJhVGUJTQy8-bRZCl1WNcT8De6?usp=sharing) shows how to compute the agreement between humans and GPT-4 judge with the dataset. Our results show that humans and GPT-4 judge achieve over 80\% agreement, the same level of agreement between humans.
## Citation
```
@misc{zheng2023judging,
title={Judging LLM-as-a-judge with MT-Bench and Chatbot Arena},
author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zi Lin and Zhuohan Li and Dacheng Li and Eric. P Xing and Hao Zhang and Joseph E. Gonzalez and Ion Stoica},
year={2023},
eprint={2306.05685},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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result-kand2-sdxl-wuerst-karlo/02dd1f44 | 2023-10-10T00:35:21.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 180 | 2023-10-10T00:35:20 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 158
num_examples: 10
download_size: 1302
dataset_size: 158
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "02dd1f44"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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yoruba_bbc_topics | 2023-01-25T15:03:35.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:yo",
"license:unknown",
"region:us"
] | null | A collection of news article headlines in Yoruba from BBC Yoruba.
Each headline is labeled with one of the following classes: africa,
entertainment, health, nigeria, politics, sport or world.
The dataset was presented in the paper:
Hedderich, Adelani, Zhu, Alabi, Markus, Klakow: Transfer Learning and
Distant Supervision for Multilingual Transformer Models: A Study on
African Languages (EMNLP 2020). | @inproceedings{hedderich-etal-2020-transfer,
title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages",
author = "Hedderich, Michael A. and
Adelani, David and
Zhu, Dawei and
Alabi, Jesujoba and
Markus, Udia and
Klakow, Dietrich",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.204",
doi = "10.18653/v1/2020.emnlp-main.204",
} | 0 | 179 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- yo
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: Yoruba Bbc News Topic Classification Dataset (YorubaBbcTopics)
dataset_info:
features:
- name: news_title
dtype: string
- name: label
dtype:
class_label:
names:
'0': africa
'1': entertainment
'2': health
'3': nigeria
'4': politics
'5': sport
'6': world
- name: date
dtype: string
- name: bbc_url_id
dtype: string
splits:
- name: train
num_bytes: 197117
num_examples: 1340
- name: validation
num_bytes: 27771
num_examples: 189
- name: test
num_bytes: 55652
num_examples: 379
download_size: 265480
dataset_size: 280540
---
# Dataset Card for Yoruba BBC News Topic Classification dataset (yoruba_bbc_topics)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** -
- **Repository:** https://github.com/uds-lsv/transfer-distant-transformer-african
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/
- **Leaderboard:** -
- **Point of Contact:** Michael A. Hedderich and David Adelani
{mhedderich, didelani} (at) lsv.uni-saarland.de
### Dataset Summary
A news headline topic classification dataset, similar to AG-news, for Yorùbá. The news headlines were collected from [BBC Yoruba](https://www.bbc.com/yoruba).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Yorùbá (ISO 639-1: yo)
## Dataset Structure
### Data Instances
An instance consists of a news title sentence and the corresponding topic label as well as publishing information (date and website id).
### Data Fields
- `news_title`: A news title.
- `label`: The label describing the topic of the news title. Can be one of the following classes: africa, entertainment, health, nigeria, politics, sport or world.
- `date`: The publication date (in Yorùbá).
- `bbc_url_id`: The identifier of the article in the BBC URL.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@michael-aloys](https://github.com/michael-aloys) for adding this dataset. | 4,156 | [
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taln-ls2n/semeval-2010-pre | 2022-09-23T07:37:43.000Z | [
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"region:us"
] | taln-ls2n | Preprocessed SemEval-2010 Benchmark dataset for Keyphrase Generation. | @inproceedings{boudin-etal-2016-document,
title = "How Document Pre-processing affects Keyphrase Extraction Performance",
author = "Boudin, Florian and
Mougard, Hugo and
Cram, Damien",
booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-3917",
pages = "121--128",
abstract = "The SemEval-2010 benchmark dataset has brought renewed attention to the task of automatic keyphrase extraction. This dataset is made up of scientific articles that were automatically converted from PDF format to plain text and thus require careful preprocessing so that irrevelant spans of text do not negatively affect keyphrase extraction performance. In previous work, a wide range of document preprocessing techniques were described but their impact on the overall performance of keyphrase extraction models is still unexplored. Here, we re-assess the performance of several keyphrase extraction models and measure their robustness against increasingly sophisticated levels of document preprocessing.",
} | 1 | 179 | 2022-04-22T12:10:54 | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
task_categories:
- text-mining
- text-generation
task_ids:
- keyphrase-generation
- keyphrase-extraction
size_categories:
- n<1K
pretty_name: Preprocessed SemEval-2010 Benchmark dataset
---
# Preprocessed SemEval-2010 Benchmark dataset for Keyphrase Generation
## About
SemEval-2010 is a dataset for benchmarking keyphrase extraction and generation models.
The dataset is composed of 244 **full-text** scientific papers collected from the [ACM Digital Library](https://dl.acm.org/).
Keyphrases were annotated by readers and combined with those provided by the authors.
Details about the SemEval-2010 dataset can be found in the original paper [(kim et al., 2010)][kim-2010].
This version of the dataset was produced by [(Boudin et al., 2016)][boudin-2016] and provides four increasingly sophisticated levels of document preprocessing:
* `lvl-1`: default text files provided by the SemEval-2010 organizers.
* `lvl-2`: for each file, we manually retrieved the original PDF file from the ACM Digital Library.
We then extract the enriched textual content of the PDF files using an Optical Character Recognition (OCR) system and perform document logical structure detection using ParsCit v110505.
We use the detected logical structure to remove author-assigned keyphrases and select only relevant elements : title, headers, abstract, introduction, related work, body text and conclusion.
We finally apply a systematic dehyphenation at line breaks.s
* `lvl-3`: we further abridge the input text from level 2 preprocessed documents to the following: title, headers, abstract, introduction, related work, background and conclusion.
* `lvl-4`: we abridge the input text from level 3 preprocessed documents using an unsupervised summarization technique.
We keep the title and abstract and select the most content bearing sentences from the remaining contents.
Titles and abstracts, collected from the [SciCorefCorpus](https://github.com/melsk125/SciCorefCorpus), are also provided.
Details about how they were extracted and cleaned up can be found in [(Chaimongkol et al., 2014)][chaimongkol-2014].
Reference keyphrases are provided in stemmed form (because they were provided like this for the test split in the competition).
They are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in [(Boudin and Gallina, 2021)][boudin-2021].
Text pre-processing (tokenization) is carried out using `spacy` (`en_core_web_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
Stemming (Porter's stemmer implementation provided in `nltk`) is applied before reference keyphrases are matched against the source text.
Details about the process can be found in `prmu.py`.
The <u>P</u>resent reference keyphrases are also ordered by their order of apparition in the concatenation of title and text (lvl-1).
## Content and statistics
The dataset is divided into the following two splits:
| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
| :--------- |------------:|-------:|-------------:|----------:|------------:|--------:|---------:|
| Train | 144 | 184.6 | 15.44 | 42.16 | 7.36 | 26.85 | 23.63 |
| Test | 100 | 203.1 | 14.66 | 40.11 | 8.34 | 27.12 | 24.43 |
Statistics (#words, PRMU distributions) are computed using the title/abstract and not the full text of scientific papers.
The following data fields are available :
- **id**: unique identifier of the document.
- **title**: title of the document.
- **abstract**: abstract of the document.
- **lvl-1**: content of the document with no text processing.
- **lvl-2**: content of the document retrieved from original PDF files and cleaned up.
- **lvl-3**: content of the document further abridged to relevant sections.
- **lvl-4**: content of the document further abridged using an unsupervised summarization technique.
- **keyphrases**: list of reference keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
## References
- (Kim et al., 2010) Su Nam Kim, Olena Medelyan, Min-Yen Kan, and Timothy Baldwin. 2010.
[SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles][kim-2010].
In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 21–26, Uppsala, Sweden. Association for Computational Linguistics.
- (Chaimongkol et al., 2014) Panot Chaimongkol, Akiko Aizawa, and Yuka Tateisi. 2014.
[Corpus for Coreference Resolution on Scientific Papers][chaimongkol-2014].
In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3187–3190, Reykjavik, Iceland. European Language Resources Association (ELRA).
- (Boudin et al., 2016) Florian Boudin, Hugo Mougard, and Damien Cram. 2016.
[How Document Pre-processing affects Keyphrase Extraction Performance][boudin-2016].
In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 121–128, Osaka, Japan. The COLING 2016 Organizing Committee.
- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021].
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
[kim-2010]: https://aclanthology.org/S10-1004/
[chaimongkol-2014]: https://aclanthology.org/L14-1259/
[boudin-2016]: https://aclanthology.org/W16-3917/
[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/
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] |
pythainlp/thainer-corpus-v2 | 2023-03-23T05:23:46.000Z | [
"task_categories:token-classification",
"language:th",
"license:cc-by-3.0",
"region:us"
] | pythainlp | null | null | 0 | 179 | 2023-03-22T16:12:10 | ---
dataset_info:
features:
- name: words
sequence: string
- name: ner
sequence:
class_label:
names:
'0': B-PERSON
'1': I-PERSON
'2': O
'3': B-ORGANIZATION
'4': B-LOCATION
'5': I-ORGANIZATION
'6': I-LOCATION
'7': B-DATE
'8': I-DATE
'9': B-TIME
'10': I-TIME
'11': B-MONEY
'12': I-MONEY
'13': B-FACILITY
'14': I-FACILITY
'15': B-URL
'16': I-URL
'17': B-PERCENT
'18': I-PERCENT
'19': B-LEN
'20': I-LEN
'21': B-AGO
'22': I-AGO
'23': B-LAW
'24': I-LAW
'25': B-PHONE
'26': I-PHONE
'27': B-EMAIL
'28': I-EMAIL
'29': B-ZIP
'30': B-TEMPERATURE
'31': I-TEMPERATURE
'32': B-DTAE
'33': I-DTAE
'34': B-DATA
'35': I-DATA
splits:
- name: train
num_bytes: 3736419
num_examples: 3938
- name: validation
num_bytes: 1214580
num_examples: 1313
- name: test
num_bytes: 1242609
num_examples: 1313
download_size: 974230
dataset_size: 6193608
license: cc-by-3.0
task_categories:
- token-classification
language:
- th
---
# Dataset Card for "thainer-corpus-v2"
Thai Named Entity Recognition Corpus
Home Page: [https://pythainlp.github.io/Thai-NER/version/2](https://pythainlp.github.io/Thai-NER/version/2)
Training script and split data: [https://zenodo.org/record/7761354](https://zenodo.org/record/7761354)
**You can download .conll to train named entity model in [https://zenodo.org/record/7761354](https://zenodo.org/record/7761354).**
**Size**
- Train: 3,938 docs
- Validation: 1,313 docs
- Test: 1,313 Docs
Some data come from crowdsourcing between Dec 2018 - Nov 2019. [https://github.com/wannaphong/thai-ner](https://github.com/wannaphong/thai-ner)
**Domain**
- News (It, politics, economy, social)
- PR (KKU news)
- general
**Source**
- I use sone data from Nutcha’s theses (http://pioneer.chula.ac.th/~awirote/Data-Nutcha.zip) and improve data by rechecking and adding more tagging.
- Twitter
- Blognone.com - It news
- thaigov.go.th
- kku.ac.th
And more (the lists are lost.)
**Tag**
- DATA - date
- TIME - time
- EMAIL - email
- LEN - length
- LOCATION - Location
- ORGANIZATION - Company / Organization
- PERSON - Person name
- PHONE - phone number
- TEMPERATURE - temperature
- URL - URL
- ZIP - Zip code
- MONEY - the amount
- LAW - legislation
- PERCENT - PERCENT
Download: [HuggingFace Hub](https://huggingface.co/datasets/pythainlp/thainer-corpus-v2)
## Cite
> Wannaphong Phatthiyaphaibun. (2022). Thai NER 2.0 (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7761354
or BibTeX
```
@dataset{wannaphong_phatthiyaphaibun_2022_7761354,
author = {Wannaphong Phatthiyaphaibun},
title = {Thai NER 2.0},
month = sep,
year = 2022,
publisher = {Zenodo},
version = {2.0},
doi = {10.5281/zenodo.7761354},
url = {https://doi.org/10.5281/zenodo.7761354}
}
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Thaweewat/alpaca-cleaned-52k-th | 2023-05-09T16:18:02.000Z | [
"task_categories:question-answering",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:th",
"license:cc-by-sa-3.0",
"instruction-finetuning",
"region:us"
] | Thaweewat | null | null | 3 | 179 | 2023-05-09T15:45:46 | ---
license: cc-by-sa-3.0
task_categories:
- question-answering
- summarization
tags:
- instruction-finetuning
language:
- th
size_categories:
- 10K<n<100K
---
# Summary
This is a Thai 🇹🇭-instructed dataset translated from cleaned version of the original Alpaca Dataset released by Stanford using Google Cloud Translation, contain 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 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.
2. **Merged Instructions:** There were many instructions that were merged together in the original dataset for some reason.
3. **Empty outputs:** Some entries in the original dataset had empty outputs.
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.
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.
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.
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.
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.
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:
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
Languages: Thai
Version: 1.0
--- | 4,488 | [
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yxchng/cc15m_yfcc15m | 2023-06-27T01:54:21.000Z | [
"region:us"
] | yxchng | null | null | 0 | 179 | 2023-06-26T07:52:11 | Entry not found | 15 | [
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] |
AILab-CVC/SEED-Bench | 2023-08-02T03:02:59.000Z | [
"task_categories:visual-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | AILab-CVC | null | null | 11 | 179 | 2023-07-28T08:12:52 | ---
license: cc-by-nc-4.0
task_categories:
- visual-question-answering
language:
- en
pretty_name: SEED-Bench
size_categories:
- 10K<n<100K
---
# SEED-Bench Card
## Benchmark details
**Benchmark type:**
SEED-Bench is a large-scale benchmark to evaluate Multimodal Large Language Models (MLLMs).
It consists of 19K multiple choice questions with accurate human annotations, which
covers 12 evaluation dimensions including the comprehension of both the image and video modality.
**Benchmark date:**
SEED-Bench was collected in July 2023.
**Paper or resources for more information:**
https://github.com/AILab-CVC/SEED-Bench
**License:**
Attribution-NonCommercial 4.0 International. It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use.
For the images of SEED-Bench, we use the data from Conceptual Captions Dataset (https://ai.google.com/research/ConceptualCaptions/)
following its license (https://github.com/google-research-datasets/conceptual-captions/blob/master/LICENSE).
Tencent does not hold the copyright for these images and the copyright belongs to the original owner of Conceptual Captions Dataset.
For the videos of SEED-Bench, we use tha data from Something-Something v2 (https://developer.qualcomm.com/software/ai-datasets/something-something),
Epic-kitchen 100 (https://epic-kitchens.github.io/2023) and
Breakfast (https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset/). We only provide the video name. Please download them in their official websites.
**Where to send questions or comments about the benchmark:**
https://github.com/AILab-CVC/SEED-Bench/issues
## Intended use
**Primary intended uses:**
The primary use of SEED-Bench is evaluate Multimodal Large Language Models on spatial and temporal understanding.
**Primary intended users:**
The primary intended users of the Benchmark are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. | 1,986 | [
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nampdn-ai/tiny-orca-textbooks | 2023-09-28T02:15:06.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2309.05463",
"arxiv:2305.07759",
"region:us"
] | nampdn-ai | null | null | 11 | 179 | 2023-08-04T09:44:37 | ---
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Orca Textbooks
size_categories:
- 100K<n<1M
license: cc-by-nc-sa-4.0
---
# Textbook-like Dataset: A Comprehensive Resource for Text-Based Skills Development in Small Language Models
This dataset is a collection of **147k synthetic textbooks** designed to enhance the text-based skills of small language models. The curriculum is meticulously structured to progress from simple to complex tasks, ensuring a gradual and effective learning experience during pretraining or finetuning SLMs.
The inspiration for this dataset comes from the technical report paper, [Textbooks Are All You Need II: phi-1.5 technical report](https://arxiv.org/abs/2309.05463). The source texts incorporated in this dataset are derived from the [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) dataset, a well-known resource in the field.
Emphasizing text-based skills, this dataset serves as a practical reasoning for small language models to learn and do exercise, providing them with a diverse range of skills to learn and adapt from. The step-by-step progression mirrors the structure of a textbook, making it an ideal in-context learning sample.
### Disclaimer
While every effort has been made to ensure the accuracy of the information contained within this dataset, please note that it is provided 'as is' and without any warranties.
The use of the `textbook` field in this dataset is intended for research purposes only. You are advised to verify any information obtained from this dataset before acting upon it.
## Tiny Series
Explore the possibilities and limitations of building Small Language Models with these tiny gems of data!
- [TinyStories](https://arxiv.org/abs/2305.07759): The paper that sparked my interest in the journey of the tiny-* series.
- [tiny-codes](https://huggingface.co/datasets/nampdn-ai/tiny-codes): Collection of 1.6M short and clear code snippets that can help LLM models learn how to reason.
- [tiny-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks): 420k "things of internet" synthetic textbooks.
- [tiny-webtext](https://huggingface.co/datasets/nampdn-ai/tiny-webtext): A 6GB (4.5M records) variety of diverse webtext enriched with critical thinking methods to make unbiased English dataset.
- [tiny-lessons](https://huggingface.co/datasets/nampdn-ai/tiny-lessons): Subset of *tiny-textbooks* dataset, various lessons about "things of internet" augmented in a bite-sized textbook Markdown format.
- [tiny-bridgedict](https://huggingface.co/datasets/nampdn-ai/tiny-bridgedict): A dataset that links and transfers knowledge between English, Vietnamese, Chinese in a tiny multilingual models.
### Others small HQ datasets with textbook-like quality
- [devdocs.io](https://huggingface.co/datasets/nampdn-ai/devdocs.io): FreeCodeCamp has provided 189k comprehensive API documentation across a wide range of tech stacks and programming languages.
- [sciphi-python-textbook](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-python-textbook)
- [textbook_quality_programming](https://huggingface.co/datasets/vikp/textbook_quality_programming)
- [sciphi-textbooks-are-all-you-need](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-textbooks-are-all-you-need)
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] |
TaylorAI/RLCD-generated-preference-data-split | 2023-08-30T20:16:20.000Z | [
"region:us"
] | TaylorAI | null | null | 0 | 179 | 2023-08-30T20:06:24 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: float64
- name: output_1
dtype: string
- name: output_2
dtype: string
- name: preference
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 142629947
num_examples: 160000
- name: validation
num_bytes: 7163731
num_examples: 7999
download_size: 88067760
dataset_size: 149793678
---
# Dataset Card for "RLCD-generated-preference-data-split"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 787 | [
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eurlex | 2022-11-18T20:01:34.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"legal-topic-classification",
"region:us"
] | null | EURLEX57K contains 57k legislative documents in English from EUR-Lex portal, annotated with EUROVOC concepts. | @inproceedings{chalkidis-etal-2019-large,
title = "Large-Scale Multi-Label Text Classification on {EU} Legislation",
author = "Chalkidis, Ilias and Fergadiotis, Emmanouil and Malakasiotis, Prodromos and Androutsopoulos, Ion",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1636",
doi = "10.18653/v1/P19-1636",
pages = "6314--6322"
} | 4 | 178 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: eurlex57k
pretty_name: the EUR-Lex dataset
tags:
- legal-topic-classification
dataset_info:
features:
- name: celex_id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: eurovoc_concepts
sequence: string
config_name: eurlex57k
splits:
- name: train
num_bytes: 167603718
num_examples: 45000
- name: test
num_bytes: 22046706
num_examples: 6000
- name: validation
num_bytes: 21942574
num_examples: 6000
download_size: 50289403
dataset_size: 211592998
---
# Dataset Card for the EUR-Lex dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/
- **Repository:** http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/
- **Paper:** https://www.aclweb.org/anthology/P19-1636/
- **Leaderboard:** N/A
- **Point of Contact:** [Ilias Chalkidis](mailto:ihalk@aueb.gr)
### Dataset Summary
EURLEX57K can be viewed as an improved version of the dataset released by Mencia and Furnkranzand (2007), which has been widely used in Large-scale Multi-label Text Classification (LMTC) research, but is less than half the size of EURLEX57K (19.6k documents, 4k EUROVOC labels) and more than ten years old.
EURLEX57K contains 57k legislative documents in English from EUR-Lex (https://eur-lex.europa.eu) with an average length of 727 words. Each document contains four major zones:
- the header, which includes the title and name of the legal body enforcing the legal act;
- the recitals, which are legal background references; and
- the main body, usually organized in articles.
**Labeling / Annotation**
All the documents of the dataset have been annotated by the Publications Office of EU (https://publications.europa.eu/en) with multiple concepts from EUROVOC (http://eurovoc.europa.eu/).
While EUROVOC includes approx. 7k concepts (labels), only 4,271 (59.31%) are present in EURLEX57K, from which only 2,049 (47.97%) have been assigned to more than 10 documents. The 4,271 labels are also divided into frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively.
### Supported Tasks and Leaderboards
The dataset supports:
**Multi-label Text Classification:** Given the text of a document, a model predicts the relevant EUROVOC concepts.
**Few-shot and Zero-shot learning:** As already noted, the labels can be divided into three groups: frequent (746 labels), few-shot (3,362), and zero- shot (163), depending on whether they were assigned to more than 50, fewer than 50 but at least one, or no training documents, respectively.
### Languages
All documents are written in English.
## Dataset Structure
### Data Instances
```json
{
"celex_id": "31979D0509",
"title": "79/509/EEC: Council Decision of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain",
"text": "COUNCIL DECISION of 24 May 1979 on financial aid from the Community for the eradication of African swine fever in Spain (79/509/EEC)\nTHE COUNCIL OF THE EUROPEAN COMMUNITIES\nHaving regard to the Treaty establishing the European Economic Community, and in particular Article 43 thereof,\nHaving regard to the proposal from the Commission (1),\nHaving regard to the opinion of the European Parliament (2),\nWhereas the Community should take all appropriate measures to protect itself against the appearance of African swine fever on its territory;\nWhereas to this end the Community has undertaken, and continues to undertake, action designed to contain outbreaks of this type of disease far from its frontiers by helping countries affected to reinforce their preventive measures ; whereas for this purpose Community subsidies have already been granted to Spain;\nWhereas these measures have unquestionably made an effective contribution to the protection of Community livestock, especially through the creation and maintenance of a buffer zone north of the river Ebro;\nWhereas, however, in the opinion of the Spanish authorities themselves, the measures so far implemented must be reinforced if the fundamental objective of eradicating the disease from the entire country is to be achieved;\nWhereas the Spanish authorities have asked the Community to contribute to the expenses necessary for the efficient implementation of a total eradication programme;\nWhereas a favourable response should be given to this request by granting aid to Spain, having regard to the undertaking given by that country to protect the Community against African swine fever and to eliminate completely this disease by the end of a five-year eradication plan;\nWhereas this eradication plan must include certain measures which guarantee the effectiveness of the action taken, and it must be possible to adapt these measures to developments in the situation by means of a procedure establishing close cooperation between the Member States and the Commission;\nWhereas it is necessary to keep the Member States regularly informed as to the progress of the action undertaken,",
"eurovoc_concepts": ["192", "2356", "2560", "862", "863"]
}
```
### Data Fields
The following data fields are provided for documents (`train`, `dev`, `test`):
`celex_id`: (**str**) The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR.\
`title`: (**str**) The title of the document.\
`text`: (**str**) The full content of each document, which is represented by its `header`, `recitals` and `main_body`.\
`eurovoc_concepts`: (**List[str]**) The relevant EUROVOC concepts (labels).
If you want to use the descriptors of EUROVOC concepts, similar to Chalkidis et al. (2020), please load: https://archive.org/download/EURLEX57K/eurovoc_concepts.jsonl
```python
import json
with open('./eurovoc_concepts.jsonl') as jsonl_file:
eurovoc_concepts = {json.loads(concept) for concept in jsonl_file.readlines()}
```
### Data Splits
| Split | No of Documents | Avg. words | Avg. labels |
| ------------------- | ------------------------------------ | --- | --- |
| Train | 45,000 | 729 | 5 |
|Development | 6,000 | 714 | 5 |
|Test | 6,000 | 725 | 5 |
## Dataset Creation
### Curation Rationale
The dataset was curated by Chalkidis et al. (2019).\
The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en).
### Source Data
#### Initial Data Collection and Normalization
The original data are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed format.
The documents were downloaded from EUR-Lex portal in HTML format.
The relevant metadata and EUROVOC concepts were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql).
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
* The original documents are available at EUR-Lex portal (https://eur-lex.europa.eu) in an unprocessed HTML format. The HTML code was striped and the documents split into sections.
* The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en).
#### Who are the annotators?
Publications Office of EU (https://publications.europa.eu/en)
### Personal and Sensitive Information
The dataset does not include personal or sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Chalkidis et al. (2019)
### Licensing Information
© European Union, 1998-2021
The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.
The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \
Read more: https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html
### Citation Information
*Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis and Ion Androutsopoulos.*
*Large-Scale Multi-Label Text Classification on EU Legislation.*
*Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Florence, Italy. 2019*
```
@inproceedings{chalkidis-etal-2019-large,
title = "Large-Scale Multi-Label Text Classification on {EU} Legislation",
author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Androutsopoulos, Ion",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1636",
doi = "10.18653/v1/P19-1636",
pages = "6314--6322"
}
```
### Contributions
Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset. | 10,874 | [
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harem | 2023-01-25T14:31:29.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:pt",
"license:unknown",
"region:us"
] | null | The HAREM is a Portuguese language corpus commonly used for Named Entity Recognition tasks. It includes about 93k words, from 129 different texts,
from several genres, and language varieties. The split of this dataset version follows the division made by [1], where 7% HAREM
documents are the validation set and the miniHAREM corpus (with about 65k words) is the test set. There are two versions of the dataset set,
a version that has a total of 10 different named entity classes (Person, Organization, Location, Value, Date, Title, Thing, Event,
Abstraction, and Other) and a "selective" version with only 5 classes (Person, Organization, Location, Value, and Date).
It's important to note that the original version of the HAREM dataset has 2 levels of NER details, namely "Category" and "Sub-type".
The dataset version processed here ONLY USE the "Category" level of the original dataset.
[1] Souza, Fábio, Rodrigo Nogueira, and Roberto Lotufo. "BERTimbau: Pretrained BERT Models for Brazilian Portuguese." Brazilian Conference on Intelligent Systems. Springer, Cham, 2020. | @inproceedings{santos2006harem,
title={Harem: An advanced ner evaluation contest for portuguese},
author={Santos, Diana and Seco, Nuno and Cardoso, Nuno and Vilela, Rui},
booktitle={quot; In Nicoletta Calzolari; Khalid Choukri; Aldo Gangemi; Bente Maegaard; Joseph Mariani; Jan Odjik; Daniel Tapias (ed) Proceedings of the 5 th International Conference on Language Resources and Evaluation (LREC'2006)(Genoa Italy 22-28 May 2006)},
year={2006}
} | 5 | 178 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: HAREM
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PESSOA
'2': I-PESSOA
'3': B-ORGANIZACAO
'4': I-ORGANIZACAO
'5': B-LOCAL
'6': I-LOCAL
'7': B-TEMPO
'8': I-TEMPO
'9': B-VALOR
'10': I-VALOR
'11': B-ABSTRACCAO
'12': I-ABSTRACCAO
'13': B-ACONTECIMENTO
'14': I-ACONTECIMENTO
'15': B-COISA
'16': I-COISA
'17': B-OBRA
'18': I-OBRA
'19': B-OUTRO
'20': I-OUTRO
splits:
- name: train
num_bytes: 1506373
num_examples: 121
- name: test
num_bytes: 1062714
num_examples: 128
- name: validation
num_bytes: 51318
num_examples: 8
download_size: 1887281
dataset_size: 2620405
- config_name: selective
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PESSOA
'2': I-PESSOA
'3': B-ORGANIZACAO
'4': I-ORGANIZACAO
'5': B-LOCAL
'6': I-LOCAL
'7': B-TEMPO
'8': I-TEMPO
'9': B-VALOR
'10': I-VALOR
splits:
- name: train
num_bytes: 1506373
num_examples: 121
- name: test
num_bytes: 1062714
num_examples: 128
- name: validation
num_bytes: 51318
num_examples: 8
download_size: 1715873
dataset_size: 2620405
---
# Dataset Card for HAREM
## 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:** [HAREM homepage](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html)
- **Repository:** [HAREM repository](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html)
- **Paper:** [HAREM: An Advanced NER Evaluation Contest for Portuguese](http://comum.rcaap.pt/bitstream/10400.26/76/1/SantosSecoCardosoVilelaLREC2006.pdf)
- **Point of Contact:** [Diana Santos](mailto:diana.santos@sintef.no)
### Dataset Summary
The HAREM is a Portuguese language corpus commonly used for Named Entity Recognition tasks. It includes about 93k words, from 129 different texts,
from several genres, and language varieties. The split of this dataset version follows the division made by [1], where 7% HAREM
documents are the validation set and the miniHAREM corpus (with about 65k words) is the test set. There are two versions of the dataset set,
a version that has a total of 10 different named entity classes (Person, Organization, Location, Value, Date, Title, Thing, Event,
Abstraction, and Other) and a "selective" version with only 5 classes (Person, Organization, Location, Value, and Date).
It's important to note that the original version of the HAREM dataset has 2 levels of NER details, namely "Category" and "Sub-type".
The dataset version processed here ONLY USE the "Category" level of the original dataset.
[1] Souza, Fábio, Rodrigo Nogueira, and Roberto Lotufo. "BERTimbau: Pretrained BERT Models for Brazilian Portuguese." Brazilian Conference on Intelligent Systems. Springer, Cham, 2020.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Portuguese
## Dataset Structure
### Data Instances
```
{
"id": "HAREM-871-07800",
"ner_tags": [3, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4,
],
"tokens": [
"Abraço", "Página", "Principal", "ASSOCIAÇÃO", "DE", "APOIO", "A", "PESSOAS", "COM", "VIH", "/", "SIDA"
]
}
```
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"O", "B-PESSOA", "I-PESSOA", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-LOCAL", "I-LOCAL", "B-TEMPO", "I-TEMPO", "B-VALOR", "I-VALOR", "B-ABSTRACCAO", "I-ABSTRACCAO", "B-ACONTECIMENTO", "I-ACONTECIMENTO", "B-COISA", "I-COISA", "B-OBRA", "I-OBRA", "B-OUTRO", "I-OUTRO"
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word.
### Data Splits
The data is split into train, validation and test set for each of the two versions (default and selective). The split sizes are as follow:
| Train | Val | Test |
| ------ | ----- | ---- |
| 121 | 8 | 128 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{santos2006harem,
title={Harem: An advanced ner evaluation contest for portuguese},
author={Santos, Diana and Seco, Nuno and Cardoso, Nuno and Vilela, Rui},
booktitle={quot; In Nicoletta Calzolari; Khalid Choukri; Aldo Gangemi; Bente Maegaard; Joseph Mariani; Jan Odjik; Daniel Tapias (ed) Proceedings of the 5 th International Conference on Language Resources and Evaluation (LREC'2006)(Genoa Italy 22-28 May 2006)},
year={2006}
}
```
### Contributions
Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset. | 7,080 | [
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um005 | 2022-11-18T21:58:09.000Z | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:other",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:ur",
"license:unknown",
"region:us"
] | null | UMC005 English-Urdu is a parallel corpus of texts in English and Urdu language with sentence alignments. The corpus can be used for experiments with statistical machine translation.
The texts come from four different sources:
- Quran
- Bible
- Penn Treebank (Wall Street Journal)
- Emille corpus
The authors provide the religious texts of Quran and Bible for direct download. Because of licensing reasons, Penn and Emille texts cannot be redistributed freely. However, if you already hold a license for the original corpora, we are able to provide scripts that will recreate our data on your disk. Our modifications include but are not limited to the following:
- Correction of Urdu translations and manual sentence alignment of the Emille texts.
- Manually corrected sentence alignment of the other corpora.
- Our data split (training-development-test) so that our published experiments can be reproduced.
- Tokenization (optional, but needed to reproduce our experiments).
- Normalization (optional) of e.g. European vs. Urdu numerals, European vs. Urdu punctuation, removal of Urdu diacritics. | @unpublished{JaZeWordOrderIssues2011,
author = {Bushra Jawaid and Daniel Zeman},
title = {Word-Order Issues in {English}-to-{Urdu} Statistical Machine Translation},
year = {2011},
journal = {The Prague Bulletin of Mathematical Linguistics},
number = {95},
institution = {Univerzita Karlova},
address = {Praha, Czechia},
issn = {0032-6585},
} | 0 | 178 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- other
language:
- en
- ur
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: umc005-english-urdu
pretty_name: UMC005 English-Urdu
dataset_info:
- config_name: bible
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- ur
- en
splits:
- name: train
num_bytes: 2350730
num_examples: 7400
- name: validation
num_bytes: 113476
num_examples: 300
- name: test
num_bytes: 104678
num_examples: 257
download_size: 3683565
dataset_size: 2568884
- config_name: quran
features:
- name: id
dtype: string
- name: translation
dtype:
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languages:
- ur
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splits:
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num_bytes: 2929711
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- name: validation
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num_examples: 214
- name: test
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num_examples: 200
download_size: 3683565
dataset_size: 3017623
- config_name: all
features:
- name: id
dtype: string
- name: translation
dtype:
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languages:
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splits:
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num_bytes: 5280441
num_examples: 13400
- name: validation
num_bytes: 156963
num_examples: 514
- name: test
num_bytes: 149079
num_examples: 457
download_size: 3683565
dataset_size: 5586483
---
# Dataset Card for UMC005 English-Urdu
## 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://ufal.ms.mff.cuni.cz/umc/005-en-ur/
- **Repository:** None
- **Paper:** https://www.researchgate.net/publication/268008206_Word-Order_Issues_in_English-to-Urdu_Statistical_Machine_Translation
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** Bushra Jawaid and Daniel Zeman
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | 4,306 | [
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biu-nlp/abstract-sim | 2023-05-29T09:33:17.000Z | [
"region:us"
] | biu-nlp | null | null | 2 | 178 | 2023-05-13T16:43:12 | A dataset of Wikipedia sentences accompannied by valid and invalid abstract descriptions. | 89 | [
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cryptom/ceval-exam | 2023-06-24T00:40:14.000Z | [
"task_categories:text-classification",
"task_categories:multiple-choice",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-nc-sa-4.0",
"arxiv:2305.08322",
"region:us"
] | cryptom | C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. | @article{huang2023ceval,
title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models},
author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian},
journal={arXiv preprint arXiv:2305.08322},
year={2023}
} | 0 | 178 | 2023-06-23T18:40:37 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
- multiple-choice
- question-answering
language:
- zh
pretty_name: C-Eval
size_categories:
- 10K<n<100K
---
C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. Please visit our [website](https://cevalbenchmark.com/) and [GitHub](https://github.com/SJTU-LIT/ceval/tree/main) or check our [paper](https://arxiv.org/abs/2305.08322) for more details.
Each subject consists of three splits: dev, val, and test. The dev set per subject consists of five exemplars with explanations for few-shot evaluation. The val set is intended to be used for hyperparameter tuning. And the test set is for model evaluation. Labels on the test split are not released, users are required to submit their results to automatically obtain test accuracy. [How to submit?](https://github.com/SJTU-LIT/ceval/tree/main#how-to-submit)
### Load the data
```python
from datasets import load_dataset
dataset=load_dataset(r"ceval/ceval-exam",name="computer_network")
print(dataset['val'][0])
# {'id': 0, 'question': '使用位填充方法，以01111110为位首flag，数据为011011111111111111110010，求问传送时要添加几个0____', 'A': '1', 'B': '2', 'C': '3', 'D': '4', 'answer': 'C', 'explanation': ''}
```
More details on loading and using the data are at our [github page](https://github.com/SJTU-LIT/ceval#data).
Please cite our paper if you use our dataset.
```
@article{huang2023ceval,
title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models},
author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian},
journal={arXiv preprint arXiv:2305.08322},
year={2023}
}
```
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AlignmentLab-AI/QualityControl | 2023-10-11T08:07:03.000Z | [
"region:us"
] | AlignmentLab-AI | null | null | 0 | 178 | 2023-10-11T04:53:07 | Entry not found | 15 | [
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europa_eac_tm | 2023-01-25T14:30:11.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:hr",
"language:hu",
"language:is",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:no",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"language:tr",
"license:cc-by-4.0",
"region:us"
] | null | In October 2012, the European Union's (EU) Directorate General for Education and Culture ( DG EAC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-six languages. This resource bears the name EAC Translation Memory, short EAC-TM.
EAC-TM covers up to 26 languages: 22 official languages of the EU (all except Irish) plus Icelandic, Croatian, Norwegian and Turkish. EAC-TM thus contains translations from English into the following 25 languages: Bulgarian, Czech, Danish, Dutch, Estonian, German, Greek, Finnish, French, Croatian, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Maltese, Norwegian, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish and Turkish.
All documents and sentences were originally written in English (source language is English) and then translated into the other languages. The texts were translated by staff of the National Agencies of the Lifelong Learning and Youth in Action programmes. They are typically professionals in the field of education/youth and EU programmes. They are thus not professional translators, but they are normally native speakers of the target language. | @Article{Steinberger2014,
author={Steinberger, Ralf
and Ebrahim, Mohamed
and Poulis, Alexandros
and Carrasco-Benitez, Manuel
and Schl{\"u}ter, Patrick
and Przybyszewski, Marek
and Gilbro, Signe},
title={An overview of the European Union's highly multilingual parallel corpora},
journal={Language Resources and Evaluation},
year={2014},
month={Dec},
day={01},
volume={48},
number={4},
pages={679-707},
issn={1574-0218},
doi={10.1007/s10579-014-9277-0},
url={https://doi.org/10.1007/s10579-014-9277-0}
} | 2 | 177 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hr
- hu
- is
- it
- lt
- lv
- mt
- nl
- 'no'
- pl
- pt
- ro
- sk
- sl
- sv
- tr
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: Europa Education and Culture Translation Memory (EAC-TM)
dataset_info:
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---
# Dataset Card for Europa Education and Culture Translation Memory (EAC-TM)
## 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://ec.europa.eu/jrc/en/language-technologies/eac-translation-memory](https://ec.europa.eu/jrc/en/language-technologies/eac-translation-memory)
- **Paper:** [https://link.springer.com/article/10.1007/s10579-014-9277-0](https://link.springer.com/article/10.1007/s10579-014-9277-0)
- **Point of Contact:** [ralf.steinberg@jrc.ec.europa.eu](mailto:ralf.steinberg@jrc.ec.europa.eu)
### Dataset Summary
This dataset is a corpus of manually produced translations from english to up to 25 languages, released in 2012 by the European Union's Directorate General for Education and Culture (EAC).
To load a language pair that is not part of the config, just specify the language code as language pair. For example, if you want to translate Czech to Greek:
`dataset = load_dataset("europa_eac_tm", language_pair=("cs", "el"))`
### Supported Tasks and Leaderboards
- `text2text-generation`: the dataset can be used to train a model for `machine-translation`. Machine translation models are usually evaluated using metrics such as [BLEU](https://huggingface.co/metrics/bleu), [ROUGE](https://huggingface.co/metrics/rouge) or [SacreBLEU](https://huggingface.co/metrics/sacrebleu). You can use the [mBART](https://huggingface.co/facebook/mbart-large-cc25) model for this task. This task has active leaderboards which can be found at [https://paperswithcode.com/task/machine-translation](https://paperswithcode.com/task/machine-translation), which usually rank models based on [BLEU score](https://huggingface.co/metrics/bleu).
### Languages
The sentences in this dataset were originally written in English (source language is English) and then translated into the other languages. The sentences are extracted from electroniv forms: application and report forms for decentralised actions of EAC's Life-long Learning Programme (LLP) and the Youth in Action Programme. The contents in the electronic forms are technically split into two types: (a) the labels and contents of drop-down menus (referred to as 'Forms' Data) and (b) checkboxes (referred to as 'Reference Data').
The dataset contains traduction of English sentences or parts of sentences to Bulgarian, Czech, Danish, Dutch, Estonian, German, Greek, Finnish, French, Croatian, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Maltese, Norwegian, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish and Turkish.
Language codes:
- `bg`
- `cs`
- `da`
- `de`
- `el`
- `en`
- `es`
- `et`
- `fi`
- `fr`
- `hr`
- `hu`
- `is`
- `it`
- `lt`
- `lv`
- `mt`
- `nl`
- `no`
- `pl`
- `pt`
- `ro`
- `sk`
- `sl`
- `sv`
- `tr`
## Dataset Structure
### Data Instances
```
{
"translation": {
"en":"Sentence to translate",
"<target_language>": "Phrase à traduire",
},
"sentence_type": 0
}
```
### Data Fields
- `translation`: Mapping of sentences to translate (in English) and translated sentences.
- `sentence_type`: Integer value, 0 if the sentence is a 'form data' (extracted from the labels and contents of drop-down menus of the source electronic forms) or 1 if the sentence is a 'reference data' (extracted from the electronic forms checkboxes).
### Data Splits
The data is not splitted (only the `train` split is available).
## Dataset Creation
### Curation Rationale
The EAC-TM is relatively small compared to the JRC-Acquis and to DGT-TM, but it has the advantage that it focuses on a very different domain, namely that of education and culture. Also, it includes translation units for the languages Croatian (HR), Icelandic (IS), Norwegian (Bokmål, NB or Norwegian, NO) and Turkish (TR).
### Source Data
#### Initial Data Collection and Normalization
EAC-TM was built in the context of translating electronic forms: application and report forms for decentralised actions of EAC's Life-long Learning Programme (LLP) and the Youth in Action Programme. All documents and sentences were originally written in English (source language is English) and then translated into the other languages.
The contents in the electronic forms are technically split into two types: (a) the labels and contents of drop-down menus (referred to as 'Forms' Data) and (b) checkboxes (referred to as 'Reference Data'). Due to the different types of data, the two collections are kept separate. For example, labels can be 'Country', 'Please specify your home country' etc., while examples for reference data are 'Germany', 'Basic/general programmes', 'Education and Culture' etc.
The data consists of translations carried out between the end of the year 2008 and July 2012.
#### Who are the source language producers?
The texts were translated by staff of the National Agencies of the Lifelong Learning and Youth in Action programmes. They are typically professionals in the field of education/youth and EU programmes. They are thus not professional translators, but they are normally native speakers of the target language.
### Annotations
#### Annotation process
Sentences were manually translated by humans.
#### Who are the annotators?
The texts were translated by staff of the National Agencies of the Lifelong Learning and Youth in Action programmes. They are typically professionals in the field of education/youth and EU programmes. They are thus not professional translators, but they are normally native speakers of the target language.
### 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
© European Union, 1995-2020
The Commission's reuse policy is implemented by the [Commission Decision of 12 December 2011 on the reuse of Commission documents](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32011D0833).
Unless otherwise indicated (e.g. in individual copyright notices), content owned by the EU on this website is licensed under the [Creative Commons Attribution 4.0 International (CC BY 4.0) licence](http://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed, provided appropriate credit is given and changes are indicated.
You may be required to clear additional rights if a specific content depicts identifiable private individuals or includes third-party works. To use or reproduce content that is not owned by the EU, you may need to seek permission directly from the rightholders. Software or documents covered by industrial property rights, such as patents, trade marks, registered designs, logos and names, are excluded from the Commission's reuse policy and are not licensed to you.
### Citation Information
```
@Article{Steinberger2014,
author={Steinberger, Ralf
and Ebrahim, Mohamed
and Poulis, Alexandros
and Carrasco-Benitez, Manuel
and Schl{\"u}ter, Patrick
and Przybyszewski, Marek
and Gilbro, Signe},
title={An overview of the European Union's highly multilingual parallel corpora},
journal={Language Resources and Evaluation},
year={2014},
month={Dec},
day={01},
volume={48},
number={4},
pages={679-707},
issn={1574-0218},
doi={10.1007/s10579-014-9277-0},
url={https://doi.org/10.1007/s10579-014-9277-0}
}
```
### Contributions
Thanks to [@SBrandeis](https://github.com/SBrandeis) for adding this dataset. | 18,124 | [
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] |
pec | 2023-06-01T14:59:50.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-retrieval",
"task_ids:dialogue-modeling",
"task_ids:utterance-retrieval",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:gpl-3.0",
"region:us"
] | null | \
A dataset of around 350K persona-based empathetic conversations. Each speaker is associated with a persona, which comprises multiple persona sentences. The response of each conversation is empathetic. | \
@inproceedings{zhong2020towards,
title = "Towards Persona-Based Empathetic Conversational Models",
author = "Zhong, Peixiang and
Zhang, Chen and
Wang, Hao and
Liu, Yong and
Miao, Chunyan",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.531",
pages = "6556--6566"} | 4 | 177 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-retrieval
task_ids:
- dialogue-modeling
- utterance-retrieval
paperswithcode_id: pec
pretty_name: Persona-Based Empathetic Conversational
dataset_info:
- config_name: happy
features:
- name: personas
sequence: string
- name: context
sequence: string
- name: context_speakers
sequence: string
- name: response
dtype: string
- name: response_speaker
dtype: string
splits:
- name: train
num_bytes: 643196978
num_examples: 157195
- name: test
num_bytes: 92003042
num_examples: 22730
- name: validation
num_bytes: 81132088
num_examples: 19829
download_size: 252434681
dataset_size: 816332108
- config_name: offmychest
features:
- name: personas
sequence: string
- name: context
sequence: string
- name: context_speakers
sequence: string
- name: response
dtype: string
- name: response_speaker
dtype: string
splits:
- name: train
num_bytes: 518616402
num_examples: 123968
- name: test
num_bytes: 64173390
num_examples: 15324
- name: validation
num_bytes: 66675909
num_examples: 16004
download_size: 252434681
dataset_size: 649465701
- config_name: all
features:
- name: personas
sequence: string
- name: context
sequence: string
- name: context_speakers
sequence: string
- name: response
dtype: string
- name: response_speaker
dtype: string
splits:
- name: train
num_bytes: 1162655628
num_examples: 281163
- name: test
num_bytes: 156310498
num_examples: 38054
- name: validation
num_bytes: 147940164
num_examples: 35833
download_size: 252434681
dataset_size: 1466906290
config_names:
- all
- happy
- offmychest
---
# Dataset Card for PEC
## 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:** [PEC repository](https://github.com/zhongpeixiang/PEC)
- **Paper:** [Towards Persona-Based Empathetic Conversational Models](https://www.aclweb.org/anthology/2020.emnlp-main.531/)
- **Point of Contact:** [Peixiang Zhong](mailto:zhongpeixiang@gmail.com)
### Dataset Summary
The PEC dataset is an English-language dataset of open-domain conversations gathered from two subreddits on Reddit, i.e., happy and offmychest. PEC has around 350K persona-based empathetic conversations. Each utterance is associated with a speaker, and each speaker has a persona of multiple persona sentences. The conversations in PEC are more empathetic than casual conversations. The conversations in the happy domain are mostly positive, whereas the conversations in the offmychest domain are mostly negative.
### Supported Tasks and Leaderboards
- `dialogue-modeling`, `utterance-retrieval`: this dataset can be used to train a generative or retrieval-based conversational model.
### Languages
English
## Dataset Structure
### Data Instances
A typical data example comprises a list of context utterances, a list of context speakers, a response to the context, the response speaker and the persona of the response speaker.
An example from PEC looks as follows:
```
{'context': ['found out this morning i got a job promotion ! ! !'],
'context_speakers': ['HeWentToJared91'],
'personas': [
"i ca n't stand working in the ugli .",
'i ’ve always liked my eyes except for the fact that they ca n’t shoot lasers',
'i feel really bad about myself as a person right now , and i could really use a hand .',
'i drank a coffee , and it just made me feel even more exhausted .',
'i want a natsuki t shirt',
"i 've dealt with depression in the past .",
'i love red dead 2'],
'response': "you look like a nice person ! we 're proud of you , and i bet you earned that promotion !",
'response_speaker': 'tylock'}
```
### Data Fields
- `context`: a list of strings, each string denotes a context utterance.
- `context_speakers`: a list of strings, each string denotes a speaker.
- `response`: a string denoting the response to the `context`.
- `response_speaker`: a string denoting the speaker of `response`.
- `personas`: a list of strings, each string denotes a persona sentence of `response_speaker`.
### Data Splits
The data is split into a training, validation and test set for each of the three domains. Note that the *all* domain is the concatenation of the *happy* and *offmychest* domains.
| domain | train | validation | test |
|------------|-------:|-----------:|------:|
| happy | 157195 | 19829 | 22730 |
| offmychest | 123968 | 16004 | 15324 |
| all | 281163 | 35833 | 38054 |
## Dataset Creation
### Curation Rationale
PEC was built to provide a testbed for machines to learn persona-based empathetic responding. In our empirical analysis, we found that different personas have different styles of empathetic responding. This dataset can also be used to investigate the link between persona and empathy in human conversations. According to our human assessment, the conversations on the happy and offmychest subreddits are significantly more empathetic than casual conversations.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained via the [pushshift API](https://pushshift.io/using-bigquery-with-reddit-data/) via Google BigQuery.
#### Who are the source language producers?
The language producers are users of the [r/happy](https://www.reddit.com/r/happy/), and [r/offmychest](https://www.reddit.com/r/offmychest/) subreddits between 2012 and 2020. No further demographic information was available from the data source.
### Annotations
#### Annotation process
The dataset does not contain any additional annotations.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
The dataset includes the speaker IDs of users on *happy* and *offmychest* subreddits.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop more personalised and empathetic conversational systems, which is an important milestone towards truly human-like conversational agents.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
A small portion of the dataset has the issues of sexism, hate, and harassment. The persona sentences are noisy.
## Additional Information
### Dataset Curators
The dataset was initially created by Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu, and Chunyan Miao, jointly done at Nanyang Technological University and Alibaba Group.
### 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{zhong-etal-2020-towards,
title = "Towards Persona-Based Empathetic Conversational Models",
author = "Zhong, Peixiang and
Zhang, Chen and
Wang, Hao and
Liu, Yong and
Miao, Chunyan",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.531",
pages = "6556--6566"
}
```
### Contributions
Thanks to [@zhongpeixiang](https://github.com/zhongpeixiang) for adding this dataset. | 8,559 | [
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wiki_summary | 2022-11-18T22:00:55.000Z | [
"task_categories:text2text-generation",
"task_categories:translation",
"task_categories:question-answering",
"task_categories:summarization",
"task_ids:abstractive-qa",
"task_ids:explanation-generation",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"task_ids:open-domain-abstractive-qa",
"task_ids:text-simplification",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fa",
"license:apache-2.0",
"region:us"
] | null | \
The dataset extracted from Persian Wikipedia into the form of articles and highlights and cleaned the dataset into pairs of articles and highlights and reduced the articles' length (only version 1.0.0) and highlights' length to a maximum of 512 and 128, respectively, suitable for parsBERT. | \
@misc{Bert2BertWikiSummaryPersian,
author = {Mehrdad Farahani},
title = {Summarization using Bert2Bert model on WikiSummary dataset},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {https://github.com/m3hrdadfi/wiki-summary},
} | 5 | 177 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- fa
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
- translation
- question-answering
- summarization
task_ids:
- abstractive-qa
- explanation-generation
- extractive-qa
- open-domain-qa
- open-domain-abstractive-qa
- text-simplification
pretty_name: WikiSummary
dataset_info:
features:
- name: id
dtype: string
- name: link
dtype: string
- name: title
dtype: string
- name: article
dtype: string
- name: highlights
dtype: string
splits:
- name: train
num_bytes: 207186608
num_examples: 45654
- name: test
num_bytes: 25693509
num_examples: 5638
- name: validation
num_bytes: 23130954
num_examples: 5074
download_size: 255168504
dataset_size: 256011071
---
# Dataset Card for [Needs More Information]
## 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/m3hrdadfi/wiki-summary
- **Repository:** https://github.com/m3hrdadfi/wiki-summary
- **Paper:** [More Information Needed]
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [Mehrdad Farahani](mailto:m3hrdadphi@gmail.com)
### Dataset Summary
The dataset extracted from Persian Wikipedia into the form of articles and highlights and cleaned the dataset into pairs of articles and highlights and reduced the articles' length (only version 1.0.0) and highlights' length to a maximum of 512 and 128, respectively, suitable for parsBERT. This dataset is created to achieve state-of-the-art results on some interesting NLP tasks like Text Summarization.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in Percy.
## Dataset Structure
### Data Instances
```
{
'id' :'0598cfd2ac491a928615945054ab7602034a8f4f',
'link': 'https://fa.wikipedia.org/wiki/انقلاب_1917_روسیه',
'title': 'انقلاب 1917 روسیه',
'article': 'نخست انقلاب فوریه ۱۹۱۷ رخ داد . در این انقلاب پس از یکسری اعتصابات ، تظاهرات و درگیریها ، نیکولای دوم ، آخرین تزار روسیه از سلطنت خلع شد و یک دولت موقت به قدرت رسید . دولت موقت زیر نظر گئورگی لووف و الکساندر کرنسکی تشکیل شد . اکثر اعضای دولت موقت ، از شاخه منشویک حزب سوسیال دموکرات کارگری روسیه بودند . دومین مرحله ، انقلاب اکتبر ۱۹۱۷ بود . انقلاب اکتبر ، تحت نظارت حزب بلشویک (شاخه رادیکال از حزب سوسیال دموکرات کارگری روسیه) و به رهبری ولادیمیر لنین به پیش رفت و طی یک یورش نظامی همهجانبه به کاخ زمستانی سن پترزبورگ و سایر اماکن مهم ، قدرت را از دولت موقت گرفت . در این انقلاب افراد بسیار کمی کشته شدند . از زمان شکست روسیه در جنگ ۱۹۰۵ با ژاپن ، اوضاع بد اقتصادی ، گرسنگی ، عقبماندگی و سرمایهداری و نارضایتیهای گوناگون در بین مردم ، سربازان ، کارگران ، کشاورزان و نخبگان روسیه بهوجود آمدهبود . سرکوبهای تزار و ایجاد مجلس دوما نظام مشروطه حاصل آن دوران است . حزب سوسیال دموکرات ، اصلیترین معترض به سیاستهای نیکلای دوم بود که بهطور گسترده بین دهقانان کشاورزان و کارگران کارخانجات صنعتی علیه سیاستهای سیستم تزار فعالیت داشت . در اوت ۱۹۱۴ میلادی ، امپراتوری روسیه به دستور تزار وقت و به منظور حمایت از اسلاوهای صربستان وارد جنگ جهانی اول در برابر امپراتوری آلمان و امپراتوری اتریش-مجارستان شد . نخست فقط بلشویکها ، مخالف ورود روسیه به این جنگ بودند و میگفتند که این جنگ ، سبب بدتر شدن اوضاع نابسامان اقتصادی و اجتماعی روسیه خواهد شد . در سال ۱۹۱۴ میلادی ، یعنی در آغاز جنگ جهانی اول ، روسیه بزرگترین ارتش جهان را داشت ، حدود ۱۲ میلیون سرباز و ۶ میلیون سرباز ذخیره ؛ ولی در پایان سال ۱۹۱۶ میلادی ، پنج میلیون نفر از سربازان روسیه کشته ، زخمی یا اسیر شده بودند . حدود دو میلیون سرباز نیز محل خدمت خود را ترک کرده و غالبا با اسلحه به شهر و دیار خود بازگشته بودند . در میان ۱۰ یا ۱۱ میلیون سرباز باقیمانده نیز ، اعتبار تزار و سلسله مراتب ارتش و اتوریته افسران بالا دست از بین رفته بود . عوامل نابسامان داخلی اعم از اجتماعی کشاورزی و فرماندهی نظامی در شکستهای روسیه بسیار مؤثر بود . شکستهای روسیه در جنگ جهانی اول ، حامیان نیکلای دوم در روسیه را به حداقل خود رساند . در اوایل فوریه ۱۹۱۷ میلادی اکثر کارگران صنعتی در پتروگراد و مسکو دست به اعتصاب زدند . سپس شورش به پادگانها و سربازان رسید . اعتراضات دهقانان نیز گسترش یافت . سوسیال دموکراتها هدایت اعتراضات را در دست گرفتند . در ۱۱ مارس ۱۹۱۷ میلادی ، تزار وقت روسیه ، نیکلای دوم ، فرمان انحلال مجلس روسیه را صادر کرد ، اما اکثر نمایندگان مجلس متفرق نشدند و با تصمیمات نیکلای دوم مخالفت کردند . سرانجام در پی تظاهرات گسترده کارگران و سپس نافرمانی سربازان در سرکوب تظاهرکنندگان در پتروگراد ، نیکلای دوم از مقام خود استعفا داد . بدین ترتیب حکمرانی دودمان رومانوفها بر روسیه پس از حدود سیصد سال پایان یافت .',
'highlights': 'انقلاب ۱۹۱۷ روسیه ، جنبشی اعتراضی ، ضد امپراتوری روسیه بود که در سال ۱۹۱۷ رخ داد و به سرنگونی حکومت تزارها و برپایی اتحاد جماهیر شوروی انجامید . مبانی انقلاب بر پایه صلح-نان-زمین استوار بود . این انقلاب در دو مرحله صورت گرفت : در طول این انقلاب در شهرهای اصلی روسیه همانند مسکو و سن پترزبورگ رویدادهای تاریخی برجستهای رخ داد . انقلاب در مناطق روستایی و رعیتی نیز پا به پای مناطق شهری در حال پیشروی بود و دهقانان زمینها را تصرف کرده و در حال بازتوزیع آن در میان خود بودند .'
}
```
### Data Fields
- `id`: Article id
- `link`: Article link
- `title`: Title of the article
- `article`: Full text content in the article
- `highlights`: Summary of the article
### Data Splits
| Train | Test | Validation |
|-------------|-------------|-------------|
| 45,654 | 5,638 | 5,074 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
No annotations.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was created by Mehrdad Farahani.
### Licensing Information
[Apache License 2.0](https://github.com/m3hrdadfi/wiki-summary/blob/master/LICENSE)
### Citation Information
```
@misc{Bert2BertWikiSummaryPersian,
author = {Mehrdad Farahani},
title = {Summarization using Bert2Bert model on WikiSummary dataset},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {https://github.com/m3hrdadfi/wiki-summary},
}
```
### Contributions
Thanks to [@tanmoyio](https://github.com/tanmoyio) for adding this dataset. | 7,708 | [
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nielsr/rvlcdip-demo | 2022-03-08T12:11:13.000Z | [
"region:us"
] | nielsr | null | null | 0 | 177 | 2022-03-08T12:11:11 | Entry not found | 15 | [
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] |
Tuana/presidents | 2023-02-28T01:06:47.000Z | [
"region:us"
] | Tuana | null | null | 1 | 177 | 2023-02-28T00:51:03 | ---
dataset_info:
features:
- name: id
dtype: string
- name: content
dtype: string
- name: content_type
dtype: string
- name: meta
struct:
- name: url
dtype: string
- name: _split_id
dtype: int64
- name: id_hash_keys
sequence: string
- name: score
dtype: 'null'
- name: embedding
dtype: 'null'
splits:
- name: train
num_bytes: 9366886
num_examples: 5529
download_size: 4997888
dataset_size: 9366886
---
# Dataset Card for "presidents"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 647 | [
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scielo | 2023-06-01T14:59:47.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:es",
"language:pt",
"license:unknown",
"arxiv:1905.01852",
"region:us"
] | null | A parallel corpus of full-text scientific articles collected from Scielo database in the following languages: English, Portuguese and Spanish. The corpus is sentence aligned for all language pairs, as well as trilingual aligned for a small subset of sentences. Alignment was carried out using the Hunalign algorithm. | @inproceedings{soares2018large,
title={A Large Parallel Corpus of Full-Text Scientific Articles},
author={Soares, Felipe and Moreira, Viviane and Becker, Karin},
booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018)},
year={2018}
} | 1 | 176 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- es
- pt
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: SciELO
dataset_info:
- config_name: en-es
features:
- name: translation
dtype:
translation:
languages:
- en
- es
splits:
- name: train
num_bytes: 71777213
num_examples: 177782
download_size: 22965217
dataset_size: 71777213
- config_name: en-pt
features:
- name: translation
dtype:
translation:
languages:
- en
- pt
splits:
- name: train
num_bytes: 1032669686
num_examples: 2828917
download_size: 322726075
dataset_size: 1032669686
- config_name: en-pt-es
features:
- name: translation
dtype:
translation:
languages:
- en
- pt
- es
splits:
- name: train
num_bytes: 147472132
num_examples: 255915
download_size: 45556562
dataset_size: 147472132
config_names:
- en-es
- en-pt
- en-pt-es
---
# Dataset Card for SciELO
## 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:**[SciELO](https://sites.google.com/view/felipe-soares/datasets#h.p_92uSCyAjWSRB)
- **Repository:**
- **Paper:** [A Large Parallel Corpus of Full-Text Scientific Articles](https://arxiv.org/abs/1905.01852)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A parallel corpus of full-text scientific articles collected from Scielo database in the following languages:English, Portuguese and Spanish.
The corpus is sentence aligned for all language pairs, as well as trilingual aligned for a small subset of sentences.
Alignment was carried out using the Hunalign algorithm.
### Supported Tasks and Leaderboards
The underlying task is machine translation.
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{soares2018large,
title={A Large Parallel Corpus of Full-Text Scientific Articles},
author={Soares, Felipe and Moreira, Viviane and Becker, Karin},
booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018)},
year={2018}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | 4,316 | [
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] |
MonoHime/ru_sentiment_dataset | 2021-05-20T00:57:22.000Z | [
"language:ru",
"sentiment",
"text-classification",
"region:us"
] | MonoHime | null | null | 3 | 176 | 2022-03-02T23:29:22 | ---
language:
- ru
tags:
- sentiment
- text-classification
---
# Dataset with sentiment of Russian text
Contains aggregated dataset of Russian texts from 6 datasets.
## Labels meaning
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## Datasets
**[Sentiment Analysis in Russian](https://www.kaggle.com/c/sentiment-analysis-in-russian/data)**
> Sentiments (positive, negative or neutral) of news in russian language from Kaggle competition.
**[Russian Language Toxic Comments](https://www.kaggle.com/blackmoon/russian-language-toxic-comments/)**
> Small dataset with labeled comments from 2ch.hk and pikabu.ru.
**[Dataset of car reviews for machine learning (sentiment analysis)](https://github.com/oldaandozerskaya/auto_reviews)**
> Glazkova A. The evaluation of the proximity of text categories for solving electronic documents classification tasks //VESTNIK TOMSKOGO GOSUDARSTVENNOGO UNIVERSITETA-UPRAVLENIE VYCHISLITELNAJA TEHNIKA I INFORMATIKA-TOMSK STATE UNIVERSITY JOURNAL OF CONTROL AND COMPUTER SCIENCE. – 2015. – Т. 31. – №. 2. – С. 18-25.
**[Sentiment datasets by Blinov](https://github.com/natasha/corus/issues/14)**
> Datasets contain reviews from different scopes.
**[LINIS Crowd](http://www.linis-crowd.org/)**
> Произведение «LINIS Crowd SENT - тональный словарь и коллекция текстов с тональной разметкой» созданное автором по имени Sergei Koltcov, Olessia Koltsova и Svetlana Alexeeva.
**[Russian Hotel Reviews Dataset](https://drive.google.com/drive/folders/17sa3h4XHcG0MJGrbfOsbL-kDW29CuJul)**
> Hotel reviews in Russian | 1,551 | [
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bigbio/osiris | 2022-12-22T15:46:10.000Z | [
"multilinguality:monolingual",
"language:en",
"license:cc-by-3.0",
"region:us"
] | bigbio | The OSIRIS corpus is a set of MEDLINE abstracts manually annotated
with human variation mentions. The corpus is distributed under the terms
of the Creative Commons Attribution License
Creative Commons Attribution 3.0 Unported License,
which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited (Furlong et al, BMC Bioinformatics 2008, 9:84). | @ARTICLE{Furlong2008,
author = {Laura I Furlong and Holger Dach and Martin Hofmann-Apitius and Ferran Sanz},
title = {OSIRISv1.2: a named entity recognition system for sequence variants
of genes in biomedical literature.},
journal = {BMC Bioinformatics},
year = {2008},
volume = {9},
pages = {84},
doi = {10.1186/1471-2105-9-84},
pii = {1471-2105-9-84},
pmid = {18251998},
timestamp = {2013.01.15},
url = {http://dx.doi.org/10.1186/1471-2105-9-84}
} | 1 | 176 | 2022-11-13T22:11:10 |
---
language:
- en
bigbio_language:
- English
license: cc-by-3.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_3p0
pretty_name: OSIRIS
homepage: https://sites.google.com/site/laurafurlongweb/databases-and-tools/corpora/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for OSIRIS
## Dataset Description
- **Homepage:** https://sites.google.com/site/laurafurlongweb/databases-and-tools/corpora/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED
The OSIRIS corpus is a set of MEDLINE abstracts manually annotated
with human variation mentions. The corpus is distributed under the terms
of the Creative Commons Attribution License
Creative Commons Attribution 3.0 Unported License,
which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited (Furlong et al, BMC Bioinformatics 2008, 9:84).
## Citation Information
```
@ARTICLE{Furlong2008,
author = {Laura I Furlong and Holger Dach and Martin Hofmann-Apitius and Ferran Sanz},
title = {OSIRISv1.2: a named entity recognition system for sequence variants
of genes in biomedical literature.},
journal = {BMC Bioinformatics},
year = {2008},
volume = {9},
pages = {84},
doi = {10.1186/1471-2105-9-84},
pii = {1471-2105-9-84},
pmid = {18251998},
timestamp = {2013.01.15},
url = {http://dx.doi.org/10.1186/1471-2105-9-84}
}
```
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GEM/xsum | 2022-10-24T15:31:30.000Z | [
"task_categories:summarization",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | GEM | This is the XSUM subset of the GEM benchmark. | @inproceedings{narayan-etal-2018-dont,
title = "Don{'}t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization",
author = "Narayan, Shashi and
Cohen, Shay B. and
Lapata, Mirella",
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://aclanthology.org/D18-1206",
doi = "10.18653/v1/D18-1206",
pages = "1797--1807",
abstract = "We introduce {``}extreme summarization{''}, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question {``}What is the article about?{''}. We collect a real-world, large-scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article{'}s topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans.",
} | 0 | 175 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: xsum
---
# Dataset Card for GEM/xsum
## Dataset Description
- **Homepage:** n/a
- **Repository:** https://github.com/EdinburghNLP/XSum
- **Paper:** https://www.aclweb.org/anthology/D18-1206
- **Leaderboard:** N/A
- **Point of Contact:** Shashi Narayan
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/xsum).
### Dataset Summary
XSum is an English news summarization dataset where the task is to predict the first sentence of an article from the rest of it.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/xsum')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/xsum).
#### website
n/a
#### paper
[ACL Anthology](https://www.aclweb.org/anthology/D18-1206)
#### authors
Shashi Narayan, Shay B. Cohen, Mirella Lapata (all affiliated with University of Edinburgh at the time of dataset creation)
## Dataset Overview
### Where to find the Data and its Documentation
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/EdinburghNLP/XSum)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://www.aclweb.org/anthology/D18-1206)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@InProceedings{xsum-emnlp,
author = "Shashi Narayan and Shay B. Cohen and Mirella Lapata",
title = "Don't Give Me the Details, Just the Summary! {T}opic-Aware Convolutional Neural Networks for Extreme Summarization",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing ",
year = "2018",
address = "Brussels, Belgium",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Shashi Narayan
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
shashinarayan@google.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
Since the source of the dataset are BBC articles, the language is in British English of the variation written by journalists.
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
Professional journalists
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The dataset is for the task of abstractive summarization in its extreme form, its about summarizing a document in a single sentence. The idea is to create a short, one-sentence news summary answering the question "What is the article about?".
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Summarization
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Given a news article, produce a single sentence summary of the content of the article.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
University of Edinburgh
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Shashi Narayan, Shay B. Cohen, Mirella Lapata (all affiliated with University of Edinburgh at the time of dataset creation)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
European Research Council (Lapata; award number 681760), the European Union under the Horizon 2020 SUMMA project (Narayan, Cohen; grant agreement 688139), and Huawei Technologies (Cohen).
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
The original data card was written by Laura Perez-Beltrachini and the data loader by Yacine Jernite. Sebastian Gehrmann migrated the data card to the new format and extended it. The v2 data loader was migrated by Abinaya Mahendiran
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `Document`: Input news article.
- `Summary`: One sentence summary of the article.
- `Id`: BBC ID of the article.
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The Document/Summary format is standard for summarization datasets.
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
The labels are the first sentence of the source article.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
'document': 'The researchers have sequenced the genome of a strain of bacterium that causes the virulent infection.\nA survey in 2007 showed that bleeding canker had spread rapidly, with almost half of the two million horse chestnuts displaying symptoms of the disease.\nThe findings have been published in the journal PLoS One.\nA visible symptom of the disease is a lesion on the bark, which oozes a resin on to the trunk or sometimes the branches.\nThe bark underneath the canker is killed, and if cankers manage to go all the way around the trunk then the horse chestnut (Aesculus hippocastanum) will die because it cuts off the food supply. [...]',
'target': "A team of UK scientists hopes to shed light on the mysteries of bleeding canker, a disease that is threatening the nation's horse chestnut trees.",
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
| Section | Number of Documents |
| ------------- |:-------------:|
| Training | 204,045 |
| Validation | 11,332 |
| Testing | 11,334 |
| Total | 226k |
| Section | number of words| number of sentences |
| ------------- |:-------------:| :-------------:|
| Documents | 431.07 | 19.77 |
| Summary | 23.26 | 1.00 |
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The identifiers in the URLs were used to randomly split the dataset into training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) sets.
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Comparable datasets are often very extractive which is not a strategy that works for one-sentence summaries. The dataset curators thus created this dataset as a way to evaluate truly abstractive models
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Same as the communicative goal in GEM: A model should summarize a news article in a single sentence
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The data was collected from articles between 2010 and 2017. No other information
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The collected articles included the following topics: News, Politics, Sports, Weather, Business, Technology, Science, Health, Family, Education, Entertainment and Arts
The dataset curators also used LDA to gain insight into this question and found that the following were the top keywords associated with each topic:
- **T1**: charge, court, murder, police, arrest, guilty, sentence, boy, bail, space, crown, trial
- **T2**: church, abuse, bishop, child, catholic, gay, pope, school, christian, priest, cardinal
- **T3**: council, people, government, local, housing, home, house, property, city, plan, authority
- **T4**: clinton, party, trump, climate, poll, vote, plaid, election, debate, change, candidate, campaign
- **T5**: country, growth, report, business, export, fall, bank, security, economy, rise, global, inflation
- **T6**: hospital, patient, trust, nhs, people, care, health, service, staff, report, review, system, child
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
The text was extracted from the HTML of the webpage. No further processing was done.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
The copyright license of the data allows reusing it for this purpose.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
yes/very likely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
The language and content of the data is focused on news and language in the UK and as such not representative of the speakers world-wide. Existing selection biases of the BBC exist in this dataset.
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yhavinga/mc4_nl_cleaned | 2022-12-16T09:24:34.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"multilinguality:en-nl",
"source_datasets:extended",
"language:nl",
"language:en",
"license:odc-by",
"arxiv:1910.10683",
"region:us"
] | yhavinga | A thoroughly cleaned version of the Dutch portion of the multilingual
colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning
detailed in the repository README file. | @article{JMLR:v21:20-074,
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 = {Journal of Machine Learning Research},
year = {2020},
volume = {21},
number = {140},
pages = {1-67},
url = {http://jmlr.org/papers/v21/20-074.html}
} | 7 | 175 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- nl
- en
license:
- odc-by
multilinguality:
- monolingual
- en-nl
size_categories:
micro:
- 120k
tiny:
- 1M<n<10M
small:
- 10M<n<100M
medium:
- 10M<n<100M
large:
- 10M<n<100M
full:
- 100M<n<1B
source_datasets:
- extended
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: mc4
pretty_name: mC4_nl_cleaned
---
# Dataset Card for Clean Dutch mC4
## Table of Contents
- [Dataset Card for Clean](#dataset-card-for-mc4)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Preprocessing](#preprocessing)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [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)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Original Homepage:** [HF Hub](https://huggingface.co/datasets/allenai/c4)
- **Paper:** [ArXiv](https://arxiv.org/abs/1910.10683)
### Dataset Summary
A cleaned version (151GB) of the Dutch part (277GB) of the C4 multilingual dataset (mC4).
While this dataset is monolingual, it is possible to download `en-nl` interleaved data, see the Dataset Config section below.
Based on the [Common Crawl dataset](https://commoncrawl.org).
The original version was prepared by [AllenAI](https://allenai.org/), hosted at the address [https://huggingface.co/datasets/allenai/c4](https://huggingface.co/datasets/allenai/c4).
### Preprocessing
The Dutch portion of mC4 was cleaned in a similar fashion as the English cleaned C4 version.
See [GitLab](https://gitlab.com/yhavinga/c4nlpreproc) for details.
In summary, the preprocessing procedure includes:
- Removing documents containing words from a selection of the [Dutch and English List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words).
- Removing sentences containing:
- Less than 3 words.
- A word longer than 250 characters.
- An end symbol not matching end-of-sentence punctuation.
- Strings associated to javascript code (e.g. `{`), lorem ipsum, policy information in Dutch or English.
- Removing documents (after sentence filtering):
- Containing less than 5 sentences.
- Containing less than 500 or more than 50'000 characters.
- Not identified as prevalently Dutch by the `LangDetect` package.
Using parallel processing with 96 CPU cores on a TPUv3 via Google Cloud to perform the complete clean of all the original Dutch
shards of mC4 (1024 of ~220Mb train, 4 of ~24Mb validation) required roughly 10 hours due to the demanding steps of sentence
tokenization and language detection. The total size of compressed `.json.gz` files is roughly halved after the procedure.
## Dataset Structure
### Data Instances
An example from the dataset:
```
{
'timestamp': '2019-02-22T15:37:25Z',
'url': 'https://ondernemingen.bnpparibasfortis.be/nl/artikel?n=vijf-gouden-tips-voor-succesvol-zaken-doen-met-japan',
'text': 'Japanse bedrijven zijn niet alleen hondstrouw aan hun leveranciers , ze betalen ook nog eens erg stipt. Alleen is het niet zo makkelijk er een voet tussen de deur te krijgen. Met de volgende tips hebt u alvast een streepje voor.\nIn Japan draait alles om vertrouwen. Neem voldoende tijd om een relatie op te bouwen.Aarzel niet om tijdig een lokale vertrouwenspersoon in te schakelen.\nJapan is een erg competitieve markt.Kwaliteit en prijs zijn erg belangrijk, u zult dus het beste van uzelf moeten geven. Gelukkig is de beloning groot. Japanse zakenlui zijn loyaal en betalen stipt!\nJapanners houden er eigenzinnige eisen op na. Kom dus niet aanzetten met uw standaardproducten voor de Europese markt. Zo moet een producent van diepvriesfrieten bijvoorbeeld perfect identieke frietjes kunnen leveren in mini- verpakkingen. Het goede nieuws is dat Japanners voor kwaliteit graag diep in hun buidel tasten.\nEn u dacht dat Europa lijdt aan reglementitis? Japanners kennen er ook wat van. Tal van voorschriften zeggen wat je wel en niet mag doen. Gelukkig zijn de regels helder geformuleerd.\nHet gebruik van het Engels is niet echt ingeburgerd in Japan. Betrek een tolk bij uw onderhandelingen en zorg voor correcte vertalingen van handleidingen of softwareprogramma’s.'
}
```
### Data Fields
The data contains the following fields:
- `url`: url of the source as a string
- `text`: text content as a string
- `timestamp`: timestamp of extraction as a string
### Data Configs
To build mC4, the original authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages.
For Dutch, the whole corpus of scraped text was divided in `1032` jsonl files, `1024` for training following
the naming style `c4-nl-cleaned.tfrecord-0XXXX-of-01024.json.gz` and 4 for validation following the
naming style `c4-nl-cleaned.tfrecord-0000X-of-00004.json.gz`. The full set of pre-processed files takes roughly 208GB of disk space to download with Git LFS.
For ease of use under different storage capacities, the following incremental configs are available: (note: files on disk are compressed)
| config | train size (docs, words, download + preproc disk space) | validation size |
|:-------|--------------------------------------------------------:|----------------:|
| micro | 125k docs, 23M words (<1GB) | 16k docs |
| tiny | 6M docs, 2B words (6 GB + 15 GB) | 16k docs |
| small | 15M docs, 6B words (14 GB + 36 GB) | 16k docs |
| medium | 31M docs, 12B words (28 GB + 72 GB) | 32k docs |
| large | 47M docs, 19B words (42 GB + 108 GB) | 48k docs |
| full | 64M docs, 25B words (58 GB + 148 GB) | 64k docs |
For each config above there also exists a config `<name>_en_nl` that interleaves `nl` and `en` examples from the cleaned
`en` variant of C4.
You can load any config like this:
```python
from datasets import load_dataset
datasets = load_dataset('yhavinga/mc4_nl_cleaned', 'tiny', streaming=True)
print(datasets)
```
This will print
```
DatasetDict({
train: Dataset({
features: ['text', 'timestamp', 'url'],
num_rows: 6303893
})
validation: Dataset({
features: ['text', 'timestamp', 'url'],
num_rows: 16189
})
})
```
Since the configs are quite large, you may want to traverse them using the streaming mode available starting from — Datasets v1.9.0:
```python
from datasets import load_dataset
mc4_nl_full_stream = load_dataset('yhavinga/mc4_nl_cleaned', "full", split='train', streaming=True)
print(next(iter(mc4_nl_full_stream))) # Prints the example presented above
```
## Dataset Creation
Refer to the original paper for more considerations regarding the choice of sources and the scraping process for creating `mC4`.
## Considerations for Using the Data
### Social Impact of Dataset
With more than 151GB (58GB compressed) of cleaned Dutch text and more than 23B estimated words, this is by far the largest available cleaned corpus for the Dutch language.
The second largest dataset available is [OSCAR](https://oscar-corpus.com/), which is only 39GB in size for its deduplicated variant, and contains vulgarity.
Using this corpus for training language models with adequate computational resources will allow researchers to reach parity with the performances observed for the English language.
This can in turn have important repercussions for the development of commercial language technology applications for the Dutch language.
### Discussion of Biases
Despite the cleaning procedure aimed at removing vulgarity and profanity, it must be considered that model trained on this scraped corpus will
inevitably reflect biases present in blog articles and comments on the Internet.
This makes the corpus especially interesting in the context of studying data biases and how to limit their impacts.
## Additional Information
### Licensing Information
AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.
### Citation Information
```
@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},
}
```
### Contributions
Thanks to [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com), [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for
providing the `cleaned_it_mc4` example that shows how upload a dataset to the Huggingface hub.
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severo/flores_101 | 2022-10-27T08:37:36.000Z | [
"task_categories:text-generation",
"task_categories:translation",
"annotations_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
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"language:yo",
"language:zu",
"license:cc-by-sa-4.0",
"conditional-text-generation",
"arxiv:2106.03193",
"region:us"
] | severo | One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the
lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource
languages, consider only restricted domains, or are low quality because they are constructed using
semi-automatic procedures. In this work, we introduce the FLORES evaluation benchmark, consisting of 3001
sentences extracted from English Wikipedia and covering a variety of different topics and domains.
These sentences have been translated in 101 languages by professional translators through a carefully
controlled process. The resulting dataset enables better assessment of model quality on the long tail of
low-resource languages, including the evaluation of many-to-many multilingual translation systems, as all
translations are multilingually aligned. By publicly releasing such a high-quality and high-coverage dataset,
we hope to foster progress in the machine translation community and beyond. | @inproceedings{,
title={The {FLORES}-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation},
author={
Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and
Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\'{a}n, Francisco and Fan, Angela
},
year={2021}
} | 0 | 175 | 2023-06-20T21:40:23 | ---
annotations_creators:
- found
language_creators:
- expert-generated
language:
- af
- am
- ar
- hy
- as
- ast
- az
- be
- bn
- bs
- bg
- my
- ca
- ceb
- zho
- hr
- cs
- da
- nl
- en
- et
- tl
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- fr
- ff
- gl
- lg
- ka
- de
- el
- gu
- ha
- he
- hi
- hu
- is
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- id
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- it
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- ml
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- mi
- mr
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- ne
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- 'no'
- ny
- oc
- or
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- pt
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- sr
- sn
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- tg
- ta
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- th
- tr
- uk
- umb
- ur
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- vi
- cy
- wo
- xh
- yo
- zu
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
- translation
size_categories:
- unknown
source_datasets:
- extended|flores
task_categories:
- text-generation
- translation
task_ids: []
paperswithcode_id: flores
pretty_name: flores101
tags:
- conditional-text-generation
---
# Dataset Card for Flores 101
## Table of Contents
- [Dataset Card for Flores 101](#dataset-card-for-flores-101)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Home:** [WMT](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html)
- **Repository:** [Github](https://github.com/facebookresearch/flores)
- **Blogpost:** [FAIR](https://ai.facebook.com/blog/the-flores-101-data-set-helping-build-better-translation-systems-around-the-world)
- **Paper:** [Arxiv](https://arxiv.org/abs/2106.03193)
- **Point of Contact:** [flores@fb.com](mailto:flores@fb.com)
- **Leaderboard** [Dynabench](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL))
### Dataset Summary
FLORES is a benchmark dataset for machine translation between English and low-resource languages.
Abstract from the original paper:
> One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages, consider only restricted domains, or are low quality because they are constructed using semi-automatic procedures. In this work, we introduce the FLORES evaluation benchmark, consisting of 3001 sentences extracted from English Wikipedia and covering a variety of different topics and domains. These sentences have been translated in 101 languages by professional translators through a carefully controlled process. The resulting dataset enables better assessment of model quality on the long tail of low-resource languages, including the evaluation of many-to-many multilingual translation systems, as all translations are multilingually aligned. By publicly releasing such a high-quality and high-coverage dataset, we hope to foster progress in the machine translation community and beyond.
**Disclaimer**: *The Flores-101 dataset is hosted by the Facebook and licensed under the [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/).
### Supported Tasks and Leaderboards
#### Multilingual Machine Translation
Refer to the [Dynabench leaderboard](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL)) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on [Large-Scale Multilingual Machine Translation](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html).
### Languages
The dataset contains parallel sentences for 101 languages, as mentioned in the original [Github](https://github.com/facebookresearch/flores/blob/master/README.md) page for the project. Languages are identified with the ISO 639-3 code (e.g. `eng`, `fra`, `rus`) as in the original dataset.
**New:** Use the configuration `all` to access the full set of parallel sentences for all the available languages in a single command.
## Dataset Structure
### Data Instances
A sample from the `dev` split for the Russian language (`rus` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.
```python
{
'id': 1,
'sentence': 'В понедельник ученые из Медицинской школы Стэнфордского университета объявили об изобретении нового диагностического инструмента, который может сортировать клетки по их типу; это маленький чип, который можно напечатать, используя стандартный струйный принтер примерно за 1 цент США.',
'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet',
'domain': 'wikinews',
'topic': 'health',
'has_image': 0,
'has_hyperlink': 0
}
```
The text is provided as-in the original dataset, without further preprocessing or tokenization.
### Data Fields
- `id`: Row number for the data entry, starting at 1.
- `sentence`: The full sentence in the specific language.
- `URL`: The URL for the English article from which the sentence was extracted.
- `domain`: The domain of the sentence.
- `topic`: The topic of the sentence.
- `has_image`: Whether the original article contains an image.
- `has_hyperlink`: Whether the sentence contains a hyperlink.
### Data Splits
| config| `dev`| `devtest`|
|-----------------:|-----:|---------:|
|all configurations| 997| 1012:|
### Dataset Creation
Please refer to the original article [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://arxiv.org/abs/2106.03193) for additional information on dataset creation.
## Additional Information
### Dataset Curators
The original authors of FLORES-101 are the curators of the original dataset. For problems or updates on this 🤗 Datasets version, please contact [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com).
### Licensing Information
Licensed with Creative Commons Attribution Share Alike 4.0. License available [here](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
Please cite the authors if you use these corpora in your work:
```bibtex
@inproceedings{flores101,
title={The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation},
author={Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\'{a}n, Francisco and Fan, Angela},
journal={arXiv preprint arXiv:2106.03193},
year={2021}
}
``` | 6,979 | [
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proto_qa | 2022-11-03T16:31:01.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2005.00771",
"region:us"
] | null | This dataset is for studying computational models trained to reason about prototypical situations. Using deterministic filtering a sampling from a larger set of all transcriptions was built. It contains 9789 instances where each instance represents a survey question from Family Feud game. Each instance exactly is a question, a set of answers, and a count associated with each answer.
Each line is a json dictionary, in which:
1. question - contains the question (in original and a normalized form)
2. answerstrings - contains the original answers provided by survey respondents (when available), along with the counts for each string. Because the FamilyFeud data has only cluster names rather than strings, those cluster names are included with 0 weight.
3. answer-clusters - lists clusters, with the count of each cluster and the strings included in that cluster. Each cluster is given a unique ID that can be linked to in the assessment files. | @InProceedings{huggingface:dataset,
title = {ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning},
authors={Michael Boratko, Xiang Lorraine Li, Tim O’Gorman, Rajarshi Das, Dan Le, Andrew McCallum},
year={2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\\url{https://github.com/iesl/protoqa-data}},
} | 1 | 174 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- other
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
- open-domain-qa
paperswithcode_id: protoqa
pretty_name: ProtoQA
dataset_info:
- config_name: proto_qa
features:
- name: normalized-question
dtype: string
- name: question
dtype: string
- name: answer-clusters
sequence:
- name: count
dtype: int32
- name: clusterid
dtype: string
- name: answers
sequence: string
- name: answerstrings
sequence: string
- name: totalcount
dtype: int32
- name: id
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 3943484
num_examples: 8782
- name: validation
num_bytes: 472121
num_examples: 980
download_size: 7352932
dataset_size: 4415605
- config_name: proto_qa_cs
features:
- name: normalized-question
dtype: string
- name: question
dtype: string
- name: answers-cleaned
sequence:
- name: count
dtype: int32
- name: clusterid
dtype: string
- name: answers
sequence: string
- name: answerstrings
sequence: string
- name: totalcount
dtype: int32
- name: id
dtype: string
- name: source
dtype: string
splits:
- name: validation
num_bytes: 84466
num_examples: 52
download_size: 115704
dataset_size: 84466
- config_name: proto_qa_cs_assessments
features:
- name: question
dtype: string
- name: assessments
sequence: string
splits:
- name: validation
num_bytes: 12473
num_examples: 52
download_size: 24755
dataset_size: 12473
---
# 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
- **Interactive Demo:** [Interactive demo](http://protoqa.com)
- **Repository:** [proto_qa repository](https://github.com/iesl/protoqa-data)
- **Paper:** [proto_qa paper](https://arxiv.org/pdf/2005.00771.pdf)
- **Point of Contact:** [Michael Boratko](mailto:mboratko@cs.umass.edu)
[Xiang Lorraine Li](mailto:xiangl@cs.umass.edu)
[Tim O’Gorman](mailto:togorman@cs.umass.edu)
[Rajarshi Das](mailto:rajarshi@cs.umass.edu)
[Dan Le](mailto:dhle@cs.umass.edu)
[Andrew McCallum](mailto:mccallum@cs.umass.edu)
### Dataset Summary
This dataset is for studying computational models trained to reason about prototypical situations. It is anticipated that still would not lead to usage in a downstream task, but as a way of studying the knowledge (and biases) of prototypical situations already contained in pre-trained models. The data it is partially based on (Family Feud).
Using deterministic filtering a sampling from a larger set of all transcriptions was built. Scraped data was acquired through fan transcriptions at [family feud](https://www.familyfeudinfo.com) and [family feud friends](http://familyfeudfriends.arjdesigns.com/); crowdsourced data was acquired with FigureEight (now Appen)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English
## Dataset Structure
### Data Instances
**What do the instances that comprise the dataset represent?**<br>
Each represents a survey question from Family Feud game and reported answer clusters
**How many instances are there in total?**<br>
9789 instances
**What data does each instance consist of?**<br>
Each instance is a question, a set of answers, and a count associated with each answer.
### Data Fields
**Data Files**<br>
Each line is a json dictionary, in which:<br>
**question** contains the question (in original and a normalized form)<br>
**answerstrings** contains the original answers provided by survey respondents (when available), along with the counts for each string. Because the FamilyFeud data has only cluster names rather than strings, those cluster names are included with 0 weight.<br>
**answer-clusters** list of clusters, with the count of each cluster and the strings included in that cluster. Each cluster is given a unique ID that can be linked to in the assessment files.
The simplified configuration includes:
- `question`: contains the original question
- `normalized-question`: contains the question in normalized form
- `totalcount`: unique identifier of the comment (can be used to look up the entry in the raw dataset)
- `id`: unique identifier of the commen
- `source`: unique identifier of the commen
- `answerstrings`: unique identifier of the commen
- `answer-clusters | answers-cleaned`: list clusters of:
* `clusterid`: Each cluster is given a unique ID that can be linked to in the assessment files
* `count`: the count of each cluster
* `answers`: the strings included in that cluster
In addition to the above, there is crowdsourced assessments file. The config "proto_qa_cs_assessments" provides mappings from additional human and model answers to clusters, to evaluate different assessment methods.
**Assessment files**<br>
The file **data/dev/crowdsource_dev.assessments.jsonl** contains mappings from additional human and model answers to clusters, to evaluate different assessment methods.
Each line contains:<br>
* `question`: contains the ID of the question
* `assessments`: maps individual strings to one of three options, either the answer cluster id, "invalid" if the answer is judged to be bad, or "valid_new_cluster" if the answer is valid but does not match any existing clusters.
### Data Splits
* proto_qa `Train` : 8781 instances for training or fine-tuning scraped from Family Feud fan sites (see paper). Scraped data has answer clusters with sizes, but only has a single string per cluster (corresponding to the original cluster name
* proto_qa `Validation` : 979 instances sampled from the same Family Feud data, for use in model validation and development.
* proto_qa_cs `Validation` :: 51 questions collected with exhaustive answer collection and manual clustering, matching the details of the eval test set (roughly 100 human answers per question)
**data/dev/crowdsource_dev.assessments.jsonl**: assessment file (format described above) for study of assessment methods.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
**How was the data associated with each instance acquired?**<br>
Scraped data was acquired through fan transcriptions at https://www.familyfeudinfo.com and http://familyfeudfriends.arjdesigns.com/ ; crowdsourced data was acquired with FigureEight (now Appen)
**If the dataset is a sample from a larger set, what was the sampling strategy?**<br>
Deterministic filtering was used (noted elsewhere), but no probabilistic sampling was used.
**Who was involved in the data collection process (e.g., students,crowdworkers , contractors) and how were they compensated?**<br>
Crowdworkers were used in the evalaution dataset. Time per task was calculated and per-task cost was set to attempt to provide a living wage
**Over what timeframe was the data collected?**<br>
Crowdsource answers were collected between Fall of 2018 and Spring of 2019. Scraped data covers question-answer pairs collected since the origin of the show in 1976
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
**Was any preprocessing/cleaning/labeling of the data done?**<br>
Obvious typos in the crowdsourced answer set were corrected
#### Who are the annotators?
The original question-answer pairs were generated by surveys of US English-speakers in a period from 1976 to present day. Crowd-sourced evaluation was constrained geographically to US English speakers but not otherwise constrained. Additional demographic data was not collected.
### Personal and Sensitive Information
**Does the dataset contain data that might be considered sensitive in any way?**<br>
As the questions address prototypical/stereotypical activities, models trained on more offensive material (such as large language models) may provide offensive answers to such questions. While we had found a few questions which we worried would actually encourage models to provide offensive answers, we cannot guarantee that the data is clean of such questions. Even a perfectly innocent version of this dataset would be encouraging models to express generalizations about situations, and therefore may provoke offensive material that is oontained in language models
**Does the dataset contain data that might be considered confidential?**<br>
The data does not concern individuals and thus does not contain any information to identify persons. Crowdsourced answers do not provide any user identifiers.
## Considerations for Using the Data
### Social Impact of Dataset
**Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?**<br>
Not egregiously so (questions are all designed to be shown on television or replications thereof),
### Discussion of Biases
**Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?**
<br>All original questions were written with US television audiences in mind, and therefore characterize prototypical situations with a specific lens. Any usages which deploy this to actually model prototypical situations globally will carry that bias.
**Are there tasks for which the dataset should not be used?**
<br>We caution regarding free-form use of this dataset for interactive "commonsense question answering" purposes without more study of the biases and stereotypes learned by such models.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The listed authors are maintaining/supporting the dataset. They pledge to help support issues, but cannot guarantee long-term support
### Licensing Information
The Proto_qa dataset is licensed under the [Creative Commons Attribution 4.0 International](https://github.com/iesl/protoqa-data/blob/master/LICENSE)
### Citation Information
```
@InProceedings{
huggingface:dataset,
title = {ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning},
authors = {Michael Boratko, Xiang Lorraine Li, Tim O’Gorman, Rajarshi Das, Dan Le, Andrew McCallum},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {https://github.com/iesl/protoqa-data},
}
```
### Contributions
Thanks to [@bpatidar](https://github.com/bpatidar) for adding this dataset. | 11,882 | [
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wiki_movies | 2022-11-18T22:00:27.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"arxiv:1606.03126",
"region:us"
] | null | The WikiMovies dataset consists of roughly 100k (templated) questions over 75k entities based on questions with answers in the open movie database (OMDb). | @misc{miller2016keyvalue,
title={Key-Value Memory Networks for Directly Reading Documents},
author={Alexander Miller and Adam Fisch and Jesse Dodge and Amir-Hossein Karimi and Antoine Bordes and Jason Weston},
year={2016},
eprint={1606.03126},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 3 | 174 | 2022-03-02T23:29:22 | ---
pretty_name: WikiMovies
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
paperswithcode_id: wikimovies
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 7274490
num_examples: 96185
- name: test
num_bytes: 755258
num_examples: 9952
- name: validation
num_bytes: 754755
num_examples: 10000
download_size: 57070041
dataset_size: 8784503
---
# Dataset Card for WikiMovies
## 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:** [WikiMovies Homepage](https://research.fb.com/downloads/babi/)
- **Repository:**
- **Paper:** [Key-Value Memory Networks for Directly Reading Documents](https://arxiv.org/pdf/1606.03126.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The WikiMovies dataset consists of roughly 100k (templated) questions over 75k entitiesbased on questions with answers in the open movie database (OMDb). It is the QA part of the Movie Dialog dataset.
### Supported Tasks and Leaderboards
- Question Answering
### Languages
The text in the dataset is written in English.
## Dataset Structure
### Data Instances
The raw data consists of question answer pairs separated by a tab. Here are 3 examples:
```buildoutcfg
1 what does Grégoire Colin appear in? Before the Rain
1 Joe Thomas appears in which movies? The Inbetweeners Movie, The Inbetweeners 2
1 what films did Michelle Trachtenberg star in? Inspector Gadget, Black Christmas, Ice Princess, Harriet the Spy, The Scribbler
```
It is unclear what the `1` is for at the beginning of each line, but it has been removed in the `Dataset` object.
### Data Fields
Here is an example of the raw data ingested by `Datasets`:
```buildoutcfg
{
'answer': 'Before the Rain',
'question': 'what does Grégoire Colin appear in?'
}
```
`answer`: a string containing the answer to a corresponding question.
`question`: a string containing the relevant question.
### Data Splits
The data is split into train, test, and dev sets. The split sizes are as follows:
| wiki-entities_qa_* | n examples|
| ----- | ---- |
| train.txt | 96185 |
| dev.txt | 10000 |
| test.txt | 9952 |
## Dataset Creation
### Curation Rationale
WikiMovies was built with the following goals in mind: (i) machine learning techniques should have ample training examples for learning; and (ii) one can analyze easily the performance of different representations of knowledge and break down the results by question type. The datasetcan be downloaded fromhttp://fb.ai/babi
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{miller2016keyvalue,
title={Key-Value Memory Networks for Directly Reading Documents},
author={Alexander Miller and Adam Fisch and Jesse Dodge and Amir-Hossein Karimi and Antoine Bordes and Jason Weston},
year={2016},
eprint={1606.03126},
archivePrefix={arXiv},
primaryClass={cs.CL}
```
### Contributions
Thanks to [@aclifton314](https://github.com/aclifton314) for adding this dataset. | 5,004 | [
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asi/wikitext_fr | 2022-10-21T16:23:07.000Z | [
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:fr",
"license:cc-by-sa-4.0",
"arxiv:1609.07843",
"region:us"
] | asi | Wikitext-fr language modeling dataset consists of over 70 million tokens
extracted from the set of french Wikipedia articles that are classified as
"quality articles" or "good articles.". The aim is to replicate the English
benchmark. | @inproceedings{simoulin:hal-03265900,
TITLE = {{Un mod{\`e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le \_\_\_\_\_\_ fran{\c c}ais}},
AUTHOR = {Simoulin, Antoine and Crabb{\'e}, Benoit},
URL = {https://hal.archives-ouvertes.fr/hal-03265900},
BOOKTITLE = {{Traitement Automatique des Langues Naturelles}},
ADDRESS = {Lille, France},
EDITOR = {Denis, Pascal and Grabar, Natalia and Fraisse, Amel and Cardon, R{\'e}mi and Jacquemin, Bernard and Kergosien, Eric and Balvet, Antonio},
PUBLISHER = {{ATALA}},
PAGES = {246-255},
YEAR = {2021},
KEYWORDS = {fran{\c c}ais. ; GPT ; G{\'e}n{\'e}ratif ; Transformer ; Pr{\'e}-entra{\^i}n{\'e}},
PDF = {https://hal.archives-ouvertes.fr/hal-03265900/file/7.pdf},
HAL_ID = {hal-03265900},
HAL_VERSION = {v1},
} | 4 | 174 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- fr
language_bcp47:
- fr-FR
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Wikitext-fr
size_categories:
- unknown
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- language-modeling
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/AntoineSimoulin/gpt-fr](https://github.com/AntoineSimoulin/gpt-fr)
- **Paper:** [https://aclanthology.org/2021.jeptalnrecital-taln.24.pdf](https://aclanthology.org/2021.jeptalnrecital-taln.24.pdf)
### Dataset Summary
Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles". It is designed to mirror the english benchmark from Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016.
[Pointer Sentinel Mixture Models](https://arxiv.org/abs/1609.07843) The dataset is available under the [Creative Commons Attribution-ShareAlike License](https://creativecommons.org/licenses/by-sa/4.0/)
### Supported Tasks and Leaderboards
- `language-modeling`: The dataset can be used to evaluate the generation abilites of a model. Success on this task is typically measured by achieving a *low* perplexity. The ([model name](https://huggingface.co/asi/gpt-fr-cased-base) currently achieves 12.9.
### Languages
The dataset is in French.
## Dataset Structure
### Data Instances
The dataset consists in the agregation of paragraphs from wikipedia articles.
```
{
'paragraph': ...,
...
}
```
### Data Fields
- `paragraph`: This is a paragraph from the original wikipedia article.
### Data Splits
The dataset is splited into a train/valid/test split.
| | Tain (35) | Train (72) | Valid | Test |
| ----- | ------ | ----- | ---- | ---- |
| Number of Documents | 2 126 | 5 902 | 60 | 60 |
| Number of tokens | 351 66 | 72 961 | 896 | 897 |
| Vocabulary size | 137 589 | 205 403 | | |
| Out of Vocabulary | 0.8% | 1.2% | | |
## Dataset Creation
### Curation Rationale
The dataset is created to evaluate French models with similart criteria than English.s
### Source Data
Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles".
We did not apply specific pre-treatments as transformers models might use a dedicated tokenization.s
#### Initial Data Collection and Normalization
We used the Wikipedia API to collect the articles since cleaning Wikipedia articles from dumps is not a trivial task.
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
The dataset is available under the [Creative Commons Attribution-ShareAlike License](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@inproceedings{simoulin:hal-03265900,
TITLE = {{Un mod{\`e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le \_\_\_\_\_\_ fran{\c c}ais}},
AUTHOR = {Simoulin, Antoine and Crabb{\'e}, Benoit},
URL = {https://hal.archives-ouvertes.fr/hal-03265900},
BOOKTITLE = {{Traitement Automatique des Langues Naturelles}},
ADDRESS = {Lille, France},
EDITOR = {Denis, Pascal and Grabar, Natalia and Fraisse, Amel and Cardon, R{\'e}mi and Jacquemin, Bernard and Kergosien, Eric and Balvet, Antonio},
PUBLISHER = {{ATALA}},
PAGES = {246-255},
YEAR = {2021},
KEYWORDS = {fran{\c c}ais. ; GPT ; G{\'e}n{\'e}ratif ; Transformer ; Pr{\'e}-entra{\^i}n{\'e}},
PDF = {https://hal.archives-ouvertes.fr/hal-03265900/file/7.pdf},
HAL_ID = {hal-03265900},
HAL_VERSION = {v1},
}
```
### Contributions
Thanks to [@AntoineSimoulin](https://github.com/AntoineSimoulin) for adding this dataset. | 5,598 | [
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] |
mozilla-foundation/common_voice_9_0 | 2023-07-29T16:00:12.000Z | [
"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
} | 11 | 174 | 2022-04-29T16:49:21 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
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as:
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bas:
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be:
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ga-IE:
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source_datasets:
- extended|common_voice
paperswithcode_id: common-voice
pretty_name: Common Voice Corpus 9.0
language_bcp47:
- ab
- ar
- as
- az
- ba
- bas
- be
- bg
- bn
- br
- ca
- ckb
- cnh
- cs
- cv
- cy
- da
- de
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- el
- en
- eo
- es
- et
- eu
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- fi
- fr
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- gn
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- hi
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- hu
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- it
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- or
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- pt
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- rm-vallader
- ro
- ru
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- sat
- sk
- sl
- sr
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- sw
- ta
- th
- tig
- tok
- tr
- tt
- ug
- uk
- ur
- uz
- vi
- vot
- yue
- zh-CN
- zh-HK
- zh-TW
extra_gated_prompt: By clicking on “Access repository” below, you also agree to not
attempt to determine the identity of speakers in the Common Voice dataset.
task_categories:
- automatic-speech-recognition
---
# Dataset Card for Common Voice Corpus 9.0
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Anton Lozhkov](mailto:anton@huggingface.co)
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 20217 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 14973 validated hours in 93 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
### Languages
```
Abkhaz, Arabic, Armenian, Assamese, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Norwegian Nynorsk, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Toki Pona, Turkish, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_9_0", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
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] |
GATE-engine/describable_textures | 2023-06-05T17:13:02.000Z | [
"region:us"
] | GATE-engine | null | null | 0 | 174 | 2023-06-04T23:57:38 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: train
num_bytes: 350355304.0
num_examples: 3960
- name: validation
num_bytes: 72331220.0
num_examples: 840
- name: test
num_bytes: 73428430.0
num_examples: 840
download_size: 0
dataset_size: 496114954.0
---
# Dataset Card for "describable_textures"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 529 | [
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GATE-engine/omniglot | 2023-06-05T18:58:27.000Z | [
"region:us"
] | GATE-engine | null | null | 0 | 174 | 2023-06-05T18:13:32 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: full
num_bytes: 11924141.5
num_examples: 32460
download_size: 10520482
dataset_size: 11924141.5
---
# Dataset Card for "omniglot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 390 | [
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] |
C-MTEB/BQ | 2023-07-28T13:52:50.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 174 | 2023-07-28T13:52:31 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: int32
splits:
- name: train
num_bytes: 8156338
num_examples: 100000
- name: validation
num_bytes: 812244
num_examples: 10000
- name: test
num_bytes: 815362
num_examples: 10000
download_size: 5588828
dataset_size: 9783944
---
# Dataset Card for "BQ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 724 | [
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dansbecker/hackernews_hiring_posts | 2021-12-07T13:46:20.000Z | [
"region:us"
] | dansbecker | null | null | 0 | 173 | 2022-03-02T23:29:22 | This dataset contains postings and comments from the following recurring threads on [Hacker News](http://news.ycombinator.com/)
1. Ask HN: Who is hiring?
2. Ask HN: Who wants to be hired?
3. Freelancer? Seeking freelancer?
These post types are stored in datasets called `hiring`, `wants_to_be_hired` and `freelancer` respectively.
Each type of posting has occurred on a regular basis for several years. You can identify when each comment/listing was added through the CommentTime field. The `ParentTitle` also indicates the date of the parent thread in text (e.g. `Ask HN: Who is hiring? (March 2021)`)
This dataset is not programmatically reproducible from source because it was uploaded as an experiment with HF datasets. The raw data was created by querying the public table `bigquery-public-data.hacker_news.full` in Google BigQuery.
Email addresses have been redacted from the dataset.
If this dataset is interesting/useful, I (Dan Becker) will look into improving reproducibility and other general clean-up.
This dataset may be useful for finding trends in tech and tech job listings. | 1,098 | [
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] |
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