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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
indonlp/NusaX-MT | 2023-01-24T17:21:03.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ace",
"language:ban",
"language:bjn",
"language:bug",
"language:en",
"language:id",
"language:jv",
"language:mad",
"language:min",
"language:nij",
"language:su",
"language:bbc",
"license:cc-by-sa-4.0",
"arxiv:2205.15960",
"region:us"
] | indonlp | NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language. | @misc{winata2022nusax,
title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya,
Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony,
Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo,
Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau,
Jey Han and Sennrich, Rico and Ruder, Sebastian},
year={2022},
eprint={2205.15960},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 5 | 320 | 2023-01-24T17:05:31 | ---
pretty_name: NusaX-MT
annotations_creators:
- expert-generated
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
language:
- ace
- ban
- bjn
- bug
- en
- id
- jv
- mad
- min
- nij
- su
- bbc
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
dataset_info:
features:
- name: id
dtype: string
- name: text_1
dtype: string
- name: text_2
dtype: string
- name: text_1_lang
dtype: string
- name: text_2_lang
dtype: string
---
# Dataset Card for NusaX-MT
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [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)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/mt)
- **Paper:** [EACL 2022](https://arxiv.org/abs/2205.15960)
- **Point of Contact:** [GitHub](https://github.com/IndoNLP/nusax/tree/main/datasets/mt)
### Dataset Summary
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language.
### Supported Tasks and Leaderboards
- Machine translation for Indonesian languages
### Languages
All possible pairs of the following:
- ace: acehnese,
- ban: balinese,
- bjn: banjarese,
- bug: buginese,
- eng: english,
- ind: indonesian,
- jav: javanese,
- mad: madurese,
- min: minangkabau,
- nij: ngaju,
- sun: sundanese,
- bbc: toba_batak,
## Dataset Creation
### Curation Rationale
There is a shortage of NLP research and resources for the Indonesian languages, despite the country having over 700 languages. With this in mind, we have created this dataset to support future research for the underrepresented languages in Indonesia.
### Source Data
#### Initial Data Collection and Normalization
NusaX-MT is a dataset for machine translation in Indonesian langauges that has been expertly translated by native speakers.
#### Who are the source language producers?
The data was produced by humans (native speakers).
### Annotations
#### Annotation process
NusaX-MT is derived from SmSA, which is the biggest publicly available dataset for Indonesian sentiment analysis. It comprises of comments and reviews from multiple online platforms. To ensure the quality of our dataset, we have filtered it by removing any abusive language and personally identifying information by manually reviewing all sentences. To ensure balance in the label distribution, we randomly picked 1,000 samples through stratified sampling and then translated them to the corresponding languages.
#### Who are the annotators?
Native speakers of both Indonesian and the corresponding languages.
Annotators were compensated based on the number of translated samples.
### Personal and Sensitive Information
Personal information is removed.
## 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
NusaX is created from review text. These data sources may contain some bias.
### Other Known Limitations
No other known limitations
## Additional Information
### Licensing Information
CC-BY-SA 4.0.
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Please contact authors for any information on the dataset.
### Citation Information
```
@misc{winata2022nusax,
title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya,
Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony,
Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo,
Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau,
Jey Han and Sennrich, Rico and Ruder, Sebastian},
year={2022},
eprint={2205.15960},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@afaji](https://github.com/afaji) for adding this dataset.
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result-kand2-sdxl-wuerst-karlo/9a272529 | 2023-10-04T08:54:53.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 320 | 2023-10-04T08:54:52 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 246
num_examples: 10
download_size: 1437
dataset_size: 246
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "9a272529"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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TJUNLP/M3KE | 2023-06-19T04:07:29.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:multiple-choice",
"size_categories:10K<n<100K",
"language:zh",
"license:apache-2.0",
"arxiv:2305.10263",
"region:us"
] | TJUNLP | A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models. | @misc{liu2023m3ke,
title={M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models},
author={Chuang Liu and Renren Jin and Yuqi Ren and Linhao Yu and Tianyu Dong and Xiaohan Peng and Shuting Zhang and Jianxiang Peng and Peiyi Zhang and Qingqing Lyu and Xiaowen Su and Qun Liu and Deyi Xiong},
year={2023},
eprint={2305.10263},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 2 | 319 | 2023-06-16T02:42:59 | ---
license: apache-2.0
task_categories:
- text-classification
- question-answering
- multiple-choice
language:
- zh
size_categories:
- 10K<n<100K
arxiv:
- 2305.10263
---
M3KE, or Massive Multi-Level Multi-Subject Knowledge Evaluation, is a benchmark developed to assess the knowledge acquired by large Chinese language models by evaluating their multitask accuracy in both zero- and few-shot settings. The benchmark comprises 20,477 questions spanning 71 tasks. For further information about M3KE, please consult our [paper](https://arxiv.org/abs/2305.10263) or visit our [GitHub](https://github.com/tjunlp-lab/M3KE) page.
## Load the data
```python
from datasets import load_dataset
ds = load_dataset(
path="TJUNLP/M3KE",
name="Computer Programming Language-Natural Sciences-Other"
)
print(ds)
"""
DatasetDict({
test: Dataset({
features: ['id', 'question', 'A', 'B', 'C', 'D', 'answer'],
num_rows: 236
})
dev: Dataset({
features: ['id', 'question', 'A', 'B', 'C', 'D', 'answer'],
num_rows: 5
})
})
"""
print(ds["test"][0])
"""
{'id': 0, 'question': '下面判断正确的是？', 'A': 'char str[10]={"china"}; 等价于 char str[10];str[]="china";', 'B': 'char *s="china"; 等价于 char *s;s="china"; ', 'C': 'char *a="china"; 等价于 char *a;*a="china";', 'D': 'char c[6]="china",d[6]="china"; 等 价 于 char c[6]=d[6]="china"; ', 'answer': ''}
"""
```
```
@misc{liu2023m3ke,
title={M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models},
author={Chuang Liu and Renren Jin and Yuqi Ren and Linhao Yu and Tianyu Dong and Xiaohan Peng and Shuting Zhang and Jianxiang Peng and Peiyi Zhang and Qingqing Lyu and Xiaowen Su and Qun Liu and Deyi Xiong},
year={2023},
eprint={2305.10263},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 1,839 | [
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germeval_14 | 2023-04-05T10:06:39.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"region:us"
] | null | The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties: - The data was sampled from German Wikipedia and News Corpora as a collection of citations. - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens. - The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating embeddings among NEs such as [ORG FC Kickers [LOC Darmstadt]]. | @inproceedings{benikova-etal-2014-nosta,
title = {NoSta-D Named Entity Annotation for German: Guidelines and Dataset},
author = {Benikova, Darina and
Biemann, Chris and
Reznicek, Marc},
booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)},
month = {may},
year = {2014},
address = {Reykjavik, Iceland},
publisher = {European Language Resources Association (ELRA)},
url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/276_Paper.pdf},
pages = {2524--2531},
} | 3 | 318 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: nosta-d-named-entity-annotation-for-german
pretty_name: GermEval14
dataset_info:
features:
- name: id
dtype: string
- name: source
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-LOC
'2': I-LOC
'3': B-LOCderiv
'4': I-LOCderiv
'5': B-LOCpart
'6': I-LOCpart
'7': B-ORG
'8': I-ORG
'9': B-ORGderiv
'10': I-ORGderiv
'11': B-ORGpart
'12': I-ORGpart
'13': B-OTH
'14': I-OTH
'15': B-OTHderiv
'16': I-OTHderiv
'17': B-OTHpart
'18': I-OTHpart
'19': B-PER
'20': I-PER
'21': B-PERderiv
'22': I-PERderiv
'23': B-PERpart
'24': I-PERpart
- name: nested_ner_tags
sequence:
class_label:
names:
'0': O
'1': B-LOC
'2': I-LOC
'3': B-LOCderiv
'4': I-LOCderiv
'5': B-LOCpart
'6': I-LOCpart
'7': B-ORG
'8': I-ORG
'9': B-ORGderiv
'10': I-ORGderiv
'11': B-ORGpart
'12': I-ORGpart
'13': B-OTH
'14': I-OTH
'15': B-OTHderiv
'16': I-OTHderiv
'17': B-OTHpart
'18': I-OTHpart
'19': B-PER
'20': I-PER
'21': B-PERderiv
'22': I-PERderiv
'23': B-PERpart
'24': I-PERpart
config_name: germeval_14
splits:
- name: train
num_bytes: 13816714
num_examples: 24000
- name: validation
num_bytes: 1266974
num_examples: 2200
- name: test
num_bytes: 2943201
num_examples: 5100
download_size: 10288972
dataset_size: 18026889
---
# Dataset Card for "germeval_14"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://sites.google.com/site/germeval2014ner/](https://sites.google.com/site/germeval2014ner/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf](https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf)
- **Point of Contact:** [Darina Benikova](mailto:benikova@aiphes.tu-darmstadt.de)
- **Size of downloaded dataset files:** 10.29 MB
- **Size of the generated dataset:** 18.03 MB
- **Total amount of disk used:** 28.31 MB
### Dataset Summary
The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties: - The data was sampled from German Wikipedia and News Corpora as a collection of citations. - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens. - The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating embeddings among NEs such as [ORG FC Kickers [LOC Darmstadt]].
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
German
## Dataset Structure
### Data Instances
#### germeval_14
- **Size of downloaded dataset files:** 10.29 MB
- **Size of the generated dataset:** 18.03 MB
- **Total amount of disk used:** 28.31 MB
An example of 'train' looks as follows. This example was too long and was cropped:
```json
{
"id": "11",
"ner_tags": [13, 14, 14, 14, 14, 0, 0, 0, 0, 0, 0, 0, 19, 20, 13, 0, 1, 0, 0, 0, 0, 0, 19, 20, 20, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"nested_ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"source": "http://de.wikipedia.org/wiki/Liste_von_Filmen_mit_homosexuellem_Inhalt [2010-01-11] ",
"tokens": "[\"Scenes\", \"of\", \"a\", \"Sexual\", \"Nature\", \"(\", \"GB\", \"2006\", \")\", \"-\", \"Regie\", \":\", \"Ed\", \"Blum\", \"Shortbus\", \"(\", \"USA\", \"2006..."
}
```
### Data Fields
The data fields are the same among all splits.
#### germeval_14
- `id`: a `string` feature.
- `source`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-LOCderiv` (3), `I-LOCderiv` (4).
- `nested_ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-LOCderiv` (3), `I-LOCderiv` (4).
### Data Splits
| name |train|validation|test|
|-----------|----:|---------:|---:|
|germeval_14|24000| 2200|5100|
## 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
[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@inproceedings{benikova-etal-2014-nosta,
title = {NoSta-D Named Entity Annotation for German: Guidelines and Dataset},
author = {Benikova, Darina and
Biemann, Chris and
Reznicek, Marc},
booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)},
month = {may},
year = {2014},
address = {Reykjavik, Iceland},
publisher = {European Language Resources Association (ELRA)},
url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/276_Paper.pdf},
pages = {2524--2531},
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@stefan-it](https://github.com/stefan-it), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. | 8,995 | [
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taeshahn/ko-lima | 2023-06-30T09:21:43.000Z | [
"license:cc-by-nc-sa-4.0",
"arxiv:2305.11206",
"region:us"
] | taeshahn | A high-quality korean dataset for efficient instruction tuning. | @InProceedings{huggingface:dataset,
title = {Ko-LIMA: Korean LIMA Dataset},
author={Hahn, Taeseung},
year={2023}
} | 9 | 318 | 2023-06-13T15:10:24 | ---
license: cc-by-nc-sa-4.0
---
# Dataset Card for Ko-LIMA
## Dataset Description
Ko-LIMA는 Meta에서 공개한 [LIMA: Less Is More for Alignment](https://arxiv.org/abs/2305.11206) (Zhou et al., 2023)의 [학습 데이터](https://huggingface.co/datasets/GAIR/lima)를 한국어로 번역한 데이터셋입니다. 번역에는 [DeepL API](https://www.deepl.com/docs-api)를 활용하였고, SK(주) Tech Collaborative Lab으로부터 비용을 지원받았습니다. 전체 텍스트 중에서 code block이나 수식을 나타내는 특수문자 사이의 텍스트는 원문을 유지하는 형태로 번역을 진행하였으며, `train` 데이터셋 1,030건과 `test` 데이터셋 300건으로 구성된 총 1,330건의 데이터를 활용하실 수 있습니다. 현재 동일한 번역 문장을 `plain`, `vicuna` 두 가지 포멧으로 제공합니다.
데이터셋 관련하여 문의가 있으신 경우 [메일](mailto:taes.hahn@gmail.com)을 통해 연락주세요! 🥰
This is Korean LIMA dataset, which is translated from the [LIMA dataset](https://huggingface.co/datasets/GAIR/lima) that Meta's [LIMA model](https://arxiv.org/abs/2305.11206) (Zhou et al., 2023) was trained on. The translation has proceeded through [DeepL API](https://www.deepl.com/docs-api) with financial support from Tech Collaborative Lab in SK Inc.
Please feel free to contact me if you have any question on the dataset.
I'm best reached via [email](mailto:taes.hahn@gmail.com).
#### Changelog
- [29 June 2023] New format added: `vicuna` format with the same translation is now available.
- [16 June 2023] Enhanced Translation: keep the text enclosed in special characters, e.g. `$` and `` ` ``, intact without translation.
- [14 June 2023] First upload.
### Usage
```python
>>> from datasets import load_dataset
>>> ko_lima = load_dataset('taeshahn/ko-lima', 'plain') # or load_dataset('taeshahn/ko-lima')
>>> ko_lima_vicuna = load_dataset('taeshahn/ko-lima', 'vicuna')
```
```python
>>> ko_lima['train'][1025]
{
'conversations': [
'저는 케냐 출신입니다. 망명을 신청하고 싶은데 비자없이 네덜란드로 망명을 신청하기 위해 여행할 수 있나요? 케냐항공에서 여권을 소지한 경우 스키폴 공항으로 가는 비자없이 비행기에 탑승할 수 있나요?',
'항공사가 탑승을 허용할 가능성은 극히 낮습니다. 네덜란드에 입국하려는 케냐 시민은 비자, 체류 기간에 필요한 충분한 자금 증명, 다음 목적지 입국에 필요한 서류를 소지해야 합니다. 또한 항공사는 케냐에서 출발하는 승객에 대해 특별 조사를 실시해야 합니다:\n\n> 다음 공항에서 네덜란드로 운항하는 항공사:\n\n아부다비(AUH), 아크라(ACC), 바레인(BAH), 베이징 캐피탈(PEK), 보리스필(KBP), 카이로(CAI), 담만(DMM), 다르에스살람(DAR), 두바이(DXB), 엔테베(EBB), 광저우(CAN), 홍콩(HKG), 하마드(DOH), 이스탄불 아타튀르크(IST), 이스탄불 사비하곡첸(SAW), 요하네스버그(JNB), 키갈리(KGL), 킬리만자로(JRO), 쿠알라룸푸르(KUL), 쿠웨이트(KWI), 라고스(LOS), 모스크바 셰레메티예보(SVO), 무스카트(MCT), 나이로비(NB ), 뉴델리(DEL), 상파울루(GRU), 싱가포르(SIN) 및 테헤란(IKA)은 네덜란드 도착 시 적절한 여행 서류가 없어 입국할 수 없는 경우 해당 항공편의 모든 승객의 여행 서류 사본을 제출하셔야 합니다. 사본에는 여권/여행 서류의 데이터 페이지, 비자가 있는 페이지, 출국/통관 스탬프가 있는 페이지(해당되는 경우)가 포함되어야 합니다. 이러한 입국 규정을 준수하지 않을 경우 항공사에 승객 1인당 최대 11,250유로의 벌금이 부과될 수 있습니다.\n\n마지막 문장에서 볼 수 있듯이 항공사는 탑승 전에 여행 서류를 확인해야 하는 강력한 인센티브를 갖게 됩니다.',
'인용된 자료의 출처는 무엇인가요?',
'출처는 항공을 통해 해외로 여행하는 승객의 여권 및 비자 요건과 같은 서류 요건이 포함된 데이터베이스인 Timatic(여행 정보 매뉴얼 자동)에서 가져온 것입니다. 위의 답변은 목적지가 네덜란드이고 귀국/귀국 여행 증빙이 있으며 5일간 체류할 계획이며 케냐 여권의 유효 기간이 약 3년 남은 케냐 시민이 케냐에서 직접 여행하는 시나리오를 기반으로 한 것입니다.'
],
'source': 'multi_turn'
}
```
```python
>>> ko_lima_vicuna['train'][1025]
{
'id': '1026-multi_turn',
'conversations': [
{
'from': '사람',
'value': '저는 케냐 출신입니다. 망명을 신청하고 싶은데 비자없이 네덜란드로 망명을 신청하기 위해 여행할 수 있나요? 케냐항공에서 여권을 소지한 경우 스키폴 공항으로 가는 비자없이 비행기에 탑승할 수 있나요?'
},
{
'from': '어시스턴트',
'value': '항공사가 탑승을 허용할 가능성은 극히 낮습니다. 네덜란드에 입국하려는 케냐 시민은 비자, 체류 기간에 필요한 충분한 자금 증명, 다음 목적지 입국에 필요한 서류를 소지해야 합니다. 또한 항공사는 케냐에서 출발하는 승객에 대해 특별 조사를 실시해야 합니다:\n\n> 다음 공항에서 네덜란드로 운항하는 항공사:\n\n아부다비(AUH), 아크라(ACC), 바레인(BAH), 베이징 캐피탈(PEK), 보리스필(KBP), 카이로(CAI), 담만(DMM), 다르에스살람(DAR), 두바이(DXB), 엔테베(EBB), 광저우(CAN), 홍콩(HKG), 하마드(DOH), 이스탄불 아타튀르크(IST), 이스탄불 사비하곡첸(SAW), 요하네스버그(JNB), 키갈리(KGL), 킬리만자로(JRO), 쿠알라룸푸르(KUL), 쿠웨이트(KWI), 라고스(LOS), 모스크바 셰레메티예보(SVO), 무스카트(MCT), 나이로비(NB ), 뉴델리(DEL), 상파울루(GRU), 싱가포르(SIN) 및 테헤란(IKA)은 네덜란드 도착 시 적절한 여행 서류가 없어 입국할 수 없는 경우 해당 항공편의 모든 승객의 여행 서류 사본을 제출하셔야 합니다. 사본에는 여권/여행 서류의 데이터 페이지, 비자가 있는 페이지, 출국/통관 스탬프가 있는 페이지(해당되는 경우)가 포함되어야 합니다. 이러한 입국 규정을 준수하지 않을 경우 항공사에 승객 1인당 최대 11,250유로의 벌금이 부과될 수 있습니다.\n\n마지막 문장에서 볼 수 있듯이 항공사는 탑승 전에 여행 서류를 확인해야 하는 강력한 인센티브를 갖게 됩니다.'
},
{
'from': '사람',
'value': '인용된 자료의 출처는 무엇인가요?'
},
{
'from': '어시스턴트',
'value': '출처는 항공을 통해 해외로 여행하는 승객의 여권 및 비자 요건과 같은 서류 요건이 포함된 데이터베이스인 Timatic(여행 정보 매뉴얼 자동)에서 가져온 것입니다. 위의 답변은 목적지가 네덜란드이고 귀국/귀국 여행 증빙이 있으며 5일간 체류할 계획이며 케냐 여권의 유효 기간이 약 3년 남은 케냐 시민이 케냐에서 직접 여행하는 시나리오를 기반으로 한 것입니다.'
}
]
}
```
### Citation Information
```
@InProceedings{kolimadataset,
title = {Ko-LIMA: Korean LIMA Dataset for Efficient Instruction-tuning},
author = {Hahn, Taeseung},
year = {2023}
}
```
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ccaligned_multilingual | 2022-11-03T16:31:56.000Z | [
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"language:fr",
"language:fy",
"language:ga",
"language:gl",
"language:gn",
"language:gu",
"language:he",
"language:hi",
"language:hr",
"language:hu",
"language:id",
"language:ig",
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"language:sk",
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"language:sn",
"language:so",
"language:sq",
"language:sr",
"language:ss",
"language:st",
"language:su",
"language:sv",
"language:sw",
"language:syc",
"language:szl",
"language:ta",
"language:te",
"language:tg",
"language:th",
"language:ti",
"language:tl",
"language:tn",
"language:tr",
"language:ts",
"language:tt",
"language:ug",
"language:uk",
"language:ur",
"language:uz",
"language:ve",
"language:vi",
"language:war",
"language:wo",
"language:xh",
"language:yi",
"language:yo",
"language:zgh",
"language:zh",
"language:zu",
"language:zza",
"license:unknown",
"region:us"
] | null | CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French). | @inproceedings{elkishky_ccaligned_2020,
author = {El-Kishky, Ahmed and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Koehn, Philipp},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
month = {November},
title = {{CCAligned}: A Massive Collection of Cross-lingual Web-Document Pairs},
year = {2020}
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.480",
doi = "10.18653/v1/2020.emnlp-main.480",
pages = "5960--5969"
} | 3 | 317 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- ak
- am
- ar
- as
- ay
- az
- be
- bg
- bm
- bn
- br
- bs
- ca
- ceb
- ckb
- cs
- cy
- de
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- ff
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- ts
- tt
- ug
- uk
- ur
- uz
- ve
- vi
- war
- wo
- xh
- yi
- yo
- zgh
- zh
- zu
- zza
license:
- unknown
multilinguality:
- translation
size_categories:
- n<1K
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
- 1M<n<10M
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
paperswithcode_id: ccaligned
pretty_name: CCAligned
dataset_info:
- config_name: documents-zz_TR
features:
- name: Domain
dtype: string
- name: Source_URL
dtype: string
- name: Target_URL
dtype: string
- name: translation
dtype:
translation:
languages:
- en_XX
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splits:
- name: train
num_bytes: 641412
num_examples: 41
download_size: 125488
dataset_size: 641412
- config_name: sentences-zz_TR
features:
- name: translation
dtype:
translation:
languages:
- en_XX
- zz_TR
- name: LASER_similarity
dtype: float32
splits:
- name: train
num_bytes: 4056
num_examples: 34
download_size: 1428
dataset_size: 4056
- config_name: documents-tz_MA
features:
- name: Domain
dtype: string
- name: Source_URL
dtype: string
- name: Target_URL
dtype: string
- name: translation
dtype:
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languages:
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splits:
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num_bytes: 51782
num_examples: 4
download_size: 11996
dataset_size: 51782
- config_name: sentences-tz_MA
features:
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dtype:
translation:
languages:
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- name: LASER_similarity
dtype: float32
splits:
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num_bytes: 6256
num_examples: 33
download_size: 2420
dataset_size: 6256
- config_name: documents-ak_GH
features:
- name: Domain
dtype: string
- name: Source_URL
dtype: string
- name: Target_URL
dtype: string
- name: translation
dtype:
translation:
languages:
- en_XX
- ak_GH
splits:
- name: train
num_bytes: 10738312
num_examples: 249
download_size: 399236
dataset_size: 10738312
- config_name: sentences-ak_GH
features:
- name: translation
dtype:
translation:
languages:
- en_XX
- ak_GH
- name: LASER_similarity
dtype: float32
splits:
- name: train
num_bytes: 50110
num_examples: 478
download_size: 17636
dataset_size: 50110
---
# Dataset Card for ccaligned_multilingual
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://www.statmt.org/cc-aligned/
- **Repository:** [Needs More Information]
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.480.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French). This corpus was created from 68 Commoncrawl Snapshots.
To load a language which isn't part of the config, all you need to do is specify the language code. You can find the valid languages in http://www.statmt.org/cc-aligned/ E.g.
```
dataset = load_dataset("ccaligned_multilingual", language_code="fr_XX", type="documents")
```
or
```
dataset = load_dataset("ccaligned_multilingual", language_code="fr_XX", type="sentences")
```
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The text in the dataset is in (137) multiple languages aligned with english.
## Dataset Structure
### Data Instances
An instance of `documents` type for language `ak_GH`:
```
{'Domain': 'islamhouse.com', 'Source_URL': 'https://islamhouse.com/en/audios/373088/', 'Target_URL': 'https://islamhouse.com/ak/audios/373088/', 'translation': {'ak_GH': "Ntwatiaa / wɔabɔ no tɔfa wɔ mu no te ase ma Umrah - Arab kasa|Islamhouse.com|Follow us:|facebook|twitter|taepe|Titles All|Fie wibesite|kasa nyina|Buukuu edi adanse ma prente|Nhyehyɛmu|Nyim/sua Islam|Curriculums|Nyina ndeɛma|Nyina ndeɛma (295)|Buukuu/ nwoma (2)|sini / muuvi (31)|ɔdio (262)|Aɛn websideNew!|Kɔ wura kramosom mu seisei|Ebio|figa/kaasɛ|Farebae|AKAkan|Kratafa titriw|kasa interface( anyimu) : Akan|Kasa ma no mu-nsɛm : Arab kasa|ɔdio|Ntwatiaa / wɔabɔ no tɔfa wɔ mu no te ase ma Umrah|play|pause|stop|mute|unmute|max volume|Kasakyerɛ ni :|Farebae:|17 / 11 / 1432 , 15/10/2011|Nhyehyɛmu:|Jurisprudence/ Esum Nimdea|Som|Hajj na Umrah|Jurisprudence/ Esum Nimdea|Som|Hajj na Umrah|Mmira ma Hajj na Umrah|nkyerɛmu|kasamu /sɛntɛns ma te ase na Umrah wɔ ... mu no hann ma no Quran na Sunnah na te ase ma no nana na no kasamu /sɛntɛns ma bi ma no emerging yi adu obusuani|Akenkane we ye di ko kasa bi su (36)|Afar - Qafár afa|Akan|Amhari ne - አማርኛ|Arab kasa - عربي|Assamese - অসমীয়া|Bengali - বাংলা|Maldive - ދިވެހި|Greek - Ελληνικά|English ( brofo kasa) - English|Persian - فارسی|Fula - pulla|French - Français|Hausa - Hausa|Kurdish - كوردی سۆرانی|Uganda ne - Oluganda|Mandinka - Mandinko|Malayalam - മലയാളം|Nepali - नेपाली|Portuguese - Português|Russian - Русский|Sango - Sango|Sinhalese - සිංහල|Somali - Soomaali|Albania ne - Shqip|Swahili - Kiswahili|Telugu - తెలుగు ప్రజలు|Tajik - Тоҷикӣ|Thai - ไทย|Tagalog - Tagalog|Turkish - Türkçe|Uyghur - ئۇيغۇرچە|Urdu - اردو|Uzbeck ne - Ўзбек тили|Vietnamese - Việt Nam|Wolof - Wolof|Chine ne - 中文|Soma kɔ bi kyerɛ adwen kɔ wɛb ebusuapanin|Soma kɔ ne kɔ hom adamfo|Soma kɔ bi kyerɛ adwen kɔ wɛb ebusuapanin|Nsɔwso fael (1)|1|الموجز في فقه العمرة|MP3 14.7 MB|Enoumah ebatahu|Rituals/Esom ajomadie ewu Hajji mmire .. 1434 AH [01] no fapemso Enum|Fiidbak/ Ye hiya wu jun kyiri|Lenke de yɛe|kɔntakt yɛn|Aɛn webside|Qura'an Kro kronkrom|Balagh|wɔ mfinimfin Dowload faele|Yɛ atuu bra Islam mu afei|Tsin de yɛe ewu|Anaa bomu/combine hɛn melin liste|© Islamhouse Website/ Islam dan webi site|×|×|Yi mu kasa|", 'en_XX': 'SUMMARY in the jurisprudence of Umrah - Arabic - Abdul Aziz Bin Marzooq Al-Turaifi|Islamhouse.com|Follow us:|facebook|twitter|QuranEnc.com|HadeethEnc.com|Type|Titles All|Home Page|All Languages|Categories|Know about Islam|All items|All items (4057)|Books (701)|Articles (548)|Fatawa (370)|Videos (1853)|Audios (416)|Posters (98)|Greeting cards (22)|Favorites (25)|Applications (21)|Desktop Applications (3)|To convert to Islam now !|More|Figures|Sources|Curriculums|Our Services|QuranEnc.com|HadeethEnc.com|ENEnglish|Main Page|Interface Language : English|Language of the content : Arabic|Audios|تعريب عنوان المادة|SUMMARY in the jurisprudence of Umrah|play|pause|stop|mute|unmute|max volume|Lecturer : Abdul Aziz Bin Marzooq Al-Turaifi|Sources:|AlRaya Islamic Recoding in Riyadh|17 / 11 / 1432 , 15/10/2011|Categories:|Islamic Fiqh|Fiqh of Worship|Hajj and Umrah|Islamic Fiqh|Fiqh of Worship|Hajj and Umrah|Pilgrimage and Umrah|Description|SUMMARY in jurisprudence of Umrah: A statement of jurisprudence and Umrah in the light of the Quran and Sunnah and understanding of the Ancestors and the statement of some of the emerging issues related to them.|This page translated into (36)|Afar - Qafár afa|Akane - Akan|Amharic - አማርኛ|Arabic - عربي|Assamese - অসমীয়া|Bengali - বাংলা|Maldivi - ދިވެހި|Greek - Ελληνικά|English|Persian - فارسی|Fula - pulla|French - Français|Hausa - Hausa|kurdish - كوردی سۆرانی|Ugandan - Oluganda|Mandinka - Mandinko|Malayalam - മലയാളം|Nepali - नेपाली|Portuguese - Português|Russian - Русский|Sango - Yanga ti Sango|Sinhalese - සිංහල|Somali - Soomaali|Albanian - Shqip|Swahili - Kiswahili|Telugu - తెలుగు|Tajik - Тоҷикӣ|Thai - ไทย|Tagalog - Tagalog|Turkish - Türkçe|Uyghur - ئۇيغۇرچە|Urdu - اردو|Uzbek - Ўзбек тили|Vietnamese - Việt Nam|Wolof - Wolof|Chinese - 中文|Send a comment to Webmaster|Send to a friend?|Send a comment to Webmaster|Attachments (1)|1|الموجز في فقه العمرة|MP3 14.7 MB|The relevant Material|The rituals of the pilgrimage season .. 1434 AH [ 01] the fifth pillar|The Quality of the Accepted Hajj (Piligrimage) and Its Limitations|Easy Path to the Rules of the Rites of Hajj|A Call to the Pilgrims of the Scared House of Allah|More|feedback|Important links|Contact us|Privacy policy|Islam Q&A|Learning Arabic Language|About Us|Convert To Islam|Noble Quran encyclopedia|IslamHouse.com Reader|Encyclopedia of Translated Prophetic Hadiths|Our Services|The Quran|Balagh|Center for downloading files|To embrace Islam now...|Follow us through|Or join our mailing list.|© Islamhouse Website|×|×|Choose language|'}}
```
An instance of `sentences` type for language `ak_GH`:
```
{'LASER_similarity': 1.4549942016601562, 'translation': {'ak_GH': 'Salah (nyamefere) ye Mmerebeia', 'en_XX': 'What he dislikes when fasting (10)'}}
```
### Data Fields
For `documents` type:
- `Domain`: a `string` feature containing the domain.
- `Source_URL`: a `string` feature containing the source URL.
- `Target_URL`: a `string` feature containing the target URL.
- `translation`: a `dictionary` feature with two keys :
- `en_XX`: a `string` feature containing the content in English.
- <language_code>: a `string` feature containing the content in the `language_code` specified.
For `sentences` type:
- `LASER_similarity`: a `float32` feature representing the LASER similarity score.
- `translation`: a `dictionary` feature with two keys :
- `en_XX`: a `string` feature containing the content in English.
- <language_code>: a `string` feature containing the content in the `language_code` specified.
### Data Splits
Split sizes of some small configurations:
| name |train|
|----------|----:|
|documents-zz_TR|41|
|sentences-zz_TR|34|
|documents-tz_MA|4|
|sentences-tz_MA|33|
|documents-ak_GH|249|
|sentences-ak_GH|478|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@inproceedings{elkishky_ccaligned_2020,
author = {El-Kishky, Ahmed and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Koehn, Philipp},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
month = {November},
title = {{CCAligned}: A Massive Collection of Cross-lingual Web-Document Pairs},
year = {2020}
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.480",
doi = "10.18653/v1/2020.emnlp-main.480",
pages = "5960--5969"
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset. | 13,315 | [
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Tevatron/beir-corpus | 2022-07-07T23:53:45.000Z | [
"region:us"
] | Tevatron | null | null | 0 | 317 | 2022-06-07T06:00:10 | Entry not found | 15 | [
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Muennighoff/flan | 2022-12-23T18:57:00.000Z | [
"task_categories:other",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"language:en",
"arxiv:2109.01652",
"region:us"
] | Muennighoff | null | null | 33 | 317 | 2022-12-12T11:32:26 | ---
annotations_creators:
- crowdsourced
- expert-generated
language:
- en
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
task_categories:
- other
---
This is a repreprocessed version of the [FLAN dataset](https://arxiv.org/abs/2109.01652) with any updates that have been made to the FLAN datasets since the release of the original FLAN. The script is available [here](https://github.com/Muennighoff/FLAN).
Tasks:
```
{'aeslc_10templates',
'ag_news_subset_10templates',
'anli_r1_10templates',
'anli_r2_10templates',
'anli_r3_10templates',
'arc_challenge_10templates',
'arc_easy_10templates',
'bool_q_10templates',
'cb_10templates',
'cnn_dailymail_10templates',
'cola_10templates',
'common_gen_10templates',
'copa_10templates',
'coqa_10templates',
'cosmos_qa_10templates',
'dart_10templates',
'definite_pronoun_resolution_10templates',
'drop_10templates',
'e2e_nlg_10templates',
'fix_punct_10templates',
'gigaword_10templates',
'glue_mrpc_10templates',
'glue_qqp_10templates',
'hellaswag_10templates',
'imdb_reviews_10templates',
'math_dataset_10templates',
'mnli_matched_10templates',
'mnli_mismatched_10templates',
'multi_news_10templates',
'multirc_10templates',
'natural_questions_10templates',
'openbookqa_10templates',
'opinion_abstracts_idebate_10templates',
'opinion_abstracts_rotten_tomatoes_10templates',
'para_crawl_enes_10templates',
'paws_wiki_10templates',
'piqa_10templates',
'qnli_10templates',
'quac_10templates',
'record_10templates',
'rte_10templates',
'samsum_10templates',
'sentiment140_10templates',
'snli_10templates',
'squad_v1_10templates',
'squad_v2_10templates',
'sst2_10templates',
'story_cloze_10templates',
'stsb_10templates',
'trec_10templates',
'trivia_qa_10templates',
'true_case_10templates',
'web_nlg_en_10templates',
'wic_10templates',
'wiki_lingua_english_en_10templates',
'wmt14_enfr_10templates',
'wmt16_translate_csen_10templates',
'wmt16_translate_deen_10templates',
'wmt16_translate_fien_10templates',
'wmt16_translate_roen_10templates',
'wmt16_translate_ruen_10templates',
'wmt16_translate_tren_10templates',
'wnli_10templates',
'word_segment_10templates',
'wsc_10templates',
'yelp_polarity_reviews_10templates'}
``` | 2,238 | [
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d0rj/curation-corpus | 2023-06-13T13:25:32.000Z | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"news",
"summarization",
"region:us"
] | d0rj | null | null | 1 | 317 | 2023-06-12T19:22:21 | ---
dataset_info:
features:
- name: title
dtype: string
- name: summary
dtype: string
- name: url
dtype: string
- name: date
dtype: string
- name: article_content
dtype: string
splits:
- name: train
num_bytes: 127948910
num_examples: 30455
download_size: 76620775
dataset_size: 127948910
license: cc-by-4.0
task_categories:
- summarization
multilinguality:
- monolingual
language:
- en
source_datasets:
- original
tags:
- news
- summarization
pretty_name: Curation Corpus for Abstractive Text Summarisation
paperswithcode_id: curation-corpus
size_categories:
- 10K<n<100K
---
# curation-corpus
## Dataset Description
- **Homepage:** [https://github.com/CurationCorp/curation-corpus](https://github.com/CurationCorp/curation-corpus)
- **Repository:** [https://github.com/CurationCorp/curation-corpus](https://github.com/CurationCorp/curation-corpus)
## Source
Data from [this official repo](https://github.com/CurationCorp/curation-corpus) with downloaded news articles content.
## Citation
```
@misc{curationcorpusbase:2020,
title={Curation Corpus Base},
author={Curation},
year={2020}
}
``` | 1,151 | [
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Short-Answer-Feedback/saf_communication_networks_english | 2023-03-31T11:46:04.000Z | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"short answer feedback",
"communication networks",
"region:us"
] | Short-Answer-Feedback | null | null | 6 | 316 | 2022-11-10T21:22:13 | ---
pretty_name: SAF - Communication Networks - English
annotations_creators:
- expert-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- short answer feedback
- communication networks
task_categories:
- text2text-generation
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: reference_answer
dtype: string
- name: provided_answer
dtype: string
- name: answer_feedback
dtype: string
- name: verification_feedback
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 2363828
num_examples: 1700
- name: validation
num_bytes: 592869
num_examples: 427
- name: test_unseen_answers
num_bytes: 515669
num_examples: 375
- name: test_unseen_questions
num_bytes: 777945
num_examples: 479
download_size: 941169
dataset_size: 4250311
license: cc-by-4.0
---
# Dataset Card for "saf_communication_networks_english"
## 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)
- [Annotation process](#annotation-process)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:** [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022)
### Dataset Summary
Short Answer Feedback (SAF) dataset is a short answer dataset introduced in [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 31 English questions covering a range of college-level communication networks topics - while the original dataset presented in the paper is comprised of an assortment of both English and German short answer questions (with reference answers). Please refer to the [saf_micro_job_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_micro_job_german) dataset to examine the German subset of the original dataset. Furthermore, a similarly constructed SAF dataset (covering the German legal domain) can be found at [saf_legal_domain_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_legal_domain_german).
### Supported Tasks and Leaderboards
- `short_answer_feedback`: The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.
### Languages
The questions, reference answers, provided answers and the answer feedback in the dataset are written in English.
## Dataset Structure
### Data Instances
An example of an entry of the training split looks as follows.
```
{
"id": "1",
"question": "Is this a question?",
"reference_answer": "Yes, that is a question.",
"provided_answer": "I'm certain this is a question.",
"answer_feedback": "The response is correct.",
"verification_feedback": "Correct",
"score": 1
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature (UUID4 in HEX format).
- `question`: a `string` feature representing a question.
- `reference_answer`: a `string` feature representing a reference answer to the question.
- `provided_answer`: a `string` feature representing an answer that was provided for a particular question.
- `answer_feedback`: a `string` feature representing the feedback given to the provided answers.
- `verification_feedback`: a `string` feature representing an automatic labeling of the score. It can be `Correct` (`score` = maximum points achievable), `Incorrect` (`score` = 0) or `Partially correct` (all intermediate scores).
- `score`: a `float64` feature representing the score given to the provided answer. For most questions it ranges from 0 to 1.
### Data Splits
The dataset is comprised of four data splits.
- `train`: used for training, contains a set of questions and the provided answers to them.
- `validation`: used for validation, contains a set of questions and the provided answers to them (derived from the original training set defined in the paper).
- `test_unseen_answers`: used for testing, contains unseen answers to the questions present in the `train` split.
- `test_unseen_questions`: used for testing, contains unseen questions that do not appear in the `train` split.
| Split |train|validation|test_unseen_answers|test_unseen_questions|
|-------------------|----:|---------:|------------------:|--------------------:|
|Number of instances| 1700| 427| 375| 479|
## Dataset Creation
### Annotation Process
Two graduate students who had completed the communication networks course were selected to evaluate the answers, and both of them underwent a general annotation guideline training (supervised by a Psychology doctoral student with prior work in the field of feedback). After the training, the annotators individually provided feedback to the answers following an agreed upon scoring rubric and the general annotation guideline. The individually annotated answer files were then combined into a cohesive gold standard after discussing and solving possible disagreements.
## Additional Information
### Citation Information
```
@inproceedings{filighera-etal-2022-answer,
title = "Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset",
author = "Filighera, Anna and
Parihar, Siddharth and
Steuer, Tim and
Meuser, Tobias and
Ochs, Sebastian",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.587",
doi = "10.18653/v1/2022.acl-long.587",
pages = "8577--8591",
}
```
### Contributions
Thanks to [@JohnnyBoy2103](https://github.com/JohnnyBoy2103) for adding this dataset. | 6,685 | [
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sidhq/email-thread-summary | 2023-07-17T03:19:09.000Z | [
"task_categories:summarization",
"language:en",
"region:us"
] | sidhq | null | null | 2 | 316 | 2023-07-17T01:08:40 | ---
dataset_info:
features:
- name: thread
struct:
- name: subject
dtype: string
- name: messages
list:
- name: timestamp
dtype: timestamp[s]
- name: from
dtype: string
- name: to
sequence: string
- name: body
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 38290851
num_examples: 3750
- name: test
num_bytes: 4189760
num_examples: 417
download_size: 13597444
dataset_size: 42480611
task_categories:
- summarization
language:
- en
pretty_name: Email Thread Summary
---
# Dataset Card for "email-thread-summary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 788 | [
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result-kand2-sdxl-wuerst-karlo/53decd51 | 2023-10-04T12:24:28.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 316 | 2023-10-04T12:24:27 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 190
num_examples: 10
download_size: 1351
dataset_size: 190
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "53decd51"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/5f48a05c | 2023-10-04T12:28:37.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 316 | 2023-10-04T12:28:36 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 176
num_examples: 10
download_size: 1365
dataset_size: 176
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "5f48a05c"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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edinburghcstr/ami | 2023-01-16T18:11:05.000Z | [
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"language:en",
"license:cc-by-4.0",
"arxiv:1906.11047",
"region:us"
] | edinburghcstr | The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
synchronized to a common timeline. These include close-talking and far-field microphones, individual and
room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings,
the participants also have unsynchronized pens available to them that record what is written. The meetings
were recorded in English using three different rooms with different acoustic properties, and include mostly
non-native speakers. \n | @inproceedings{10.1007/11677482_3,
author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain and Post, Wilfried and Reidsma, Dennis and Wellner, Pierre},
title = {The AMI Meeting Corpus: A Pre-Announcement},
year = {2005},
isbn = {3540325492},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/11677482_3},
doi = {10.1007/11677482_3},
abstract = {The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting
recordings. It is being created in the context of a project that is developing meeting
browsing technology and will eventually be released publicly. Some of the meetings
it contains are naturally occurring, and some are elicited, particularly using a scenario
in which the participants play different roles in a design team, taking a design project
from kick-off to completion over the course of a day. The corpus is being recorded
using a wide range of devices including close-talking and far-field microphones, individual
and room-view video cameras, projection, a whiteboard, and individual pens, all of
which produce output signals that are synchronized with each other. It is also being
hand-annotated for many different phenomena, including orthographic transcription,
discourse properties such as named entities and dialogue acts, summaries, emotions,
and some head and hand gestures. We describe the data set, including the rationale
behind using elicited material, and explain how the material is being recorded, transcribed
and annotated.},
booktitle = {Proceedings of the Second International Conference on Machine Learning for Multimodal Interaction},
pages = {28–39},
numpages = {12},
location = {Edinburgh, UK},
series = {MLMI'05}
} | 17 | 314 | 2022-08-17T22:02:08 | ---
annotations_creators: []
language:
- en
language_creators: []
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: AMI
size_categories: []
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
---
# Dataset Card for AMI
## 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)
- [Terms of Usage](#terms-of-usage)
## Dataset Description
- **Homepage:** https://groups.inf.ed.ac.uk/ami/corpus/
- **Repository:** https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [jonathan@ed.ac.uk](mailto:jonathan@ed.ac.uk)
## Dataset Description
The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
synchronized to a common timeline. These include close-talking and far-field microphones, individual and
room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings,
the participants also have unsynchronized pens available to them that record what is written. The meetings
were recorded in English using three different rooms with different acoustic properties, and include mostly
non-native speakers.
**Note**: This dataset corresponds to the data-processing of [KALDI's AMI S5 recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5).
This means text is normalized and the audio data is chunked according to the scripts above!
To make the user experience as simply as possible, we provide the already chunked data to the user here so that the following can be done:
### Example Usage
```python
from datasets import load_dataset
ds = load_dataset("edinburghcstr/ami", "ihm")
print(ds)
```
gives:
```
DatasetDict({
train: Dataset({
features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
num_rows: 108502
})
validation: Dataset({
features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
num_rows: 13098
})
test: Dataset({
features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
num_rows: 12643
})
})
```
```py
ds["train"][0]
```
automatically loads the audio into memory:
```
{'meeting_id': 'EN2001a',
'audio_id': 'AMI_EN2001a_H00_MEE068_0000557_0000594',
'text': 'OKAY',
'audio': {'path': '/cache/dir/path/downloads/extracted/2d75d5b3e8a91f44692e2973f08b4cac53698f92c2567bd43b41d19c313a5280/EN2001a/train_ami_en2001a_h00_mee068_0000557_0000594.wav',
'array': array([0. , 0. , 0. , ..., 0.00033569, 0.00030518,
0.00030518], dtype=float32),
'sampling_rate': 16000},
'begin_time': 5.570000171661377,
'end_time': 5.940000057220459,
'microphone_id': 'H00',
'speaker_id': 'MEE068'}
```
The dataset was tested for correctness by fine-tuning a Wav2Vec2-Large model on it, more explicitly [the `wav2vec2-large-lv60` checkpoint](https://huggingface.co/facebook/wav2vec2-large-lv60).
As can be seen in this experiments, training the model for less than 2 epochs gives
*Result (WER)*:
| "dev" | "eval" |
|---|---|
| 25.27 | 25.21 |
as can be seen [here](https://huggingface.co/patrickvonplaten/ami-wav2vec2-large-lv60).
The results are in-line with results of published papers:
- [*Hybrid acoustic models for distant and multichannel large vocabulary speech recognition*](https://www.researchgate.net/publication/258075865_Hybrid_acoustic_models_for_distant_and_multichannel_large_vocabulary_speech_recognition)
- [Multi-Span Acoustic Modelling using Raw Waveform Signals](https://arxiv.org/abs/1906.11047)
You can run [run.sh](https://huggingface.co/patrickvonplaten/ami-wav2vec2-large-lv60/blob/main/run.sh) to reproduce the result.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
#### Transcribed Subsets Size
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Citation Information
### Contributions
Thanks to [@sanchit-gandhi](https://github.com/sanchit-gandhi), [@patrickvonplaten](https://github.com/patrickvonplaten),
and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
## Terms of Usage
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awettig/Pile-Gutenberg-0.5B-6K-opt | 2023-07-10T19:44:26.000Z | [
"region:us"
] | awettig | null | null | 0 | 314 | 2023-07-10T19:42:59 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6500959920
num_examples: 81380
- name: test
num_bytes: 64945692
num_examples: 813
download_size: 1706776857
dataset_size: 6565905612
---
# Dataset Card for "Pile-Gutenberg-0.5B-6K-opt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 527 | [
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bigcode/commits_ft | 2023-07-11T04:31:12.000Z | [
"region:us"
] | bigcode | Code Commits for Instruction Tuning | @InProceedings{huggingface:dataset,
title = {Code Commits for Instruction Tuning},
author={BigCode},
year={2023}
} | 0 | 314 | 2023-07-11T04:00:41 | Entry not found | 15 | [
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result-kand2-sdxl-wuerst-karlo/ff0ba7a6 | 2023-10-04T13:47:35.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 314 | 2023-10-04T13:47:34 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 224
num_examples: 10
download_size: 1359
dataset_size: 224
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ff0ba7a6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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msra_ner | 2023-01-25T14:40:51.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:zh",
"license:unknown",
"region:us"
] | null | The Third International Chinese Language
Processing Bakeoff was held in Spring
2006 to assess the state of the art in two
important tasks: word segmentation and
named entity recognition. Twenty-nine
groups submitted result sets in the two
tasks across two tracks and a total of five
corpora. We found strong results in both
tasks as well as continuing challenges.
MSRA NER is one of the provided dataset.
There are three types of NE, PER (person),
ORG (organization) and LOC (location).
The dataset is in the BIO scheme.
For more details see https://faculty.washington.edu/levow/papers/sighan06.pdf | @inproceedings{levow2006third,
author = {Gina{-}Anne Levow},
title = {The Third International Chinese Language Processing Bakeoff: Word
Segmentation and Named Entity Recognition},
booktitle = {SIGHAN@COLING/ACL},
pages = {108--117},
publisher = {Association for Computational Linguistics},
year = {2006}
} | 18 | 313 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: MSRA NER
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
config_name: msra_ner
splits:
- name: train
num_bytes: 33323074
num_examples: 45001
- name: test
num_bytes: 2642934
num_examples: 3443
download_size: 15156606
dataset_size: 35966008
train-eval-index:
- config: msra_ner
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
---
# Dataset Card for MSRA NER
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/OYE93/Chinese-NLP-Corpus/tree/master/NER/MSRA)
- **Repository:** [Github](https://github.com/OYE93/Chinese-NLP-Corpus)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset. | 3,608 | [
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shailja/Verilog_GitHub | 2023-09-20T17:14:18.000Z | [
"license:mit",
"arxiv:2212.11140",
"region:us"
] | shailja | null | null | 3 | 313 | 2022-12-19T15:19:55 | ---
license: mit
---
---
pipeline_tag: text-generation
tags:
- code
model-index:
- name: VeriGen
results:
- task:
type: text-generation
dataset:
type:
name:
extra_gated_prompt: >-
## Model License Agreement
Please read the BigCode [OpenRAIL-M
license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement)
agreement before accepting it.
extra_gated_fields:
I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox
---
# VeriGen
## Table of Contents
1. [Dataset Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [License](##license)
5. [Citation](##citation)
## Dataset Summary
- The dataset comprises Verilog modules as entries. The entries were retrieved from the GitHub dataset on BigQuery.
- For training [models (https://huggingface.co/shailja/fine-tuned-codegen-2B-Verilog)], we filtered entries with no of characters exceeding 20000 and duplicates (exact duplicates ignoring whitespaces).
- **Paper:** [ Benchmarking Large Language Models for Automated Verilog RTL Code Generation](https://arxiv.org/abs/2212.11140)
- **Point of Contact:** [contact@shailja](mailto:shailja.thakur90@gmail.com)
- **Languages:** Verilog (Hardware Description Language)
### Data Splits
The dataset only contains a train split.
### Use
```python
# pip install datasets
from datasets import load_dataset
ds = load_dataset("shailja/Verilog_GitHub", streaming=True, split="train")
print(next(iter(ds)))
#OUTPUT:
```
### Intended Use
The dataset consists of source code from a range of GitHub repositories. As such, they can potentially include non-compilable, low-quality, and vulnerable code.
### Attribution & Other Requirements
The pretraining dataset of the model was not filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected.
# License
The dataset is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
# Citation
```
@misc{https://doi.org/10.48550/arxiv.2212.11140,
doi = {10.48550/ARXIV.2212.11140},
url = {https://arxiv.org/abs/2212.11140},
author = {Thakur, Shailja and Ahmad, Baleegh and Fan, Zhenxing and Pearce, Hammond and Tan, Benjamin and Karri, Ramesh and Dolan-Gavitt, Brendan and Garg, Siddharth},
title = {Benchmarking Large Language Models for Automated Verilog RTL Code Generation},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
``` | 2,782 | [
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sanchit-gandhi/concatenated_librispeech | 2023-01-26T11:45:39.000Z | [
"region:us"
] | sanchit-gandhi | null | null | 0 | 313 | 2023-01-26T10:26:12 | ---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 707889.0
num_examples: 1
download_size: 0
dataset_size: 707889.0
---
# Dataset Card for "concatenated_librispeech"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 359 | [
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Polyglot-or-Not/Fact-Completion | 2023-06-14T03:05:21.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text2text-generation",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"language:en",
"language:fr",
"language:es",
"language:de",
"language:uk",
"language:bg",
"language:ca",
"language:da",
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"language:cs",
"license:apache-2.0",
"natural-language-understanding",
"arxiv:2302.13971",
"arxiv:2305.13675",
"arxiv:2210.03329",
"arxiv:2210.07229",
"region:us"
] | Polyglot-or-Not | null | null | 10 | 313 | 2023-03-22T23:42:30 | ---
license: apache-2.0
tags:
- natural-language-understanding
language_creators:
- expert-generated
- machine-generated
multilinguality:
- multilingual
pretty_name: Polyglot or Not? Fact-Completion Benchmark
size_categories:
- 100K<n<1M
task_categories:
- text-generation
- fill-mask
- text2text-generation
dataset_info:
features:
- name: dataset_id
dtype: string
- name: stem
dtype: string
- name: 'true'
dtype: string
- name: 'false'
dtype: string
- name: relation
dtype: string
- name: subject
dtype: string
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dtype: string
splits:
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num_bytes: 3474255
num_examples: 26254
- name: Spanish
num_bytes: 3175733
num_examples: 18786
- name: French
num_bytes: 3395566
num_examples: 18395
- name: Russian
num_bytes: 659526
num_examples: 3289
- name: Portuguese
num_bytes: 4158146
num_examples: 22974
- name: German
num_bytes: 2611160
num_examples: 16287
- name: Italian
num_bytes: 3709786
num_examples: 20448
- name: Ukrainian
num_bytes: 1868358
num_examples: 7918
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num_examples: 17568
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num_examples: 9427
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num_examples: 20577
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num_examples: 5426
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num_examples: 4650
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num_bytes: 1195097
num_examples: 7358
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num_examples: 23365
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num_bytes: 1299653
num_examples: 7873
- name: Dutch
num_bytes: 3732795
num_examples: 22590
- name: Catalan
num_bytes: 3319466
num_examples: 18898
download_size: 27090207
dataset_size: 52358225
language:
- en
- fr
- es
- de
- uk
- bg
- ca
- da
- hr
- hu
- it
- nl
- pl
- pt
- ro
- ru
- sl
- sr
- sv
- cs
---
# Dataset Card
- **Homepage:** https://bit.ly/ischool-berkeley-capstone
- **Repository:** https://github.com/daniel-furman/Capstone
- **Point of Contact:** daniel_furman@berkeley.edu
## Dataset Summary
This is the dataset for **Polyglot or Not?: Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models**.
## Test Description
Given a factual association such as *The capital of France is **Paris***, we determine whether a model adequately "knows" this information with the following test:
* Step **1**: prompt the model to predict the likelihood of the token **Paris** following *The Capital of France is*
* Step **2**: prompt the model to predict the average likelihood of a set of false, counterfactual tokens following the same stem.
If the value from **1** is greater than the value from **2** we conclude that model adequately recalls that fact. Formally, this is an application of the Contrastive Knowledge Assessment proposed in [[1][bib]].
For every foundation model of interest (like [LLaMA](https://arxiv.org/abs/2302.13971)), we perform this assessment on a set of facts translated into 20 languages. All told, we score foundation models on 303k fact-completions ([results](https://github.com/daniel-furman/capstone#multilingual-fact-completion-results)).
We also score monolingual models (like [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)) on English-only fact-completion ([results](https://github.com/daniel-furman/capstone#english-fact-completion-results)).
## Languages
The dataset covers 20 languages, which use either the Latin or Cyrillic scripts: bg, ca, cs, da, de, en, es, fr, hr, hu, it,
nl, pl, pt, ro, ru, sl, sr, sv, uk.
## Data Splits
The dataset splits correspond to the 20 languages above.
## Source Data
We sourced the English cut of the dataset from [1] and [2] and used the Google Translate API to produce the other 19 language cuts.
## Licensing Information
The dataset is licensed under the Apache 2.0 license and may be used with the corresponding affordances without limit.
## Citation Information
```
@misc{schott2023polyglot,
doi = {10.48550/arXiv.2305.13675},
title={Polyglot or Not? Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models},
author={Tim Schott and Daniel Furman and Shreshta Bhat},
year={2023},
eprint={2305.13675,
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Bibliography
[1] Dong, Qingxiu, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. "Calibrating Factual Knowledge in Pretrained Language Models". In Findings of the Association for Computational Linguistics: EMNLP 2022. [arXiv:2210.03329][cka] (2022).
```
@misc{dong2022calibrating,
doi = {10.48550/arXiv.2210.03329},
title={Calibrating Factual Knowledge in Pretrained Language Models},
author={Qingxiu Dong and Damai Dai and Yifan Song and Jingjing Xu and Zhifang Sui and Lei Li},
year={2022},
eprint={2210.03329},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
[2] Meng, Kevin, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. "Mass Editing Memory in a Transformer." arXiv preprint [arXiv:2210.07229][memit] (2022).
```
@misc{meng2022massediting,
doi = {10.48550/arXiv.2210.07229},
title={Mass-Editing Memory in a Transformer},
author={Kevin Meng and Arnab Sen Sharma and Alex Andonian and Yonatan Belinkov and David Bau},
year={2022},
eprint={2210.07229},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 5,729 | [
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DFKI-SLT/cdcp | 2023-08-08T12:47:42.000Z | [
"region:us"
] | DFKI-SLT | null | @inproceedings{niculae-etal-2017-argument,
title = "Argument Mining with Structured {SVM}s and {RNN}s",
author = "Niculae, Vlad and
Park, Joonsuk and
Cardie, Claire",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1091",
doi = "10.18653/v1/P17-1091",
pages = "985--995",
abstract = "We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20{\\%} of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.",
} | 0 | 312 | 2023-06-26T10:07:42 | Entry not found | 15 | [
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arielnlee/Superimposed-Masked-Dataset | 2023-08-01T18:08:45.000Z | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"occlusion",
"arxiv:2306.17848",
"region:us"
] | arielnlee | SMD is an occluded ImageNet-1K validation set, created to be an additional way to evaluate the impact of occlusion on model performance. This experiment used a variety of occluder objects that are not in the ImageNet-1K label space and are unambiguous in relationship to objects that reside in the label space. | @misc{lee2023hardwiring,
title={Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing},
author={Ariel N. Lee and Sarah Adel Bargal and Janavi Kasera and Stan Sclaroff and Kate Saenko and Nataniel Ruiz},
year={2023},
eprint={2306.17848},
archivePrefix={arXiv},
primaryClass={cs.CV}
} | 1 | 312 | 2023-06-28T05:07:48 | ---
license: other
task_categories:
- image-classification
language:
- en
tags:
- occlusion
size_categories:
- 10K<n<100K
---
# Superimposed Masked Dataset (SMD)
SMD is an occluded version of the ImageNet-1K validation set, created to serve as an additional way to evaluate the impact of occlusion on model performance. Occluder objects were segmented using Meta's Segment Anything and are not in the ImageNet-1K label space. They were chosen to be unambiguous in relationship to objects that reside in the label space. Additional details about the dataset, including code to generate your own version of SMD, actual occlusion percentage of each image in the dataset, as well as occluder object segmentation masks, will be released shortly.
The occluders shown above from left to right, starting from the top row: <strong>Grogu (baby yoda), bacteria, bacteriophage, airpods, origami heart, drone, diamonds (stones, not setting) and coronavirus</strong>. Occluder object images were obtained through Unsplash.
SMD was created for testing model robustness to occlusion in [Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing](https://arielnlee.github.io/PatchMixing/).
## Citations
```bibtex
@misc{lee2023hardwiring,
title={Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing},
author={Ariel N. Lee and Sarah Adel Bargal and Janavi Kasera and Stan Sclaroff and Kate Saenko and Nataniel Ruiz},
year={2023},
eprint={2306.17848},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@article{imagenet15russakovsky,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challenge} },
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}
``` | 2,086 | [
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awettig/Pile-ArXiv-0.5B-6K-opt | 2023-07-10T19:42:58.000Z | [
"region:us"
] | awettig | null | null | 0 | 312 | 2023-07-10T19:41:28 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6500959920
num_examples: 81380
- name: test
num_bytes: 64945692
num_examples: 813
download_size: 1581567196
dataset_size: 6565905612
---
# Dataset Card for "Pile-ArXiv-0.5B-6K-opt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 523 | [
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result-kand2-sdxl-wuerst-karlo/4390ae17 | 2023-10-04T16:37:17.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 312 | 2023-10-04T16:37:16 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 175
num_examples: 10
download_size: 1353
dataset_size: 175
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "4390ae17"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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sordonia/facts-text-davinci-003_clen128_maxD100_maxC-1 | 2023-10-13T19:29:48.000Z | [
"region:us"
] | sordonia | null | null | 0 | 312 | 2023-10-13T19:29:35 | ## model_name: text-davinci-003
## max_contexts_per_subject: -1
## max_documents_per_subject: 100
## max_context_length: 128
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] |
bigbio/bioasq_task_b | 2022-12-22T15:41:12.000Z | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | bigbio | The data are intended to be used as training and development data for BioASQ
10, which will take place during 2022. There is one file containing the data:
- training10b.json
The file contains the data of the first nine editions of the challenge: 4234
questions [1] with their relevant documents, snippets, concepts and RDF
triples, exact and ideal answers.
Differences with BioASQ-training9b.json
- 492 new questions added from BioASQ9
- The question with id 56c1f01eef6e394741000046 had identical body with
602498cb1cb411341a00009e. All relevant elements from both questions
are available in the merged question with id 602498cb1cb411341a00009e.
- The question with id 5c7039207c78d69471000065 had identical body with
601c317a1cb411341a000014. All relevant elements from both questions
are available in the merged question with id 601c317a1cb411341a000014.
- The question with id 5e4b540b6d0a27794100001c had identical body with
602828b11cb411341a0000fc. All relevant elements from both questions
are available in the merged question with id 602828b11cb411341a0000fc.
- The question with id 5fdb42fba43ad31278000027 had identical body with
5d35eb01b3a638076300000f. All relevant elements from both questions
are available in the merged question with id 5d35eb01b3a638076300000f.
- The question with id 601d76311cb411341a000045 had identical body with
6060732b94d57fd87900003d. All relevant elements from both questions
are available in the merged question with id 6060732b94d57fd87900003d.
[1] 4234 questions : 1252 factoid, 1148 yesno, 1018 summary, 816 list | @article{tsatsaronis2015overview,
title = {
An overview of the BIOASQ large-scale biomedical semantic indexing and
question answering competition
},
author = {
Tsatsaronis, George and Balikas, Georgios and Malakasiotis, Prodromos
and Partalas, Ioannis and Zschunke, Matthias and Alvers, Michael R and
Weissenborn, Dirk and Krithara, Anastasia and Petridis, Sergios and
Polychronopoulos, Dimitris and others
},
year = 2015,
journal = {BMC bioinformatics},
publisher = {BioMed Central Ltd},
volume = 16,
number = 1,
pages = 138
} | 3 | 311 | 2022-09-26T04:05:28 | ---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: NLM_LICENSE
pretty_name: BioASQ Task B
homepage: http://participants-area.bioasq.org/datasets/
bigbio_pubmed: true
bigbio_public: false
bigbio_tasks:
- QUESTION_ANSWERING
---
# Dataset Card for BioASQ Task B
## Dataset Description
- **Homepage:** http://participants-area.bioasq.org/datasets/
- **Pubmed:** True
- **Public:** False
- **Tasks:** QA
The BioASQ corpus contains multiple question
answering tasks annotated by biomedical experts, including yes/no, factoid, list,
and summary questions. Pertaining to our objective of comparing neural language
models, we focus on the the yes/no questions (Task 7b), and leave the inclusion
of other tasks to future work. Each question is paired with a reference text
containing multiple sentences from a PubMed abstract and a yes/no answer. We use
the official train/dev/test split of 670/75/140 questions.
See 'Domain-Specific Language Model Pretraining for Biomedical
Natural Language Processing'
## Citation Information
```
@article{tsatsaronis2015overview,
title = {
An overview of the BIOASQ large-scale biomedical semantic indexing and
question answering competition
},
author = {
Tsatsaronis, George and Balikas, Georgios and Malakasiotis, Prodromos
and Partalas, Ioannis and Zschunke, Matthias and Alvers, Michael R and
Weissenborn, Dirk and Krithara, Anastasia and Petridis, Sergios and
Polychronopoulos, Dimitris and others
},
year = 2015,
journal = {BMC bioinformatics},
publisher = {BioMed Central Ltd},
volume = 16,
number = 1,
pages = 138
}
```
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nielsr/funsd-iob-original | 2022-11-19T13:38:09.000Z | [
"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}
} | 0 | 311 | 2022-11-19T13:30:51 | Entry not found | 15 | [
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awettig/Pile-YoutubeSubtitles-0.5B-6K-opt | 2023-07-10T19:35:45.000Z | [
"region:us"
] | awettig | null | null | 0 | 311 | 2023-07-10T19:34:17 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6500643383
num_examples: 81380
- name: test
num_bytes: 64945692
num_examples: 813
download_size: 1594423762
dataset_size: 6565589075
---
# Dataset Card for "Pile-YoutubeSubtitles-0.5B-6K-opt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 534 | [
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awettig/Pile-HackerNews-0.5B-6K-opt | 2023-07-10T19:37:24.000Z | [
"region:us"
] | awettig | null | null | 0 | 311 | 2023-07-10T19:35:46 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6359132637
num_examples: 81380
- name: test
num_bytes: 64945692
num_examples: 813
download_size: 1710629426
dataset_size: 6424078329
---
# Dataset Card for "Pile-HackerNews-0.5B-6K-opt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 528 | [
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result-kand2-sdxl-wuerst-karlo/991f2e12 | 2023-10-04T17:42:55.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T17:42:54 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 154
num_examples: 10
download_size: 1300
dataset_size: 154
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "991f2e12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/3ed8d887 | 2023-10-04T17:46:58.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T17:46:57 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 156
num_examples: 10
download_size: 1308
dataset_size: 156
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "3ed8d887"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/c50ece24 | 2023-10-04T17:50:04.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T17:50:03 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 163
num_examples: 10
download_size: 1317
dataset_size: 163
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "c50ece24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/4b23c5a8 | 2023-10-04T17:54:41.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T17:54:38 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 178
num_examples: 10
download_size: 1335
dataset_size: 178
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "4b23c5a8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/c98495e0 | 2023-10-04T17:58:59.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T17:58:57 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 156
num_examples: 10
download_size: 1307
dataset_size: 156
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "c98495e0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/7709cb1f | 2023-10-04T18:03:33.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T18:03:32 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1331
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7709cb1f"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/0415e725 | 2023-10-04T18:08:30.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T18:08:29 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 163
num_examples: 10
download_size: 1335
dataset_size: 163
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "0415e725"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/23611323 | 2023-10-04T18:18:01.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 311 | 2023-10-04T18:18:00 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 166
num_examples: 10
download_size: 1336
dataset_size: 166
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "23611323"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
lighteval/EntityMatching | 2023-05-09T15:35:01.000Z | [
"region:us"
] | lighteval | null | @inproceedings{mudgal2018deep,
title={Deep learning for entity matching: A design space exploration},
author={Mudgal, Sidharth and Li, Han and Rekatsinas, Theodoros and Doan, AnHai and Park, Youngchoon and Krishnan, Ganesh and Deep, Rohit and Arcaute, Esteban and Raghavendra, Vijay},
booktitle={Proceedings of the 2018 International Conference on Management of Data},
pages={19--34},
year={2018}
} | 2 | 310 | 2023-05-09T14:56:27 | Entry not found | 15 | [
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toughdata/quora-question-answer-dataset | 2023-08-28T13:36:21.000Z | [
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:text2text-generation",
"language:en",
"license:gpl-3.0",
"question",
"answer",
"quora",
"region:us"
] | toughdata | null | null | 0 | 310 | 2023-08-23T22:53:09 | ---
license: gpl-3.0
task_categories:
- question-answering
- conversational
- text2text-generation
language:
- en
tags:
- question
- answer
- quora
pretty_name: Quora Question/Answer Pairs
---
Quora Question Answer Dataset (Quora-QuAD) contains 56,402 question-answer pairs scraped from Quora.
# Usage:
For instructions on fine-tuning a model (Flan-T5) with this dataset, please check out the article: https://www.toughdata.net/blog/post/finetune-flan-t5-question-answer-quora-dataset | 485 | [
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] |
yzhuang/autotree_snnxor_n15_l1_10 | 2023-09-18T21:51:32.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 310 | 2023-09-05T17:49:19 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
- name: test
num_bytes: 236440000
num_examples: 10000
download_size: 432260994
dataset_size: 709320000
---
# Dataset Card for "autotree_snnxor_n15_l1_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 880 | [
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result-kand2-sdxl-wuerst-karlo/eacbe536 | 2023-10-04T18:13:45.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:13:44 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 173
num_examples: 10
download_size: 1377
dataset_size: 173
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "eacbe536"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/b4de2e4d | 2023-10-04T18:26:47.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:26:46 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 193
num_examples: 10
download_size: 1385
dataset_size: 193
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b4de2e4d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/7acd34b3 | 2023-10-04T18:30:20.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:30:19 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 166
num_examples: 10
download_size: 1331
dataset_size: 166
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7acd34b3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/fc5ced1b | 2023-10-04T18:33:32.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:33:31 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 149
num_examples: 10
download_size: 1316
dataset_size: 149
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "fc5ced1b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/7f43ba07 | 2023-10-04T18:38:54.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:38:54 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 188
num_examples: 10
download_size: 1352
dataset_size: 188
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7f43ba07"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/a8072b85 | 2023-10-04T18:47:47.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:47:46 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 170
num_examples: 10
download_size: 1348
dataset_size: 170
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a8072b85"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/be76ce08 | 2023-10-04T18:52:19.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 310 | 2023-10-04T18:52:19 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 178
num_examples: 10
download_size: 1354
dataset_size: 178
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "be76ce08"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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mozilla-foundation/common_voice_6_1 | 2023-07-29T16:00:07.000Z | [
"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
} | 4 | 309 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
ab:
- n<1K
ar:
- 10K<n<100K
as:
- n<1K
br:
- 10K<n<100K
ca:
- 100K<n<1M
cnh:
- 1K<n<10K
cs:
- 10K<n<100K
cv:
- 10K<n<100K
cy:
- 10K<n<100K
de:
- 100K<n<1M
dv:
- 10K<n<100K
el:
- 10K<n<100K
en:
- 1M<n<10M
eo:
- 10K<n<100K
es:
- 100K<n<1M
et:
- 10K<n<100K
eu:
- 10K<n<100K
fa:
- 100K<n<1M
fi:
- 1K<n<10K
fr:
- 100K<n<1M
fy-NL:
- 10K<n<100K
ga-IE:
- 1K<n<10K
hi:
- n<1K
hsb:
- 1K<n<10K
hu:
- 1K<n<10K
ia:
- 1K<n<10K
id:
- 10K<n<100K
it:
- 100K<n<1M
ja:
- 1K<n<10K
ka:
- 1K<n<10K
kab:
- 100K<n<1M
ky:
- 10K<n<100K
lg:
- 1K<n<10K
lt:
- 1K<n<10K
lv:
- 1K<n<10K
mn:
- 10K<n<100K
mt:
- 10K<n<100K
nl:
- 10K<n<100K
or:
- 1K<n<10K
pa-IN:
- 1K<n<10K
pl:
- 100K<n<1M
pt:
- 10K<n<100K
rm-sursilv:
- 1K<n<10K
rm-vallader:
- 1K<n<10K
ro:
- 1K<n<10K
ru:
- 10K<n<100K
rw:
- 1M<n<10M
sah:
- 1K<n<10K
sl:
- 1K<n<10K
sv-SE:
- 10K<n<100K
ta:
- 10K<n<100K
th:
- 10K<n<100K
tr:
- 10K<n<100K
tt:
- 10K<n<100K
uk:
- 10K<n<100K
vi:
- 1K<n<10K
vot:
- n<1K
zh-CN:
- 10K<n<100K
zh-HK:
- 10K<n<100K
zh-TW:
- 10K<n<100K
source_datasets:
- extended|common_voice
paperswithcode_id: common-voice
pretty_name: Common Voice Corpus 6.1
language_bcp47:
- ab
- ar
- as
- br
- ca
- cnh
- cs
- cv
- cy
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy-NL
- ga-IE
- hi
- hsb
- hu
- ia
- id
- it
- ja
- ka
- kab
- ky
- lg
- lt
- lv
- mn
- mt
- nl
- or
- pa-IN
- pl
- pt
- rm-sursilv
- rm-vallader
- ro
- ru
- rw
- sah
- sl
- sv-SE
- ta
- th
- tr
- tt
- uk
- vi
- vot
- zh-CN
- zh-HK
- zh-TW
extra_gated_prompt: By clicking on “Access repository” below, you also agree to not
attempt to determine the identity of speakers in the Common Voice dataset.
task_categories:
- automatic-speech-recognition
---
# Dataset Card for Common Voice Corpus 6.1
## 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 9283 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 7335 validated hours in 60 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, Assamese, Basque, Breton, Catalan, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Dhivehi, Dutch, English, Esperanto, Estonian, Finnish, French, Frisian, Georgian, German, Greek, Hakha Chin, Hindi, Hungarian, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Lithuanian, Luganda, Maltese, Mongolian, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Slovenian, Sorbian, Upper, Spanish, Swedish, Tamil, Tatar, Thai, Turkish, Ukrainian, 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_6_1", "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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result-kand2-sdxl-wuerst-karlo/980edb53 | 2023-10-04T18:22:22.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 309 | 2023-10-04T18:22:21 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 156
num_examples: 10
download_size: 1319
dataset_size: 156
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "980edb53"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/f7f54a55 | 2023-10-04T18:43:41.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 309 | 2023-10-04T18:43:40 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 141
num_examples: 10
download_size: 1325
dataset_size: 141
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "f7f54a55"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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mteb/toxic_conversations_50k | 2022-09-27T19:14:35.000Z | [
"language:en",
"region:us"
] | mteb | null | null | 3 | 307 | 2022-05-26T17:47:49 | ---
language:
- en
---
# Toxic Conversation
This is a version of the [Jigsaw Unintended Bias in Toxicity Classification dataset](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview). It contains comments from the Civil Comments platform together with annotations if the comment is toxic or not.
This dataset just contains the first 50k training examples.
10 annotators annotated each example and, as recommended in the task page, set a comment as toxic when target >= 0.5
The dataset is inbalanced, with only about 8% of the comments marked as toxic.
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casehold/casehold | 2023-10-04T19:55:29.000Z | [
"region:us"
] | casehold | CaseHOLD (Case Holdings On Legal Decisions) is a law dataset comprised of over 53,000+ multiple choice questions to identify the relevant holding of a cited case. | @inproceedings{zhengguha2021,
title={When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset},
author={Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho},
year={2021},
eprint={2104.08671},
archivePrefix={arXiv},
primaryClass={cs.CL},
booktitle={Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law},
publisher={Association for Computing Machinery}
} | 5 | 307 | 2023-03-27T23:04:36 | Entry not found | 15 | [
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] |
dmrau/trec_dl20-qrels | 2023-10-09T08:28:57.000Z | [
"region:us"
] | dmrau | null | null | 0 | 307 | 2023-10-06T11:23:29 | ---
dataset_info:
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: string
splits:
- name: test
num_bytes: 298319
num_examples: 11386
download_size: 0
dataset_size: 298319
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "trec_dl20-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 509 | [
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] |
hmao/reformatted_singleapi_openai | 2023-10-23T23:26:04.000Z | [
"region:us"
] | hmao | null | null | 0 | 307 | 2023-10-21T03:43:10 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: api_name
dtype: string
- name: api_definition
dtype: string
- name: dataset_name
dtype: string
splits:
- name: train
num_bytes: 21189
num_examples: 14
download_size: 14297
dataset_size: 21189
---
# Dataset Card for "reformatted_singleapi_openai"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 536 | [
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] |
code_x_glue_cc_cloze_testing_all | 2023-06-01T14:59:51.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:code",
"license:c-uda",
"region:us"
] | null | Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
Here we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. Each instance in the dataset contains a masked code function, its docstring and the target word.
The only difference between ClozeTest-maxmin and ClozeTest-all is their selected words sets, where ClozeTest-maxmin only contains two words while ClozeTest-all contains 930 words. | @article{CodeXGLUE,
title={CodeXGLUE: An Open Challenge for Code Intelligence},
journal={arXiv},
year={2020},
}
@article{feng2020codebert,
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others},
journal={arXiv preprint arXiv:2002.08155},
year={2020}
}
@article{husain2019codesearchnet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
} | 3 | 306 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- slot-filling
pretty_name: CodeXGlueCcClozeTestingAll
dataset_info:
- config_name: go
features:
- name: id
dtype: int32
- name: idx
dtype: string
- name: nl_tokens
sequence: string
- name: pl_tokens
sequence: string
splits:
- name: train
num_bytes: 22409765
num_examples: 25282
download_size: 32578836
dataset_size: 22409765
- config_name: java
features:
- name: id
dtype: int32
- name: idx
dtype: string
- name: nl_tokens
sequence: string
- name: pl_tokens
sequence: string
splits:
- name: train
num_bytes: 40392965
num_examples: 40492
download_size: 56468936
dataset_size: 40392965
- config_name: javascript
features:
- name: id
dtype: int32
- name: idx
dtype: string
- name: nl_tokens
sequence: string
- name: pl_tokens
sequence: string
splits:
- name: train
num_bytes: 16090182
num_examples: 13837
download_size: 22665666
dataset_size: 16090182
- config_name: php
features:
- name: id
dtype: int32
- name: idx
dtype: string
- name: nl_tokens
sequence: string
- name: pl_tokens
sequence: string
splits:
- name: train
num_bytes: 51328988
num_examples: 51930
download_size: 73115225
dataset_size: 51328988
- config_name: python
features:
- name: id
dtype: int32
- name: idx
dtype: string
- name: nl_tokens
sequence: string
- name: pl_tokens
sequence: string
splits:
- name: train
num_bytes: 40631213
num_examples: 40137
download_size: 56766288
dataset_size: 40631213
- config_name: ruby
features:
- name: id
dtype: int32
- name: idx
dtype: string
- name: nl_tokens
sequence: string
- name: pl_tokens
sequence: string
splits:
- name: train
num_bytes: 3454904
num_examples: 4437
download_size: 4825752
dataset_size: 3454904
config_names:
- go
- java
- javascript
- php
- python
- ruby
---
# Dataset Card for "code_x_glue_cc_cloze_testing_all"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits-sample-size)
- [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/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-all
### Dataset Summary
CodeXGLUE ClozeTesting-all dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-all
Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
Here we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. Each instance in the dataset contains a masked code function, its docstring and the target word.
The only difference between ClozeTest-maxmin and ClozeTest-all is their selected words sets, where ClozeTest-maxmin only contains two words while ClozeTest-all contains 930 words.
### Supported Tasks and Leaderboards
- `slot-filling`: The dataset can be used to train a model for predicting the missing token from a piece of code, similar to the Cloze test.
### Languages
- Go **programming** language
- Java **programming** language
- Javascript **programming** language
- PHP **programming** language
- Python **programming** language
- Ruby **programming** language
## Dataset Structure
### Data Instances
#### go
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["MarshalJSON", "supports", "json", ".", "Marshaler", "interface"],
"pl_tokens": ["func", "(", "v", "ContextRealtimeData", ")", "MarshalJSON", "(", ")", "(", "[", "]", "byte", ",", "error", ")", "{", "w", ":=", "jwriter", ".", "<mask>", "{", "}", "\n", "easyjsonC5a4559bEncodeGithubComChromedpCdprotoWebaudio7", "(", "&", "w", ",", "v", ")", "\n", "return", "w", ".", "Buffer", ".", "BuildBytes", "(", ")", ",", "w", ".", "Error", "\n", "}"]
}
```
#### java
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["/", "*", "(", "non", "-", "Javadoc", ")"],
"pl_tokens": ["@", "Override", "public", "int", "peekBit", "(", ")", "throws", "AACException", "{", "int", "ret", ";", "if", "(", "bitsCached", ">", "0", ")", "{", "ret", "=", "(", "cache", ">>", "(", "bitsCached", "-", "1", ")", ")", "&", "1", ";", "}", "else", "{", "final", "int", "word", "=", "readCache", "(", "true", ")", ";", "ret", "=", "(", "<mask>", ">>", "WORD_BITS", "-", "1", ")", "&", "1", ";", "}", "return", "ret", ";", "}"]
}
```
#### javascript
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["Cast", "query", "params", "according", "to", "type"],
"pl_tokens": ["function", "castQueryParams", "(", "relId", ",", "data", ",", "{", "relationships", "}", ")", "{", "const", "relationship", "=", "relationships", "[", "relId", "]", "if", "(", "!", "relationship", ".", "query", ")", "{", "return", "{", "}", "}", "return", "Object", ".", "keys", "(", "relationship", ".", "query", ")", ".", "reduce", "(", "(", "params", ",", "<mask>", ")", "=>", "{", "const", "value", "=", "getField", "(", "data", ",", "relationship", ".", "query", "[", "key", "]", ")", "if", "(", "value", "===", "undefined", ")", "{", "throw", "new", "TypeError", "(", "'Missing value for query param'", ")", "}", "return", "{", "...", "params", ",", "[", "key", "]", ":", "value", "}", "}", ",", "{", "}", ")", "}"]
}
```
#### php
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["Get", "choices", "."],
"pl_tokens": ["protected", "<mask>", "getChoices", "(", "FormFieldTranslation", "$", "translation", ")", "{", "$", "choices", "=", "preg_split", "(", "'/\\r\\n|\\r|\\n/'", ",", "$", "translation", "->", "getOption", "(", "'choices'", ")", ",", "-", "1", ",", "PREG_SPLIT_NO_EMPTY", ")", ";", "return", "array_combine", "(", "$", "choices", ",", "$", "choices", ")", ";", "}"]
}
```
#### python
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["Post", "a", "review"],
"pl_tokens": ["def", "post_review", "(", "session", ",", "review", ")", ":", "# POST /api/projects/0.1/reviews/", "<mask>", "=", "make_post_request", "(", "session", ",", "'reviews'", ",", "json_data", "=", "review", ")", "json_data", "=", "response", ".", "json", "(", ")", "if", "response", ".", "status_code", "==", "200", ":", "return", "json_data", "[", "'status'", "]", "else", ":", "raise", "ReviewNotPostedException", "(", "message", "=", "json_data", "[", "'message'", "]", ",", "error_code", "=", "json_data", "[", "'error_code'", "]", ",", "request_id", "=", "json_data", "[", "'request_id'", "]", ")"]
}
```
#### ruby
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["By", "default", "taskers", "don", "t", "see", "the", "flor", "variables", "in", "the", "execution", ".", "If", "include_vars", "or", "exclude_vars", "is", "present", "in", "the", "configuration", "of", "the", "tasker", "some", "or", "all", "of", "the", "variables", "are", "passed", "."],
"pl_tokens": ["def", "gather_vars", "(", "executor", ",", "tconf", ",", "message", ")", "# try to return before a potentially costly call to executor.vars(nid)", "return", "nil", "if", "(", "tconf", ".", "keys", "&", "%w[", "include_vars", "exclude_vars", "]", ")", ".", "empty?", "# default behaviour, don't pass variables to taskers", "iv", "=", "expand_filter", "(", "tconf", "[", "'include_vars'", "]", ")", "return", "nil", "if", "iv", "==", "false", "ev", "=", "expand_filter", "(", "tconf", "[", "'exclude_vars'", "]", ")", "return", "{", "}", "if", "ev", "==", "true", "vars", "=", "executor", ".", "vars", "(", "message", "[", "'nid'", "]", ")", "return", "vars", "if", "iv", "==", "true", "vars", "=", "vars", ".", "select", "{", "|", "k", ",", "v", "|", "var_match", "(", "k", ",", "iv", ")", "}", "if", "<mask>", "vars", "=", "vars", ".", "reject", "{", "|", "k", ",", "v", "|", "var_match", "(", "k", ",", "ev", ")", "}", "if", "ev", "vars", "end"]
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### go, java, javascript, php, python, ruby
|field name| type | description |
|----------|----------------|------------------------------|
|id |int32 | Index of the sample |
|idx |string | Original index in the dataset|
|nl_tokens |Sequence[string]| Natural language tokens |
|pl_tokens |Sequence[string]| Programming language tokens |
### Data Splits
| name |train|
|----------|----:|
|go |25282|
|java |40492|
|javascript|13837|
|php |51930|
|python |40137|
|ruby | 4437|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Data from CodeSearchNet Challenge dataset.
[More Information Needed]
#### Who are the source language producers?
Software Engineering developers.
### 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
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@article{CodeXGLUE,
title={CodeXGLUE: An Open Challenge for Code Intelligence},
journal={arXiv},
year={2020},
}
@article{feng2020codebert,
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others},
journal={arXiv preprint arXiv:2002.08155},
year={2020}
}
@article{husain2019codesearchnet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. | 11,892 | [
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ARTeLab/ilpost | 2022-11-17T02:50:32.000Z | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"language:it",
"region:us"
] | ARTeLab | null | null | 2 | 306 | 2022-03-02T23:29:22 | ---
language:
- it
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
task_categories:
- summarization
---
# Dataset Card for ilpost
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
IlPost dataset, containing news articles taken from IlPost.
There are two features:
- source: Input news article.
- target: Summary of the article.
### Supported Tasks and Leaderboards
- `abstractive-summarization`, `summarization`
### Languages
The text in the dataset is in Italian
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
``` | 4,121 | [
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pietrolesci/stress_tests_nli | 2022-04-25T09:32:28.000Z | [
"region:us"
] | pietrolesci | null | null | 0 | 306 | 2022-04-25T09:21:50 | ## Overview
Original dataset page [here](https://abhilasharavichander.github.io/NLI_StressTest/) and dataset available [here](https://drive.google.com/open?id=1faGA5pHdu5Co8rFhnXn-6jbBYC2R1dhw).
## Dataset curation
Added new column `label` with encoded labels with the following mapping
```
{"entailment": 0, "neutral": 1, "contradiction": 2}
```
and the columns with parse information are dropped as they are not well formatted.
Also, the name of the file from which each instance comes is added in the column `dtype`.
## Code to create the dataset
```python
import pandas as pd
from datasets import Dataset, ClassLabel, Value, Features, DatasetDict
import json
from pathlib import Path
# load data
ds = {}
path = Path("<path to folder>")
for i in path.rglob("*.jsonl"):
print(i)
name = str(i).split("/")[0].lower()
dtype = str(i).split("/")[1].lower()
# read data
with i.open("r") as fl:
df = pd.DataFrame([json.loads(line) for line in fl])
# select columns
df = df.loc[:, ["sentence1", "sentence2", "gold_label"]]
# add file name as column
df["dtype"] = dtype
# encode labels
df["label"] = df["gold_label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
ds[name] = df
# cast to dataset
features = Features(
{
"sentence1": Value(dtype="string"),
"sentence2": Value(dtype="string"),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
"dtype": Value(dtype="string"),
"gold_label": Value(dtype="string"),
}
)
ds = DatasetDict({k: Dataset.from_pandas(v, features=features) for k, v in ds.items()})
ds.push_to_hub("pietrolesci/stress_tests_nli", token="<token>")
# check overlap between splits
from itertools import combinations
for i, j in combinations(ds.keys(), 2):
print(
f"{i} - {j}: ",
pd.merge(
ds[i].to_pandas(),
ds[j].to_pandas(),
on=["sentence1", "sentence2", "label"],
how="inner",
).shape[0],
)
#> numerical_reasoning - negation: 0
#> numerical_reasoning - length_mismatch: 0
#> numerical_reasoning - spelling_error: 0
#> numerical_reasoning - word_overlap: 0
#> numerical_reasoning - antonym: 0
#> negation - length_mismatch: 0
#> negation - spelling_error: 0
#> negation - word_overlap: 0
#> negation - antonym: 0
#> length_mismatch - spelling_error: 0
#> length_mismatch - word_overlap: 0
#> length_mismatch - antonym: 0
#> spelling_error - word_overlap: 0
#> spelling_error - antonym: 0
#> word_overlap - antonym: 0
``` | 2,622 | [
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] |
pierreguillou/DocLayNet-base | 2023-05-17T08:56:30.000Z | [
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_categories:token-classification",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"size_categories:1K<n<10K",
"language:en",
"language:de",
"language:fr",
"language:ja",
"license:other",
"DocLayNet",
"COCO",
"PDF",
"IBM",
"Financial-Reports",
"Finance",
"Manuals",
"Scientific-Articles",
"Science",
"Laws",
"Law",
"Regulations",
"Patents",
"Government-Tenders",
"object-detection",
"image-segmentation",
"token-classification",
"arxiv:2206.01062",
"region:us"
] | pierreguillou | Accurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present \textit{DocLayNet}, a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide smallline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10\% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNet-trained models are more robust and thus the preferred choice for general-purpose document-layout analysis. | @article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},
doi = {10.1145/3534678.353904},
url = {https://arxiv.org/abs/2206.01062},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022}
} | 7 | 306 | 2023-01-25T17:53:26 | ---
language:
- en
- de
- fr
- ja
annotations_creators:
- crowdsourced
license: other
pretty_name: DocLayNet base
size_categories:
- 1K<n<10K
tags:
- DocLayNet
- COCO
- PDF
- IBM
- Financial-Reports
- Finance
- Manuals
- Scientific-Articles
- Science
- Laws
- Law
- Regulations
- Patents
- Government-Tenders
- object-detection
- image-segmentation
- token-classification
task_categories:
- object-detection
- image-segmentation
- token-classification
task_ids:
- instance-segmentation
---
# Dataset Card for DocLayNet base
## About this card (01/27/2023)
### Property and license
All information from this page but the content of this paragraph "About this card (01/27/2023)" has been copied/pasted from [Dataset Card for DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet).
DocLayNet is a dataset created by Deep Search (IBM Research) published under [license CDLA-Permissive-1.0](https://huggingface.co/datasets/ds4sd/DocLayNet#licensing-information).
I do not claim any rights to the data taken from this dataset and published on this page.
### DocLayNet dataset
[DocLayNet dataset](https://github.com/DS4SD/DocLayNet) (IBM) provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories.
Until today, the dataset can be downloaded through direct links or as a dataset from Hugging Face datasets:
- direct links: [doclaynet_core.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip) (28 GiB), [doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip) (7.5 GiB)
- Hugging Face dataset library: [dataset DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet)
Paper: [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://arxiv.org/abs/2206.01062) (06/02/2022)
### Processing into a format facilitating its use by HF notebooks
These 2 options require the downloading of all the data (approximately 30GBi), which requires downloading time (about 45 mn in Google Colab) and a large space on the hard disk. These could limit experimentation for people with low resources.
Moreover, even when using the download via HF datasets library, it is necessary to download the EXTRA zip separately ([doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip), 7.5 GiB) to associate the annotated bounding boxes with the text extracted by OCR from the PDFs. This operation also requires additional code because the boundings boxes of the texts do not necessarily correspond to those annotated (a calculation of the percentage of area in common between the boundings boxes annotated and those of the texts makes it possible to make a comparison between them).
At last, in order to use Hugging Face notebooks on fine-tuning layout models like LayoutLMv3 or LiLT, DocLayNet data must be processed in a proper format.
For all these reasons, I decided to process the DocLayNet dataset:
- into 3 datasets of different sizes:
- [DocLayNet small](https://huggingface.co/datasets/pierreguillou/DocLayNet-small) (about 1% of DocLayNet) < 1.000k document images (691 train, 64 val, 49 test)
- [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) (about 10% of DocLayNet) < 10.000k document images (6910 train, 648 val, 499 test)
- [DocLayNet large](https://huggingface.co/datasets/pierreguillou/DocLayNet-large) (about 100% of DocLayNet) < 100.000k document images (69.103 train, 6.480 val, 4.994 test)
- with associated texts and PDFs (base64 format),
- and in a format facilitating their use by HF notebooks.
*Note: the layout HF notebooks will greatly help participants of the IBM [ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents](https://ds4sd.github.io/icdar23-doclaynet/)!*
### About PDFs languages
Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062):
"We did not control the document selection with regard to language. **The vast majority of documents contained in DocLayNet (close to 95%) are published in English language.** However, **DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%)**. While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."
### About PDFs categories distribution
Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062):
"The pages in DocLayNet can be grouped into **six distinct categories**, namely **Financial Reports, Manuals, Scientific Articles, Laws & Regulations, Patents and Government Tenders**. Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories (Financial Reports and Manuals) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."
### Download & overview
The size of the DocLayNet small is about 10% of the DocLayNet dataset (random selection respectively in the train, val and test files).
```
# !pip install -q datasets
from datasets import load_dataset
dataset_base = load_dataset("pierreguillou/DocLayNet-base")
# overview of dataset_base
DatasetDict({
train: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 6910
})
validation: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 648
})
test: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 499
})
})
```
### Annotated bounding boxes
The DocLayNet base makes easy to display document image with the annotaed bounding boxes of paragraphes or lines.
Check the notebook [processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb](https://github.com/piegu/language-models/blob/master/processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb) in order to get the code.
#### Paragraphes
#### Lines
### HF notebooks
- [notebooks LayoutLM](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLM) (Niels Rogge)
- [notebooks LayoutLMv2](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv2) (Niels Rogge)
- [notebooks LayoutLMv3](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3) (Niels Rogge)
- [notebooks LiLT](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT) (Niels Rogge)
- [Document AI: Fine-tuning LiLT for document-understanding using Hugging Face Transformers](https://github.com/philschmid/document-ai-transformers/blob/main/training/lilt_funsd.ipynb) ([post](https://www.philschmid.de/fine-tuning-lilt#3-fine-tune-and-evaluate-lilt) of Phil Schmid)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/
- **Repository:** https://github.com/DS4SD/DocLayNet
- **Paper:** https://doi.org/10.1145/3534678.3539043
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail.
4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.
### Supported Tasks and Leaderboards
We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/.
## Dataset Structure
### Data Fields
DocLayNet provides four types of data assets:
1. PNG images of all pages, resized to square `1025 x 1025px`
2. Bounding-box annotations in COCO format for each PNG image
3. Extra: Single-page PDF files matching each PNG image
4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content
The COCO image record are defined like this example
```js
...
{
"id": 1,
"width": 1025,
"height": 1025,
"file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",
// Custom fields:
"doc_category": "financial_reports" // high-level document category
"collection": "ann_reports_00_04_fancy", // sub-collection name
"doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
"page_no": 9, // page number in original document
"precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
},
...
```
The `doc_category` field uses one of the following constants:
```
financial_reports,
scientific_articles,
laws_and_regulations,
government_tenders,
manuals,
patents
```
### Data Splits
The dataset provides three splits
- `train`
- `val`
- `test`
## Dataset Creation
### Annotations
#### Annotation process
The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf).
#### Who are the annotators?
Annotations are crowdsourced.
## Additional Information
### Dataset Curators
The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
You can contact us at [deepsearch-core@zurich.ibm.com](mailto:deepsearch-core@zurich.ibm.com).
Curators:
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
### Licensing Information
License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)
### Citation Information
```bib
@article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
doi = {10.1145/3534678.353904},
url = {https://doi.org/10.1145/3534678.3539043},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {3743–3751},
numpages = {9},
location = {Washington DC, USA},
series = {KDD '22}
}
```
### Contributions
Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset. | 13,860 | [
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RIPS-Goog-23/RVL-CDIP | 2023-06-29T06:25:59.000Z | [
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open-source-metrics/stars | 2023-09-06T18:46:39.000Z | [
"region:us"
] | open-source-metrics | null | null | 0 | 305 | 2023-03-23T12:51:59 | ---
dataset_info:
features:
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dtype: string
- name: dates
dtype: string
splits:
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- name: transformers
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- name: safetensors
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download_size: 5048468
dataset_size: 8307226
configs:
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data_files:
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path: data/peft-*
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path: data/hub_docs-*
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path: data/evaluate-*
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path: data/huggingface_hub-*
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path: data/accelerate-*
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path: data/datasets-*
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path: data/optimum-*
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path: data/pytorch_image_models-*
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path: data/gradio-*
- split: tokenizers
path: data/tokenizers-*
- split: diffusers
path: data/diffusers-*
- split: transformers
path: data/transformers-*
- split: safetensors
path: data/safetensors-*
---
# Dataset Card for "stars"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,868 | [
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THUDM/webglm-qa | 2023-07-12T17:14:35.000Z | [
"task_categories:text-generation",
"task_categories:question-answering",
"multilinguality:monolingual",
"size_categories:100M<n<200M",
"language:en",
"arxiv:2306.07906",
"region:us"
] | THUDM | null | null | 19 | 305 | 2023-07-11T16:59:04 | ---
annotations_creators: []
language:
- en
multilinguality:
- monolingual
source_datasets: []
task_categories:
- text-generation
- question-answering
pretty_name: WebGLM-QA
size_categories:
- 100M<n<200M
---
# WebGLM-QA
## Dataset Description
[WebGLM-QA](https://github.com/THUDM/WebGLM) is the dataset used to train the WebGLM generator module. It consists of 43,579 high-quality data samples for the train split, 1,000 for the validation split, and 400 for the test split. Refer to [our paper](https://arxiv.org/abs/2306.07906) for the data construction details.
## Dataset Structure
To load the dataset, you can try the following code.
```python
from datasets import load_dataset
load_dataset("THUDM/webglm-qa")
DatasetDict({
train: Dataset({
features: ['question', 'answer', 'references'],
num_rows: 43579
})
test: Dataset({
features: ['question', 'answer', 'references'],
num_rows: 400
})
validation: Dataset({
features: ['question', 'answer', 'references'],
num_rows: 1000
})
})
```
```python
next(iter(data["test"]))
{'question': 'Just got my (Canadian) mortgage renewal notice telling me I have to chose Subsequent Payment Terms.', 'answer': "When renewing a mortgage in Canada, your lender must notify you in advance of the renewal date with your options for renewal terms[1][2]. Your mortgage will typically automatically renew or become in default if you don't take action[3]. Depending on your lender, you may be able to renew your mortgage as early as 6 months prior to your current mortgage term expiring[2][3][5]. RBC Royal Bank mortgage customers can choose Subsequent Payment Terms and be protected from an increase in interest rates for the interest type and term they selected[4].", 'references': ['When faced with a mortgage renewal, this simply means that your current contracted mortgage term is approaching its expiration date. You see, the majority of mortgages in Toronto and in general mortgages in Ontario are contracted for a finite period of time that is referred to as the “mortgage term”. This period tends to range from as little as a few months to as long as 10 years in Canada.', 'You can either proactively reach out to your lender several months prior to your renewal date to find out, but if you don’t, your lender must notify you in advance of the renewal date what your options are. If your mortgage does happen to be with a federally regulated bank, then they are obligated to send you an official renewal statement with no less than 21 days remaining on your current mortgage term. Also, if your lender chooses not to renew your mortgage then they must notify you in advance and provide you with enough time to refinance your mortgage elsewhere or to pay it off.', 'When it comes to mortgage renewals, if you do not take action your mortgage will in many cases either renew automatically or become in default. When your mortgage term approaches the end, your mortgage lender will typically offer you renewal terms that you may choose to accept, negotiate, or decline. Provided you continue to make your monthly mortgage payments on time, lenders will rarely not extend to you an offer to renew your mortgage, although this can happen without cause depending on your mortgage commitment and contract.', "When you renew your RBC Royal Bank mortgage at maturity, you are protected from an increase in interest rates, for the interest type and term you selected, in the 30-day period prior to your regularly scheduled renewal date. And, if the interest rate changes before your actual mortgage renewal date, you'll automatically receive the lower rate for the term and type you chose.", 'When renewing your mortgage in Canada, some lenders may allow you to renew your mortgage as early as 6 months prior to your current mortgage term expiring.']}
```
## Data Fields
* ``question``: a question raised by a user or individual related to a certain topic.
* ``answer``: the generated response to the question.
* ``references``: a list of quotes or snippets from sources used to generate the answer given.
## Data Splits
We split the dataset into train, validation, and test.
## Citation Information
Refer to https://github.com/THUDM/WebGLM. | 4,264 | [
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] |
result-kand2-sdxl-wuerst-karlo/ac298fb2 | 2023-10-04T23:40:46.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 305 | 2023-10-04T23:40:45 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 165
num_examples: 10
download_size: 1316
dataset_size: 165
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ac298fb2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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code_x_glue_cc_cloze_testing_maxmin | 2023-06-01T14:59:51.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:code",
"license:c-uda",
"region:us"
] | null | Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
Here we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. Each instance in the dataset contains a masked code function, its docstring and the target word.
The only difference between ClozeTest-maxmin and ClozeTest-all is their selected words sets, where ClozeTest-maxmin only contains two words while ClozeTest-all contains 930 words. | @article{CodeXGLUE,
title={CodeXGLUE: An Open Challenge for Code Intelligence},
journal={arXiv},
year={2020},
}
@article{feng2020codebert,
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others},
journal={arXiv preprint arXiv:2002.08155},
year={2020}
}
@article{husain2019codesearchnet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
} | 1 | 304 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- slot-filling
pretty_name: CodeXGlueCcClozeTestingMaxmin
dataset_info:
- config_name: go
features:
- name: id
dtype: int32
- name: idx
dtype: string
- name: nl_tokens
sequence: string
- name: pl_tokens
sequence: string
splits:
- name: train
num_bytes: 204997
num_examples: 152
download_size: 298893
dataset_size: 204997
- config_name: java
features:
- name: id
dtype: int32
- name: idx
dtype: string
- name: nl_tokens
sequence: string
- name: pl_tokens
sequence: string
splits:
- name: train
num_bytes: 785754
num_examples: 482
download_size: 1097733
dataset_size: 785754
- config_name: javascript
features:
- name: id
dtype: int32
- name: idx
dtype: string
- name: nl_tokens
sequence: string
- name: pl_tokens
sequence: string
splits:
- name: train
num_bytes: 594347
num_examples: 272
download_size: 836112
dataset_size: 594347
- config_name: php
features:
- name: id
dtype: int32
- name: idx
dtype: string
- name: nl_tokens
sequence: string
- name: pl_tokens
sequence: string
splits:
- name: train
num_bytes: 705477
num_examples: 407
download_size: 1010305
dataset_size: 705477
- config_name: python
features:
- name: id
dtype: int32
- name: idx
dtype: string
- name: nl_tokens
sequence: string
- name: pl_tokens
sequence: string
splits:
- name: train
num_bytes: 2566353
num_examples: 1264
download_size: 3577929
dataset_size: 2566353
- config_name: ruby
features:
- name: id
dtype: int32
- name: idx
dtype: string
- name: nl_tokens
sequence: string
- name: pl_tokens
sequence: string
splits:
- name: train
num_bytes: 48946
num_examples: 38
download_size: 67675
dataset_size: 48946
config_names:
- go
- java
- javascript
- php
- python
- ruby
---
# Dataset Card for "code_x_glue_cc_cloze_testing_maxmin"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits-sample-size)
- [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/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-maxmin
### Dataset Summary
CodeXGLUE ClozeTesting-maxmin dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-maxmin
Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
Here we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. Each instance in the dataset contains a masked code function, its docstring and the target word.
The only difference between ClozeTest-maxmin and ClozeTest-all is their selected words sets, where ClozeTest-maxmin only contains two words while ClozeTest-all contains 930 words.
### Supported Tasks and Leaderboards
- `slot-filling`: The dataset can be used to train a model for predicting the missing token from a piece of code, similar to the Cloze test.
### Languages
- Go **programming** language
- Java **programming** language
- Javascript **programming** language
- PHP **programming** language
- Python **programming** language
- Ruby **programming** language
## Dataset Structure
### Data Instances
#### go
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "maxmin-1",
"nl_tokens": ["SetMaxStructPoolSize", "sets", "the", "struct", "pools", "max", "size", ".", "this", "may", "be", "usefull", "for", "fine", "grained", "performance", "tuning", "towards", "your", "application", "however", "the", "default", "should", "be", "fine", "for", "nearly", "all", "cases", ".", "only", "increase", "if", "you", "have", "a", "deeply", "nested", "struct", "structure", ".", "NOTE", ":", "this", "method", "is", "not", "thread", "-", "safe", "NOTE", ":", "this", "is", "only", "here", "to", "keep", "compatibility", "with", "v5", "in", "v6", "the", "method", "will", "be", "removed"],
"pl_tokens": ["func", "(", "v", "*", "Validate", ")", "SetMaxStructPoolSize", "(", "<mask>", "int", ")", "{", "structPool", "=", "&", "sync", ".", "Pool", "{", "New", ":", "newStructErrors", "}", "\n", "}"]
}
```
#### java
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "maxmin-1",
"nl_tokens": ["Test", "whether", "find", "can", "be", "found", "at", "position", "startPos", "in", "the", "string", "src", "."],
"pl_tokens": ["public", "static", "boolean", "startsWith", "(", "char", "[", "]", "src", ",", "char", "[", "]", "find", ",", "int", "startAt", ")", "{", "int", "startPos", "=", "startAt", ";", "boolean", "result", "=", "true", ";", "// Check ranges", "if", "(", "src", ".", "length", "<", "startPos", "+", "find", ".", "length", ")", "{", "result", "=", "false", ";", "}", "else", "{", "final", "int", "<mask>", "=", "find", ".", "length", ";", "for", "(", "int", "a", "=", "0", ";", "a", "<", "max", "&&", "result", ";", "a", "++", ")", "{", "if", "(", "src", "[", "startPos", "]", "!=", "find", "[", "a", "]", ")", "{", "result", "=", "false", ";", "}", "startPos", "++", ";", "}", "}", "return", "result", ";", "}"]
}
```
#### javascript
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "maxmin-1",
"nl_tokens": ["string", ".", "max", "Maximum", "length", "of", "the", "string"],
"pl_tokens": ["function", "(", "string", ")", "{", "// string.check check sting type and size", "return", "(", "(", "typeof", "string", "===", "'string'", "||", "string", "instanceof", "String", ")", "&&", "string", ".", "length", ">=", "this", ".", "<mask>", "&&", "string", ".", "length", "<=", "this", ".", "max", "&&", "(", "!", "this", ".", "match", "||", "string", ".", "match", "(", "this", ".", "match", ")", ")", ")", ";", "}"]
}
```
#### php
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "maxmin-1",
"nl_tokens": ["Read", "the", "next", "character", "from", "the", "supplied", "string", ".", "Return", "null", "when", "we", "have", "run", "out", "of", "characters", "."],
"pl_tokens": ["public", "function", "readOne", "(", ")", "{", "if", "(", "$", "this", "->", "pos", "<=", "$", "this", "->", "<mask>", ")", "{", "$", "value", "=", "$", "this", "->", "string", "[", "$", "this", "->", "pos", "]", ";", "$", "this", "->", "pos", "+=", "1", ";", "}", "else", "{", "$", "value", "=", "null", ";", "}", "return", "$", "value", ";", "}"]
}
```
#### python
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "maxmin-1",
"nl_tokens": ["Returns", "intermediary", "colors", "for", "given", "list", "of", "colors", "."],
"pl_tokens": ["def", "_interpolate", "(", "self", ",", "colors", ",", "n", "=", "100", ")", ":", "gradient", "=", "[", "]", "for", "i", "in", "_range", "(", "n", ")", ":", "l", "=", "len", "(", "colors", ")", "-", "1", "x", "=", "int", "(", "1.0", "*", "i", "/", "n", "*", "l", ")", "x", "=", "<mask>", "(", "x", "+", "0", ",", "l", ")", "y", "=", "min", "(", "x", "+", "1", ",", "l", ")", "base", "=", "1.0", "*", "n", "/", "l", "*", "x", "d", "=", "(", "i", "-", "base", ")", "/", "(", "1.0", "*", "n", "/", "l", ")", "r", "=", "colors", "[", "x", "]", ".", "r", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "r", "*", "d", "g", "=", "colors", "[", "x", "]", ".", "g", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "g", "*", "d", "b", "=", "colors", "[", "x", "]", ".", "b", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "b", "*", "d", "a", "=", "colors", "[", "x", "]", ".", "a", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "a", "*", "d", "gradient", ".", "append", "(", "color", "(", "r", ",", "g", ",", "b", ",", "a", ",", "mode", "=", "\"rgb\"", ")", ")", "gradient", ".", "append", "(", "colors", "[", "-", "1", "]", ")", "return", "gradient"]
}
```
#### ruby
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "maxmin-1",
"nl_tokens": ["Delete", "all", "copies", "that", "are", "older", "than", "the", "max", "age", "provided", "in", "seconds", "."],
"pl_tokens": ["def", "clean", "(", "<mask>", ":", "24", "*", "60", "*", "60", ")", "Futex", ".", "new", "(", "file", ",", "log", ":", "@log", ")", ".", "open", "do", "list", "=", "load", "list", ".", "reject!", "do", "|", "s", "|", "if", "s", "[", ":time", "]", ">=", "Time", ".", "now", "-", "max", "false", "else", "@log", ".", "debug", "(", "\"Copy ##{s[:name]}/#{s[:host]}:#{s[:port]} is too old, over #{Age.new(s[:time])}\"", ")", "true", "end", "end", "save", "(", "list", ")", "deleted", "=", "0", "files", ".", "each", "do", "|", "f", "|", "next", "unless", "list", ".", "find", "{", "|", "s", "|", "s", "[", ":name", "]", "==", "File", ".", "basename", "(", "f", ",", "Copies", "::", "EXT", ")", "}", ".", "nil?", "file", "=", "File", ".", "join", "(", "@dir", ",", "f", ")", "size", "=", "File", ".", "size", "(", "file", ")", "File", ".", "delete", "(", "file", ")", "@log", ".", "debug", "(", "\"Copy at #{f} deleted: #{Size.new(size)}\"", ")", "deleted", "+=", "1", "end", "list", ".", "select!", "do", "|", "s", "|", "cp", "=", "File", ".", "join", "(", "@dir", ",", "\"#{s[:name]}#{Copies::EXT}\"", ")", "wallet", "=", "Wallet", ".", "new", "(", "cp", ")", "begin", "wallet", ".", "refurbish", "raise", "\"Invalid protocol #{wallet.protocol} in #{cp}\"", "unless", "wallet", ".", "protocol", "==", "Zold", "::", "PROTOCOL", "true", "rescue", "StandardError", "=>", "e", "FileUtils", ".", "rm_rf", "(", "cp", ")", "@log", ".", "debug", "(", "\"Copy at #{cp} deleted: #{Backtrace.new(e)}\"", ")", "deleted", "+=", "1", "false", "end", "end", "save", "(", "list", ")", "deleted", "end", "end"]
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### go, java, javascript, php, python, ruby
|field name| type | description |
|----------|----------------|------------------------------|
|id |int32 | Index of the sample |
|idx |string | Original index in the dataset|
|nl_tokens |Sequence[string]| Natural language tokens |
|pl_tokens |Sequence[string]| Programming language tokens |
### Data Splits
| name |train|
|----------|----:|
|go | 152|
|java | 482|
|javascript| 272|
|php | 407|
|python | 1264|
|ruby | 38|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Data from CodeSearchNet Challenge dataset.
[More Information Needed]
#### Who are the source language producers?
Software Engineering developers.
### 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
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@article{CodeXGLUE,
title={CodeXGLUE: An Open Challenge for Code Intelligence},
journal={arXiv},
year={2020},
}
@article{feng2020codebert,
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others},
journal={arXiv preprint arXiv:2002.08155},
year={2020}
}
@article{husain2019codesearchnet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. | 13,565 | [
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aboonaji/alpaca_micro_demo | 2023-08-08T13:57:18.000Z | [
"region:us"
] | aboonaji | null | null | 0 | 304 | 2023-08-08T13:00:10 | Entry not found | 15 | [
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] |
inkoziev/jokes_dialogues | 2023-02-19T07:07:16.000Z | [
"task_categories:conversational",
"language:ru",
"license:cc-by-nc-4.0",
"region:us"
] | inkoziev | null | null | 1 | 303 | 2023-02-18T11:59:12 | ---
license: cc-by-nc-4.0
task_categories:
- conversational
language:
- ru
---
# Диалоги из анекдотов и шуток
Датасет содержит результат парсинга анекдотов, наскрапленных с разных сайтов.
## Формат
Каждый сэмпл содержит четыре поля:
"context" - контекст диалога, включая все недиалоговые вставки. Обратите внимание, что контекст содержит как предшествующие реплики, так и прочий сопутствующий текст, так
как он определяет общий сеттинг, необходимый для генерации реплики. Из реплики удалены маркеры косвенной речи.
"utterance" - диалоговая реплика.
"hash" - хэш-код исходного полного текста для связывания сэмплов.
"reply_num" - порядковый номер диалоговой реплики. Часто последняя реплика является "пайнчалайном", в ней сконцентрирована суть шутки.
Один исходный текст может дать несколько сэмплов, если в нем было много реплик. | 839 | [
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hate_offensive | 2023-01-25T14:31:32.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"hate-speech-detection",
"arxiv:1905.12516",
"region:us"
] | null | null | @article{article,
author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
year = {2017},
month = {03},
pages = {},
title = {Automated Hate Speech Detection and the Problem of Offensive Language}
} | 6 | 301 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: hate-speech-and-offensive-language
pretty_name: HateOffensive
tags:
- hate-speech-detection
dataset_info:
features:
- name: total_annotation_count
dtype: int32
- name: hate_speech_annotations
dtype: int32
- name: offensive_language_annotations
dtype: int32
- name: neither_annotations
dtype: int32
- name: label
dtype:
class_label:
names:
'0': hate-speech
'1': offensive-language
'2': neither
- name: tweet
dtype: string
splits:
- name: train
num_bytes: 2811298
num_examples: 24783
download_size: 2546446
dataset_size: 2811298
---
# Dataset Card for HateOffensive
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage** : https://arxiv.org/abs/1905.12516
- **Repository** : https://github.com/t-davidson/hate-speech-and-offensive-language
- **Paper** : https://arxiv.org/abs/1905.12516
- **Leaderboard** :
- **Point of Contact** : trd54 at cornell dot edu
### Dataset Summary
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`)
## Dataset Structure
### Data Instances
```
{
"count": 3,
"hate_speech_annotation": 0,
"offensive_language_annotation": 0,
"neither_annotation": 3,
"label": 2, # "neither"
"tweet": "!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. & as a man you should always take the trash out...")
}
```
### Data Fields
count: (Integer) number of users who coded each tweet (min is 3, sometimes more users coded a tweet when judgments were determined to be unreliable,
hate_speech_annotation: (Integer) number of users who judged the tweet to be hate speech,
offensive_language_annotation: (Integer) number of users who judged the tweet to be offensive,
neither_annotation: (Integer) number of users who judged the tweet to be neither offensive nor non-offensive,
label: (Class Label) integer class label for majority of CF users (0: 'hate-speech', 1: 'offensive-language' or 2: 'neither'),
tweet: (string)
### Data Splits
This dataset is not splitted, only the train split is available.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
Usernames are not anonymized in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
MIT License
### Citation Information
@inproceedings{hateoffensive,
title = {Automated Hate Speech Detection and the Problem of Offensive Language},
author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media},
series = {ICWSM '17},
year = {2017},
location = {Montreal, Canada},
pages = {512-515}
}
### Contributions
Thanks to [@MisbahKhan789](https://github.com/MisbahKhan789) for adding this dataset. | 4,719 | [
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AlekseyKorshuk/hellaswag | 2022-06-06T10:33:23.000Z | [
"region:us"
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ds4sd/DocLayNet | 2023-01-25T17:01:19.000Z | [
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"size_categories:10K<n<100K",
"license:other",
"layout-segmentation",
"COCO",
"document-understanding",
"PDF",
"region:us"
] | ds4sd | DocLayNet is a human-annotated document layout segmentation dataset from a broad variety of document sources. | @article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},
doi = {10.1145/3534678.353904},
url = {https://arxiv.org/abs/2206.01062},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022}
} | 24 | 301 | 2023-01-17T07:51:59 | ---
annotations_creators:
- crowdsourced
license: other
pretty_name: DocLayNet
size_categories:
- 10K<n<100K
tags:
- layout-segmentation
- COCO
- document-understanding
- PDF
task_categories:
- object-detection
- image-segmentation
task_ids:
- instance-segmentation
---
# Dataset Card for DocLayNet
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/
- **Repository:** https://github.com/DS4SD/DocLayNet
- **Paper:** https://doi.org/10.1145/3534678.3539043
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail.
4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.
### Supported Tasks and Leaderboards
We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/.
## Dataset Structure
### Data Fields
DocLayNet provides four types of data assets:
1. PNG images of all pages, resized to square `1025 x 1025px`
2. Bounding-box annotations in COCO format for each PNG image
3. Extra: Single-page PDF files matching each PNG image
4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content
The COCO image record are defined like this example
```js
...
{
"id": 1,
"width": 1025,
"height": 1025,
"file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",
// Custom fields:
"doc_category": "financial_reports" // high-level document category
"collection": "ann_reports_00_04_fancy", // sub-collection name
"doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
"page_no": 9, // page number in original document
"precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
},
...
```
The `doc_category` field uses one of the following constants:
```
financial_reports,
scientific_articles,
laws_and_regulations,
government_tenders,
manuals,
patents
```
### Data Splits
The dataset provides three splits
- `train`
- `val`
- `test`
## Dataset Creation
### Annotations
#### Annotation process
The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf).
#### Who are the annotators?
Annotations are crowdsourced.
## Additional Information
### Dataset Curators
The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
You can contact us at [deepsearch-core@zurich.ibm.com](mailto:deepsearch-core@zurich.ibm.com).
Curators:
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
### Licensing Information
License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)
### Citation Information
```bib
@article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
doi = {10.1145/3534678.353904},
url = {https://doi.org/10.1145/3534678.3539043},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {3743–3751},
numpages = {9},
location = {Washington DC, USA},
series = {KDD '22}
}
```
### Contributions
Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset.
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vietgpt/openbookqa_en | 2023-06-03T22:16:08.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"SFT",
"region:us"
] | vietgpt | null | null | 0 | 301 | 2023-06-03T22:08:45 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question_stem
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 895386
num_examples: 4957
- name: validation
num_bytes: 95428
num_examples: 500
- name: test
num_bytes: 91759
num_examples: 500
download_size: 609610
dataset_size: 1082573
task_categories:
- text-classification
language:
- en
tags:
- SFT
size_categories:
- 1K<n<10K
---
# OpenBookQA
- Source: https://huggingface.co/datasets/openbookqa
- Num examples:
- 4,957 (train)
- 500 (validation)
- 500 (test)
- Language: English
```python
from datasets import load_dataset
load_dataset("vietgpt/piqa_en")
```
- Format for GPT-3
```python
def preprocess_gpt3(sample):
question_stem = sample['question_stem']
choices = sample['choices']['text']
answerKey = sample['answerKey']
if answerKey == 'A':
output = f'\n<|correct|> {choices[0]}\n<|incorrect|> {choices[1]}\n<|incorrect|> {choices[2]}\n<|incorrect|> {choices[3]}'
elif answerKey == 'B':
output = f'\n<|correct|> {choices[1]}\n<|incorrect|> {choices[0]}\n<|incorrect|> {choices[2]}\n<|incorrect|> {choices[3]}'
elif answerKey == 'C':
output = f'\n<|correct|> {choices[2]}\n<|incorrect|> {choices[0]}\n<|incorrect|> {choices[1]}\n<|incorrect|> {choices[3]}'
else:
output = f'\n<|correct|> {choices[3]}\n<|incorrect|> {choices[0]}\n<|incorrect|> {choices[1]}\n<|incorrect|> {choices[2]}'
return {'text': f'<|startoftext|><|context|> {question_stem} <|answer|> {output} <|endoftext|>'}
"""
<|startoftext|><|context|> The sun is responsible for <|answer|>
<|correct|> plants sprouting, blooming and wilting
<|incorrect|> puppies learning new tricks
<|incorrect|> children growing up and getting old
<|incorrect|> flowers wilting in a vase <|endoftext|>
"""
``` | 1,998 | [
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spacemanidol/dset-corpus | 2023-09-27T19:17:42.000Z | [
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] | spacemanidol | null | 0 | 301 | 2023-09-21T18:52:00 | Entry not found | 15 | [
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laion/laion1B-nolang-aesthetic | 2022-05-22T13:40:12.000Z | [
"region:us"
] | laion | null | null | 0 | 300 | 2022-05-22T12:34:57 | Entry not found | 15 | [
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shmuhammad/AfriSenti-twitter-sentiment | 2023-09-03T09:59:15.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"language:amh",
"language:ary",
"language:ar",
"language:arq",
"language:hau",
"language:ibo",
"language:kin",
"language:por",
"language:pcm",
"language:eng",
"language:oro",
"language:swa",
"language:tir",
"language:twi",
"language:tso",
"language:yor",
"sentiment analysis, Twitter, tweets",
"sentiment",
"arxiv:2302.08956",
"arxiv:2304.06845",
"arxiv:2201.08277",
"region:us"
] | shmuhammad | AfriSenti is the largest sentiment analysis benchmark dataset for under-represented African languages---covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and yoruba). | @inproceedings{muhammad-etal-2023-semeval,
title="{S}em{E}val-2023 Task 12: Sentiment Analysis for African Languages ({A}fri{S}enti-{S}em{E}val)",
author="Muhammad, Shamsuddeen Hassan and
Yimam, Seid and
Abdulmumin, Idris and
Ahmad, Ibrahim Sa'id and
Ousidhoum, Nedjma, and
Ayele, Abinew, and
Adelani, David and
Ruder, Sebastian and
Beloucif, Meriem and
Bello, Shehu Bello and
Mohammad, Saif M.",
booktitle="Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month=jul,
year="2023",
} | 3 | 300 | 2023-02-16T21:02:20 | ---
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-classification
- sentiment-scoring
- semantic-similarity-classification
- semantic-similarity-scoring
tags:
- sentiment analysis, Twitter, tweets
- sentiment
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
language:
- amh
- ary
- ar
- arq
- hau
- ibo
- kin
- por
- pcm
- eng
- oro
- swa
- tir
- twi
- tso
- yor
pretty_name: AfriSenti
---
# Dataset Card for AfriSenti Dataset
<p align="center">
<img src="https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/images/afrisenti-twitter.png", width="700" height="500">
--------------------------------------------------------------------------------
## Dataset Description
- **Homepage:** https://github.com/afrisenti-semeval/afrisent-semeval-2023
- **Repository:** [GitHub](https://github.com/afrisenti-semeval/afrisent-semeval-2023)
- **Paper:** [AfriSenti: AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://arxiv.org/pdf/2302.08956.pdf)
- **Paper:** [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://arxiv.org/pdf/2304.06845.pdf)
- **Paper:** [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://arxiv.org/pdf/2201.08277.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** [shamsuddeen Muhammad](shamsuddeen2004@gmail.com)
### Dataset Summary
AfriSenti is the largest sentiment analysis dataset for under-represented African languages, covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba).
The datasets are used in the first Afrocentric SemEval shared task, SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval). AfriSenti allows the research community to build sentiment analysis systems for various African languages and enables the study of sentiment and contemporary language use in African languages.
### Supported Tasks and Leaderboards
The AfriSenti can be used for a wide range of sentiment analysis tasks in African languages, such as sentiment classification, sentiment intensity analysis, and emotion detection. This dataset is suitable for training and evaluating machine learning models for various NLP tasks related to sentiment analysis in African languages.
[SemEval 2023 Task 12 : Sentiment Analysis for African Languages](https://codalab.lisn.upsaclay.fr/competitions/7320)
### Languages
14 African languages (Amharic (amh), Algerian Arabic (ary), Hausa(hau), Igbo(ibo), Kinyarwanda(kin), Moroccan Arabic/Darija(arq), Mozambican Portuguese(por), Nigerian Pidgin (pcm), Oromo (oro), Swahili(swa), Tigrinya(tir), Twi(twi), Xitsonga(tso), and Yoruba(yor)).
## Dataset Structure
### Data Instances
For each instance, there is a string for the tweet and a string for the label. See the AfriSenti [dataset viewer](https://huggingface.co/datasets/shmuhammad/AfriSenti/viewer/shmuhammad--AfriSenti/train) to explore more examples.
```
{
"tweet": "string",
"label": "string"
}
```
### Data Fields
The data fields are:
```
tweet: a string feature.
label: a classification label, with possible values including positive, negative and neutral.
```
### Data Splits
The AfriSenti dataset has 3 splits: train, validation, and test. Below are the statistics for Version 1.0.0 of the dataset.
| | ama | arq | hau | ibo | ary | orm | pcm | pt-MZ | kin | swa | tir | tso | twi | yo |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| train | 5,982 | 1,652 | 14,173 | 10,193 | 5,584| - | 5,122 | 3,064 | 3,303 | 1,811 | - | 805 | 3,482| 8,523 |
| dev | 1,498 | 415 | 2,678 | 1,842 | 1,216 | 397 | 1,282 | 768 | 828 | 454 | 399 | 204 | 389 | 2,091 |
| test | 2,000 | 959 | 5,304 | 3,683 | 2,962 | 2,097 | 4,155 | 3,663 | 1,027 | 749 | 2,001 | 255 | 950 | 4,516 |
| total | 9,483 | 3,062 | 22,155 | 15,718 | 9,762 | 2,494 | 10,559 | 7,495 | 5,158 | 3,014 | 2,400 | 1,264 | 4,821 | 15,130 |
### How to use it
```python
from datasets import load_dataset
# you can load specific languages (e.g., Amharic). This download train, validation and test sets.
ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh")
# train set only
ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "train")
# test set only
ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "test")
# validation set only
ds = load_dataset("shmuhammad/AfriSenti-twitter-sentiment", "amh", split = "validation")
```
## Dataset Creation
### Curation Rationale
AfriSenti Version 1.0.0 aimed to be used in the first Afrocentric SemEval shared task **[SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval)](https://afrisenti-semeval.github.io)**.
### Source Data
Twitter
#### 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
We anonymized the tweets by replacing all *@mentions* by *@user* and removed all URLs.
## Considerations for Using the Data
### Social Impact of Dataset
The Afrisenti dataset has the potential to improve sentiment analysis for African languages, which is essential for understanding and analyzing the diverse perspectives of people in the African continent. This dataset can enable researchers and developers to create sentiment analysis models that are specific to African languages, which can be used to gain insights into the social, cultural, and political views of people in African countries. Furthermore, this dataset can help address the issue of underrepresentation of African languages in natural language processing, paving the way for more equitable and inclusive AI technologies.
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
AfriSenti is an extension of NaijaSenti, a dataset consisting of four Nigerian languages: Hausa, Yoruba, Igbo, and Nigerian-Pidgin. This dataset has been expanded to include other 10 African languages, and was curated with the help of the following:
| Language | Dataset Curators |
|---|---|
| Algerian Arabic (arq) | Nedjma Ousidhoum, Meriem Beloucif |
| Amharic (ama) | Abinew Ali Ayele, Seid Muhie Yimam |
| Hausa (hau) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
| Igbo (ibo) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
| Kinyarwanda (kin)| Samuel Rutunda |
| Moroccan Arabic/Darija (ary) | Oumaima Hourrane |
| Mozambique Portuguese (pt-MZ) | Felermino Dário Mário António Ali |
| Nigerian Pidgin (pcm) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
| Oromo (orm) | Abinew Ali Ayele, Seid Muhie Yimam, Hagos Tesfahun Gebremichael, Sisay Adugna Chala, Hailu Beshada Balcha, Wendimu Baye Messell, Tadesse Belay |
| Swahili (swa) | Davis Davis |
| Tigrinya (tir) | Abinew Ali Ayele, Seid Muhie Yimam, Hagos Tesfahun Gebremichael, Sisay Adugna Chala, Hailu Beshada Balcha, Wendimu Baye Messell, Tadesse Belay |
| Twi (twi) | Salomey Osei, Bernard Opoku, Steven Arthur |
| Xithonga (tso) | Felermino Dário Mário António Ali |
| Yoruba (yor) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
### Licensing Information
This AfriSenti is licensed under a Creative Commons Attribution 4.0 International License
### Citation Information
```
@inproceedings{Muhammad2023AfriSentiAT,
title={AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages},
author={Shamsuddeen Hassan Muhammad and Idris Abdulmumin and Abinew Ali Ayele and Nedjma Ousidhoum and David Ifeoluwa Adelani and Seid Muhie Yimam and Ibrahim Sa'id Ahmad and Meriem Beloucif and Saif Mohammad and Sebastian Ruder and Oumaima Hourrane and Pavel Brazdil and Felermino D'ario M'ario Ant'onio Ali and Davis Davis and Salomey Osei and Bello Shehu Bello and Falalu Ibrahim and Tajuddeen Gwadabe and Samuel Rutunda and Tadesse Belay and Wendimu Baye Messelle and Hailu Beshada Balcha and Sisay Adugna Chala and Hagos Tesfahun Gebremichael and Bernard Opoku and Steven Arthur},
year={2023}
}
```
```
@article{muhammad2023semeval,
title={SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)},
author={Muhammad, Shamsuddeen Hassan and Abdulmumin, Idris and Yimam, Seid Muhie and Adelani, David Ifeoluwa and Ahmad, Ibrahim Sa'id and Ousidhoum, Nedjma and Ayele, Abinew and Mohammad, Saif M and Beloucif, Meriem},
journal={arXiv preprint arXiv:2304.06845},
year={2023}
}
```
### Contributions
[More Information Needed] | 9,115 | [
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masakhane/afriqa | 2023-07-07T16:57:28.000Z | [
"task_categories:question-answering",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"language:bem",
"language:fon",
"language:ha",
"language:ig",
"language:kin",
"language:sw",
"language:wo",
"language:yo",
"language:zu",
"language:tw",
"license:cc-by-sa-4.0",
"cross-lingual",
"question-answering",
"qa",
"arxiv:2305.06897",
"region:us"
] | masakhane | AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages
AfriQA is the first cross-lingual question answering (QA) dataset with a focus on African languages.
The dataset includes over 12,000 XOR QA examples across 10 African languages, making it an invaluable resource for developing more equitable QA technology. | \ | 5 | 300 | 2023-04-23T20:05:43 | ---
license: cc-by-sa-4.0
task_categories:
- question-answering
language:
- bem
- fon
- ha
- ig
- kin
- sw
- wo
- yo
- zu
- tw
pretty_name: AfriQA
size_categories:
- 10K<n<100K
multilinguality:
- multilingual
tags:
- cross-lingual
- question-answering
- qa
---
# Dataset Card for AfriQA
## 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 Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [homepage](https://github.com/masakhane-io/afriqa)
- **Repository:** [github](https://github.com/masakhane-io/afriqa)
- **Paper:** [paper]()
- **Point of Contact:** [Masakhane](https://www.masakhane.io/) or oogundep@uwaterloo.ca
### Dataset Summary
AfriQA is the first cross-lingual question answering (QA) dataset with a focus on African languages. The dataset includes over 12,000 XOR QA examples across 10 African languages, making it an invaluable resource for developing more equitable QA technology.
The train/validation/test sets are available for all the 10 languages.
### Supported Tasks and Leaderboards
- `question-answering`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better) and [Exact Match Accuracy](https://huggingface.co/spaces/evaluate-metric/exact_match).
### Languages
There are 20 languages available :
- Bemba (bem)
- Fon (fon)
- Hausa (hau)
- Igbo (ibo)
- Kinyarwanda (kin)
- Swahili (swą)
- Twi (twi)
- Wolof (wol)
- Yorùbá (yor)
- Zulu (zul)
## Dataset Structure
### Data Instances
- Data Format:
- id : Question ID
- question : Question in African Language
- translated_question : Question translated into a pivot language (English/French)
- answers : Answer in African Language
- lang : Datapoint Language (African Language) e.g `bem`
- split : Dataset Split
- translated_answer : Answer in Pivot Language
- translation_type : Translation type of question and answers
```bash
{ "id": 0,
"question": "Bushe icaalo ca Egypt caali tekwapo ne caalo cimbi?",
"translated_question": "Has the country of Egypt been colonized before?",
"answers": "['Emukwai']",
"lang": "bem",
"split": "dev",
"translated_answer": "['yes']",
"translation_type": "human_translation"
}
```
### Data Splits
For all languages, there are three splits.
The original splits were named `train`, `dev` and `test` and they correspond to the `train`, `validation` and `test` splits.
The splits have the following sizes :
| Language | train | dev | test |
|-----------------|------:|-----------:|-----:|
| Bemba | 502 | 503 | 314 |
| Fon | 427 | 428 | 386 |
| Hausa | 435 | 436 | 300 |
| Igbo | 417 | 418 | 409 |
| Kinyarwanda | 407 | 409 | 347 |
| Swahili | 415 | 417 | 302 |
| Twi | 451 | 452 | 490 |
| Wolof | 503 | 504 | 334 |
| Yoruba | 360 | 361 | 332 |
| Zulu | 387 | 388 | 325 |
| <b>Total</b> | <b>4333</b> | <b>4346</b> |<b>3560</b> |
## Dataset Creation
### Curation Rationale
The dataset was introduced to introduce question-answering resources to 10 languages that were under-served for natural language processing.
[More Information Needed]
### Source Data
...
#### Initial Data Collection and Normalization
...
#### Who are the source language producers?
...
### Annotations
#### Annotation process
Details can be found here ...
#### Who are the annotators?
Annotators were recruited from [Masakhane](https://www.masakhane.io/)
### Personal and Sensitive Information
...
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains.
## Additional Information
### Dataset Curators
### Licensing Information
The licensing status of the data is CC 4.0 Non-Commercial
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@misc{ogundepo2023afriqa,
title={AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages},
author={Odunayo Ogundepo and Tajuddeen R. Gwadabe and Clara E. Rivera and Jonathan H. Clark and Sebastian Ruder and David Ifeoluwa Adelani and Bonaventure F. P. Dossou and Abdou Aziz DIOP and Claytone Sikasote and Gilles Hacheme and Happy Buzaaba and Ignatius Ezeani and Rooweither Mabuya and Salomey Osei and Chris Emezue and Albert Njoroge Kahira and Shamsuddeen H. Muhammad and Akintunde Oladipo and Abraham Toluwase Owodunni and Atnafu Lambebo Tonja and Iyanuoluwa Shode and Akari Asai and Tunde Oluwaseyi Ajayi and Clemencia Siro and Steven Arthur and Mofetoluwa Adeyemi and Orevaoghene Ahia and Aremu Anuoluwapo and Oyinkansola Awosan and Chiamaka Chukwuneke and Bernard Opoku and Awokoya Ayodele and Verrah Otiende and Christine Mwase and Boyd Sinkala and Andre Niyongabo Rubungo and Daniel A. Ajisafe and Emeka Felix Onwuegbuzia and Habib Mbow and Emile Niyomutabazi and Eunice Mukonde and Falalu Ibrahim Lawan and Ibrahim Said Ahmad and Jesujoba O. Alabi and Martin Namukombo and Mbonu Chinedu and Mofya Phiri and Neo Putini and Ndumiso Mngoma and Priscilla A. Amuok and Ruqayya Nasir Iro and Sonia Adhiambo},
year={2023},
eprint={2305.06897},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@ToluClassics](https://github.com/ToluClassics) for adding this dataset. | 6,574 | [
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juletxara/pawsx_mt | 2023-07-21T10:18:49.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:multi-input-text-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-paws",
"language:en",
"license:other",
"paraphrase-identification",
"arxiv:1908.11828",
"region:us"
] | juletxara | PAWS-X, a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages.
This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine
translated training pairs in six typologically distinct languages: French, Spanish, German,
Chinese, Japanese, and Korean. English language is available by default. All translated
pairs are sourced from examples in PAWS-Wiki.
For further details, see the accompanying paper: PAWS-X: A Cross-lingual Adversarial Dataset
for Paraphrase Identification (https://arxiv.org/abs/1908.11828)
NOTE: There might be some missing or wrong labels in the dataset and we have replaced them with -1. | @InProceedings{pawsx2019emnlp,
title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
booktitle = {Proc. of EMNLP},
year = {2019}
} | 0 | 299 | 2023-05-23T10:39:03 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- expert-generated
- machine-generated
language:
- en
license:
- other
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-paws
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
- semantic-similarity-scoring
- text-scoring
- multi-input-text-classification
paperswithcode_id: paws-x
pretty_name: 'PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification'
tags:
- paraphrase-identification
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---
# Dataset Card for PAWS-X MT
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx)
- **Repository:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx)
- **Paper:** [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://arxiv.org/abs/1908.11828)
- **Point of Contact:** [Yinfei Yang](yinfeiy@google.com)
### Dataset Summary
This dataset contains 23,659 **human** translated PAWS evaluation pairs and
296,406 **machine** translated training pairs in six typologically distinct
languages: French, Spanish, German, Chinese, Japanese, and Korean. All
translated pairs are sourced from examples in
[PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki).
For further details, see the accompanying paper:
[PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase
Identification](https://arxiv.org/abs/1908.11828)
This is a machine-translated version of the original dataset into English from each langauge.
### Supported Tasks and Leaderboards
It has been majorly used for paraphrase identification for English and other 6 languages namely French, Spanish, German, Chinese, Japanese, and Korean
### Languages
The dataset is in English, French, Spanish, German, Chinese, Japanese, and Korean
## Dataset Structure
### Data Instances
For en:
```
id : 1
sentence1 : In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland .
sentence2 : In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England .
label : 0
```
For fr:
```
id : 1
sentence1 : À Paris, en octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, lui demandant un passeport pour retourner en Angleterre en passant par l'Écosse.
sentence2 : En octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, à Paris, et lui demanda un passeport pour retourner en Écosse par l'Angleterre.
label : 0
```
### Data Fields
All files are in tsv format with four columns:
Column Name | Data
:---------- | :--------------------------------------------------------
id | An ID that matches the ID of the source pair in PAWS-Wiki
sentence1 | The first sentence
sentence2 | The second sentence
label | Label for each pair
The source text of each translation can be retrieved by looking up the ID in the
corresponding file in PAWS-Wiki.
### Data Splits
The numbers of examples for each of the seven languages are shown below:
Language | Train | Dev | Test
:------- | ------: | -----: | -----:
en | 49,401 | 2,000 | 2,000
fr | 49,401 | 2,000 | 2,000
es | 49,401 | 2,000 | 2,000
de | 49,401 | 2,000 | 2,000
zh | 49,401 | 2,000 | 2,000
ja | 49,401 | 2,000 | 2,000
ko | 49,401 | 2,000 | 2,000
> **Caveat**: please note that the dev and test sets of PAWS-X are both sourced
> from the dev set of PAWS-Wiki. As a consequence, the same `sentence 1` may
> appear in both the dev and test sets. Nevertheless our data split guarantees
> that there is no overlap on sentence pairs (`sentence 1` + `sentence 2`)
> between dev and test.
## Dataset Creation
### Curation Rationale
Most existing work on adversarial data generation focuses on English. For example, PAWS (Paraphrase Adversaries from Word Scrambling) (Zhang et al., 2019) consists of challenging English paraphrase identification pairs from Wikipedia and Quora. They remedy this gap with PAWS-X, a new dataset of 23,659 human translated PAWS evaluation pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. They provide baseline numbers for three models with different capacity to capture non-local context and sentence structure, and using different multilingual training and evaluation regimes. Multilingual BERT (Devlin et al., 2019) fine-tuned on PAWS English plus machine-translated data performs the best, with a range of 83.1-90.8 accuracy across the non-English languages and an average accuracy gain of 23% over the next best model. PAWS-X shows the effectiveness of deep, multilingual pre-training while also leaving considerable headroom as a new challenge to drive multilingual research that better captures structure and contextual information.
### Source Data
PAWS (Paraphrase Adversaries from Word Scrambling)
#### Initial Data Collection and Normalization
All translated pairs are sourced from examples in [PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki)
#### Who are the source language producers?
This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean.
### Annotations
#### Annotation process
If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.
#### Who are the annotators?
The paper mentions the translate team, especially Mengmeng Niu, for the help with the annotations.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
### Licensing Information
The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
### Citation Information
```
@InProceedings{pawsx2019emnlp,
title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
booktitle = {Proc. of EMNLP},
year = {2019}
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@gowtham1997](https://github.com/gowtham1997) for adding this dataset. | 31,253 | [
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jxie/guacamol | 2023-08-03T23:49:15.000Z | [
"region:us"
] | jxie | null | null | 0 | 299 | 2023-08-03T23:49:05 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 65660530
num_examples: 1273104
- name: validation
num_bytes: 4097829
num_examples: 79568
- name: test
num_bytes: 12306244
num_examples: 238706
download_size: 45009159
dataset_size: 82064603
---
# Dataset Card for "guacamol"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 488 | [
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siyue/squall | 2023-09-08T06:08:06.000Z | [
"task_categories:table-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] | siyue | To explore the utility of fine-grained, lexical-level supervision, authors introduce SQUALL, a dataset that enriches 11,276 WikiTableQuestions \
English-language questions with manually created SQL equivalents plus \
alignments between SQL and question fragments. | @inproceedings{Shi:Zhao:Boyd-Graber:Daume-III:Lee-2020,
Title = {On the Potential of Lexico-logical Alignments for Semantic Parsing to {SQL} Queries},
Author = {Tianze Shi and Chen Zhao and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Lillian Lee},
Booktitle = {Findings of EMNLP},
Year = {2020},
} | 0 | 299 | 2023-09-02T06:59:17 | ---
license: mit
task_categories:
- table-question-answering
language:
- en
pretty_name: SQUALL
size_categories:
- 10K<n<100K
---
## SQUALL Dataset
To explore the utility of fine-grained, lexical-level supervision, authors introduce SQUALL, a dataset that enriches 11,276 WikiTableQuestions English-language questions with manually created SQL equivalents plus alignments between SQL and question fragments. 5-fold splits are applied to the full dataset (1 fold as dev set at each time). The subset defines which fold is selected as the validation dataset.
WARN: labels of test set are unknown.
## Source
Please refer to [github repo](https://github.com/tzshi/squall/) for source data.
## Use
```python
from datasets import load_dataset
dataset = load_dataset("siyue/squall","0")
```
Example:
```python
{
'nt': 'nt-10922',
'tbl': '204_879',
'columns':
{
'raw_header': ['year', 'host / location', 'division i overall', 'division i undergraduate', 'division ii overall', 'division ii community college'],
'tokenized_header': [['year'], ['host', '\\\\/', 'location'], ['division', 'i', 'overall'], ['division', 'i', 'undergraduate'], ['division', 'ii', 'overall'], ['division', 'ii', 'community', 'college']],
'column_suffixes': [['number'], ['address'], [], [], [], []],
'column_dtype': ['number', 'address', 'text', 'text', 'text', 'text'],
'example': ['1997', 'penn', 'chicago', 'swarthmore', 'harvard', 'valencia cc']
},
'nl': ['when', 'was', 'the', 'last', 'time', 'the', 'event', 'was', 'held', 'in', 'minnesota', '?'],
'nl_pos': ['WRB', 'VBD-AUX', 'DT', 'JJ', 'NN', 'DT', 'NN', 'VBD-AUX', 'VBN', 'IN', 'NNP', '.'],
'nl_ner': ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'LOCATION', 'O'],
'nl_incolumns': [False, False, False, False, False, False, False, False, False, False, False, False],
'nl_incells': [False, False, False, False, False, False, False, False, False, False, True, False],
'columns_innl': [False, False, False, False, False, False],
'tgt': '2007',
'sql':
{
'sql_type': ['Keyword', 'Column', 'Keyword', 'Keyword', 'Keyword', 'Column', 'Keyword', 'Literal.String', 'Keyword', 'Keyword', 'Column', 'Keyword', 'Keyword', 'Keyword'],
'value': ['select', 'c1', 'from', 'w', 'where', 'c2', '=', "'minnesota'", 'order', 'by', 'c1_number', 'desc', 'limit', '1'],
'span_indices': [[], [], [], [], [], [], [], [10, 10], [], [], [], [], [], []]
},
'nl_ralign':
{
'aligned_sql_token_type': ['None', 'None', 'Column', 'Column', 'Column', 'None', 'None', 'None', 'Column', 'Column', 'Literal', 'None'],
'aligned_sql_token_info': [None, None, 'c1_number', 'c1_number', 'c1', None, None, None, 'c2', 'c2', None, None],
'align':
{
'nl_indices': [[10], [9, 8], [4], [3, 2]],
'sql_indices': [[7], [5], [1], [8, 9, 10, 11, 12, 13]]
}
},
'align':
{
'nl_indices': [[10], [9, 8], [4], [3, 2]],
'sql_indices': [[7], [5], [1], [8, 9, 10, 11, 12, 13]]
}
}
```
## Contact
For any issues or questions, kindly email us at: Siyue Zhang (siyue001@e.ntu.edu.sg).
## Citation
```
@inproceedings{Shi:Zhao:Boyd-Graber:Daume-III:Lee-2020,
Title = {On the Potential of Lexico-logical Alignments for Semantic Parsing to {SQL} Queries},
Author = {Tianze Shi and Chen Zhao and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Lillian Lee},
Booktitle = {Findings of EMNLP},
Year = {2020},
}
``` | 3,398 | [
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] |
maxolotl/must-c-en-es-wait3-02 | 2023-10-22T07:48:24.000Z | [
"region:us"
] | maxolotl | null | null | 0 | 298 | 2023-10-22T07:48:05 | ---
dataset_info:
features:
- name: current_source
dtype: string
- name: current_target
dtype: string
- name: target_token
dtype: string
splits:
- name: train
num_bytes: 995120593
num_examples: 5240243
- name: test
num_bytes: 9960448
num_examples: 57187
- name: validation
num_bytes: 5429701
num_examples: 27549
download_size: 184348036
dataset_size: 1010510742
---
# Dataset Card for "must-c-en-es-wait3-02"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 597 | [
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xed_en_fi | 2023-06-01T14:59:50.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:extended|other-OpenSubtitles2016",
"language:en",
"language:fi",
"license:cc-by-4.0",
"arxiv:2011.01612",
"region:us"
] | null | A multilingual fine-grained emotion dataset. The dataset consists of human annotated Finnish (25k) and English sentences (30k). Plutchik’s
core emotions are used to annotate the dataset with the addition of neutral to create a multilabel multiclass
dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to
show that XED performs on par with other similar datasets and is therefore a useful tool for
sentiment analysis and emotion detection. | @inproceedings{ohman2020xed,
title={XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection},
author={{\"O}hman, Emily and P{\"a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg},
booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},
year={2020}
} | 6 | 297 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
- fi
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- extended|other-OpenSubtitles2016
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
- multi-label-classification
- sentiment-classification
paperswithcode_id: xed
pretty_name: XedEnglishFinnish
dataset_info:
- config_name: en_annotated
features:
- name: sentence
dtype: string
- name: labels
sequence:
class_label:
names:
'0': neutral
'1': anger
'2': anticipation
'3': disgust
'4': fear
'5': joy
'6': sadness
'7': surprise
'8': trust
splits:
- name: train
num_bytes: 1018485
num_examples: 17528
download_size: 2421235
dataset_size: 1018485
- config_name: en_neutral
features:
- name: sentence
dtype: string
- name: labels
dtype:
class_label:
names:
'0': neutral
'1': anger
'2': anticipation
'3': disgust
'4': fear
'5': joy
'6': sadness
'7': surprise
'8': trust
splits:
- name: train
num_bytes: 401129
num_examples: 9675
download_size: 2421235
dataset_size: 401129
- config_name: fi_annotated
features:
- name: sentence
dtype: string
- name: labels
sequence:
class_label:
names:
'0': neutral
'1': anger
'2': anticipation
'3': disgust
'4': fear
'5': joy
'6': sadness
'7': surprise
'8': trust
splits:
- name: train
num_bytes: 756224
num_examples: 14449
download_size: 2421235
dataset_size: 756224
- config_name: fi_neutral
features:
- name: sentence
dtype: string
- name: labels
dtype:
class_label:
names:
'0': neutral
'1': anger
'2': anticipation
'3': disgust
'4': fear
'5': joy
'6': sadness
'7': surprise
'8': trust
splits:
- name: train
num_bytes: 427499
num_examples: 10794
download_size: 2421235
dataset_size: 427499
config_names:
- en_annotated
- en_neutral
- fi_annotated
- fi_neutral
---
# Dataset Card for xed_english_finnish
## 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:** [Github](https://github.com/Helsinki-NLP/XED)
- **Paper:** [Arxiv](https://arxiv.org/abs/2011.01612)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is the XED dataset. The dataset consists of emotion annotated movie subtitles from OPUS. We use Plutchik's 8 core emotions to annotate. The data is multilabel. The original annotations have been sourced for mainly English and Finnish.
For the English data we used Stanford NER (named entity recognition) (Finkel et al., 2005) to replace names and locations with the tags: [PERSON] and [LOCATION] respectively.
For the Finnish data, we replaced names and locations using the Turku NER corpus (Luoma et al., 2020).
### Supported Tasks and Leaderboards
Sentiment Classification, Multilabel Classification, Multilabel Classification, Intent Classification
### Languages
English, Finnish
## Dataset Structure
### Data Instances
```
{ "sentence": "A confession that you hired [PERSON] ... and are responsible for my father's murder."
"labels": [1, 6] # anger, sadness
}
```
### Data Fields
- sentence: a line from the dataset
- labels: labels corresponding to the emotion as an integer
Where the number indicates the emotion in ascending alphabetical order: anger:1, anticipation:2, disgust:3, fear:4, joy:5, sadness:6, surprise:7, trust:8, with neutral:0 where applicable.
### Data Splits
For English:
Number of unique data points: 17528 ('en_annotated' config) + 9675 ('en_neutral' config)
Number of emotions: 8 (+neutral)
For Finnish:
Number of unique data points: 14449 ('fi_annotated' config) + 10794 ('fi_neutral' config)
Number of emotions: 8 (+neutral)
## 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
License: Creative Commons Attribution 4.0 International License (CC-BY)
### Citation Information
@inproceedings{ohman2020xed,
title={XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection},
author={{\"O}hman, Emily and P{\`a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg},
booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},
year={2020}
}
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq), [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset. | 6,476 | [
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mteb/reddit-clustering-p2p | 2022-09-27T19:13:59.000Z | [
"language:en",
"region:us"
] | mteb | null | null | 0 | 297 | 2022-05-11T08:52:19 | ---
language:
- en
---
10 sets with the following stats:
1. 91 labels & 15592 samples
2. 64 labels & 79172 samples
3. 38 labels & 1942 samples
4. 11 labels & 13224 samples
5. 64 labels & 92303 samples
6. 87 labels & 28607 samples
7. 10 labels & 69146 samples
8. 48 labels & 67469 samples
9. 64 labels & 29683 samples
10. 31 labels & 62261 samples
Selected at random using the script available on the mteb github repository.
| 428 | [
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nlp-guild/intent-recognition-biomedical | 2022-09-22T16:13:44.000Z | [
"license:mit",
"region:us"
] | nlp-guild | null | null | 0 | 297 | 2022-09-22T16:10:30 | ---
license: mit
---
[source](https://github.com/wangle1218/KBQA-for-Diagnosis/tree/main/nlu/bert_intent_recognition/data) | 123 | [
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GabeHD/pokemon-type-captions | 2022-10-23T04:40:59.000Z | [
"region:us"
] | GabeHD | null | null | 3 | 297 | 2022-10-18T08:38:18 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 19372532.0
num_examples: 898
download_size: 0
dataset_size: 19372532.0
---
# Dataset Card for Pokémon type captions
Contains official artwork and type-specific caption for Pokémon #1-898 (Bulbasaur-Calyrex).
Each Pokémon is represented once by the default form from [PokéAPI](https://pokeapi.co/)
Each row contains `image` and `text` keys:
- `image` is a 475x475 PIL jpg of the Pokémon's official artwork.
- `text` is a label describing the Pokémon by its type(s)
## Attributions
_Images and typing information pulled from [PokéAPI](https://pokeapi.co/)_
_Based on the [Lambda Labs Pokémon Blip Captions Dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions)_
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] |
banghua/tldr_reward_model_labeled | 2023-09-21T19:08:04.000Z | [
"region:us"
] | banghua | null | null | 0 | 297 | 2023-08-06T17:18:36 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 300444471.0
num_examples: 176163
download_size: 177215543
dataset_size: 300444471.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tldr_reward_model_labeled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 539 | [
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] |
roszcz/giant-midi-masked-v3 | 2023-10-03T18:34:23.000Z | [
"region:us"
] | roszcz | null | null | 0 | 297 | 2023-10-03T16:25:29 | ---
dataset_info:
features:
- name: pitch
sequence: int8
length: 90
- name: start
sequence: float64
length: 90
- name: dstart
sequence: float64
length: 90
- name: end
sequence: float64
length: 90
- name: duration
sequence: float64
length: 90
- name: velocity
sequence: int8
length: 90
- name: source
dtype: string
- name: masking_space
struct:
- name: <Random Mask>
sequence: bool
length: 90
- name: <LH Mask>
sequence: bool
length: 90
- name: <RH Mask>
sequence: bool
length: 90
- name: <Harmonic Root Mask>
sequence: bool
length: 90
- name: <Harmonic Outliers Mask>
sequence: bool
length: 90
splits:
- name: train
num_bytes: 24181696800
num_examples: 7140520
download_size: 23770439021
dataset_size: 24181696800
---
# Dataset Card for "giant-midi-masked-v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,062 | [
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alexandrainst/nst-da | 2023-10-05T14:27:00.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"size_categories:100K<n<1M",
"language:da",
"license:cc0-1.0",
"region:us"
] | alexandrainst | null | null | 2 | 297 | 2023-10-05T11:27:17 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: age
dtype: int64
- name: sex
dtype: string
- name: dialect
dtype: string
- name: recording_datetime
dtype: string
splits:
- name: train
num_bytes: 55199435558.0
num_examples: 182605
- name: test
num_bytes: 8894080220.0
num_examples: 54747
download_size: 5358057252
dataset_size: 64093515778.0
size_categories:
- 100K<n<1M
license: cc0-1.0
task_categories:
- automatic-speech-recognition
- text-to-speech
language:
- da
pretty_name: NST-da
---
# Dataset Card for NST-da
## Dataset Description
- **Repository:** <https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-55/>
- **Point of Contact:** [Dan Saattrup Nielsen](mailto:dan.nielsen@alexandra.dk)
- **Size of downloaded dataset files:** 5.36 GB
- **Size of the generated dataset:** 64.09 GB
- **Total amount of disk used:** 69.45 GB
### Dataset Summary
This dataset is an upload of the [NST Danish ASR Database (16 kHz) – reorganized](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-55/).
The training and test splits are the original ones.
### Supported Tasks and Leaderboards
Training automatic speech recognition is the intended task for this dataset. No leaderboard is active at this point.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 5.36 GB
- **Size of the generated dataset:** 64.09 GB
- **Total amount of disk used:** 69.45 GB
An example from the dataset looks as follows.
```
{
'audio': {
'path': 'dk14x404-05072000-1531_u0008121.wav',
'array': array([ 0.00265503, 0.00248718, 0.00253296, ..., -0.00030518,
-0.00035095, -0.00064087]),
'sampling_rate': 16000
},
'text': 'Desuden er der en svømmeprøve, en fremmedsprogstest samt en afsluttende samtale.',
'speaker_id': 404,
'age': 24,
'sex': 'Female',
'dialect': 'Storkøbenhavn',
'recording_datetime': '2000-07-05T15:31:14'
}
```
### Data Fields
The data fields are the same among all splits.
- `audio`: an `Audio` feature.
- `text`: a `string` feature.
- `speaker_id`: an `int64` feature.
- `age`: an `int64` feature.
- `sex`: a `string` feature.
- `dialect`: a `string` feature.
- `recording_datetime`: a `string` feature.
### Dataset Statistics
There are 183,205 samples in the training split, and 54,747 samples in the test split.
#### Speakers
There are 539 unique speakers in the training dataset and 56 unique speakers in the test dataset, where 54 of them are also present in the training set.
#### Age Distribution
#### Dialect Distribution
#### Sex Distribution
#### Transcription Length Distribution
## Dataset Creation
### Curation Rationale
There are not many large-scale ASR datasets in Danish.
### Source Data
The data originates from the now bankrupt company Nordisk språkteknologi (NST), whose data was transferred to the National Library of Norway, who subsequently released it into the public domain.
## Additional Information
### Dataset Curators
[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
Institute](https://alexandra.dk/) reorganised the dataset and uploaded it to the Hugging Face Hub.
### Licensing Information
The dataset is licensed under the [CC0
license](https://creativecommons.org/share-your-work/public-domain/cc0/). | 4,217 | [
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] |
TheFusion21/PokemonCards | 2022-11-21T18:28:25.000Z | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | TheFusion21 | null | null | 6 | 296 | 2022-11-20T14:14:51 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
pretty_name: Pokemoncards
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- text-to-image
- image-to-text
task_ids:
- image-captioning
---
# Dataset Card for PokemonCards
### Languages
All of the data is in English.
## Dataset Structure
### Data Instances
```json
{
"id": "pl1-1",
"image_url": "https://images.pokemontcg.io/pl1/1_hires.png",
"caption": "A Stage 2 Pokemon Card of type Lightning with the title ""Ampharos"" and 130 HP of rarity ""Rare Holo"" evolved from Flaaffy from the set Platinum and the flavor text: ""None"". It has the attack ""Gigavolt"" with the cost Lightning, Colorless, the energy cost 2 and the damage of 30+ with the description: ""Flip a coin. If heads, this attack does 30 damage plus 30 more damage. If tails, the Defending Pokemon is now Paralyzed."". It has the attack ""Reflect Energy"" with the cost Lightning, Colorless, Colorless, the energy cost 3 and the damage of 70 with the description: ""Move an Energy card attached to Ampharos to 1 of your Benched Pokemon."". It has the ability ""Damage Bind"" with the description: ""Each Pokemon that has any damage counters on it (both yours and your opponent's) can't use any Poke-Powers."". It has weakness against Fighting +30. It has resistance against Metal -20.",
"name": "Ampharos",
"hp": "130",
"set_name": "Platinum"
}
```
### Data Fields
- `id`: Unique ID of the pokemon card.
- `image_url`: Static URL for downloading the image associated with the post.
- `caption`: Caption generated for this card.
- `name`: Name of the pokemon on that card.
- `hp`: Health of the pokemon.
- `set_name`: The name of the set the card is in.
### Data Splits
All the data is contained in training set. The training set has nearly 13k instances.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions | 2,765 | [
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] |
microsoft/LCC_python | 2023-06-21T03:13:06.000Z | [
"region:us"
] | microsoft | null | null | 1 | 296 | 2023-06-21T03:12:37 | ---
dataset_info:
features:
- name: gt
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 1761900743
num_examples: 100000
- name: validation
num_bytes: 146577328
num_examples: 10000
- name: test
num_bytes: 149430294
num_examples: 10000
download_size: 703086720
dataset_size: 2057908365
---
# Dataset Card for "LCC_python"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 530 | [
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] |
heegyu/hh-rlhf-vicuna-format | 2023-09-06T03:07:11.000Z | [
"region:us"
] | heegyu | null | null | 1 | 296 | 2023-08-28T08:37:18 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: chosen
struct:
- name: from
dtype: string
- name: value
dtype: string
- name: rejected
struct:
- name: from
dtype: string
- name: value
dtype: string
- name: source
dtype: string
- name: context
list:
- name: from
dtype: string
- name: value
dtype: string
- name: instruction
struct:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 210193081
num_examples: 160800
- name: test
num_bytes: 11248194
num_examples: 8552
download_size: 128509060
dataset_size: 221441275
---
Original data is [Antrophic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf). It was converted to Vicuna format
sample
```
{'chosen': {'from': 'gpt',
'value': 'A horseshoe is usually made out of metal and is about 3 to 3.5 inches long and around 1 inch thick. The horseshoe should also have a 2 inch by 3 inch flat at the bottom where the rubber meets the metal. We also need two stakes and six horseshoes.'},
'rejected': {'from': 'gpt',
'value': 'Horseshoes are either metal or plastic discs. The horseshoes come in different weights, and the lighter ones are easier to throw, so they are often the standard for beginning players.'},
'source': 'helpful-base',
'context': [{'from': 'human',
'value': 'Hi, I want to learn to play horseshoes. Can you teach me?'},
{'from': 'gpt',
'value': 'I can, but maybe I should begin by telling you that a typical game consists of 2 players and 6 or 8 horseshoes.'}],
'instruction': {'from': 'human',
'value': 'Okay. What else is needed to play, and what are the rules?'}}
```
source columns has 4 values
```
{'harmless-base',
'helpful-base',
'helpful-online',
'helpful-rejection-sampled'}
```
In context, chosen, rejected column, 'from' key is either 'human' or 'gpt'. | 2,046 | [
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Geonmo/midjourney-prompts-only | 2023-10-25T09:12:51.000Z | [
"region:us"
] | Geonmo | null | null | 0 | 296 | 2023-10-25T09:10:06 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 495800699
num_examples: 3492544
download_size: 334934157
dataset_size: 495800699
---
# Dataset Card for "midjourney-prompts-only"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 374 | [
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wdc/products-2017 | 2022-10-23T05:50:24.000Z | [
"task_categories:text-classification",
"annotations_creators:weak supervision",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | wdc | Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label "match" or "no match")
In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test set. We provide training and validation sets in four different sizes for four product categories. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web via weak supervision.
The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites. | @inproceedings{primpeli2019wdc,
title={The WDC training dataset and gold standard for large-scale product matching},
author={Primpeli, Anna and Peeters, Ralph and Bizer, Christian},
booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
pages={381--386},
year={2019}
} | 2 | 295 | 2022-05-16T13:23:21 | ---
annotations_creators:
- weak supervision
- expert-generated
language:
- en
language_bcp47:
- en-US
license:
- unknown
multilinguality:
- monolingual
pretty_name: products-2017
size_categories:
- 1K<n<10K
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
- data-integration
task_ids:
- entity-matching
- identity-resolution
- product-matching
paperswithcode_id: wdc-products
---
# Dataset Card for [products-2017]
## 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)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [LSPCv2 Homepage](http://webdatacommons.org/largescaleproductcorpus/v2/index.html)
- **Point of Contact:** [Ralph Peeters](mailto:ralph.peeters@uni-mannheim.de)
### Dataset Summary
Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label "match" or "no match")
In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test set. We provide training and validation sets in four different sizes for four product categories. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web via weak supervision.
The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites.
### Supported Tasks and Leaderboards
Entity Matching, Product Matching
### Languages
English
## Dataset Structure
### Data Instances
The data is structured as pairs of product offers with the corresponding match/non-match label. This is an example instance from the computers category:
```
{"pair_id":"581109#16637861","label":0,"id_left":581109,"category_left":"Computers_and_Accessories","cluster_id_left":1324529,"brand_left":"\"Gigabyte\"@en","title_left":" \"Gigabyte Radeon RX 480 G1 Gaming 4096MB GDDR5 PCI-Express Graphics Card\"@en \"Gigabyte Gr| OcUK\"@en","description_left":"\"GV-RX480G1 GAMING-4GD, Core Clock: 1202MHz, Boost Clock: 1290MHz, Memory: 4096MB 7000MHz GDDR5, Stream Processors: 2304, Crossfire Ready, VR Ready, FreeSync Ready, 3 Years Warranty\"@en ","price_left":null,"specTableContent_left":null,"id_right":16637861,"category_right":"Computers_and_Accessories","cluster_id_right":107415,"brand_right":"\"Gigabyte\"@en","title_right":" \"Gigabyte Radeon RX 550 Gaming OC 2048MB GDDR5 PCI-Express Graphics Card\"@en \"Gigabyte Gr| OcUK\"@en","description_right":"\"GV-RX550GAMING OC-2GD, Boost: 1219MHz, Memory: 2048MB 7000MHz GDDR5, Stream Processors: 512, DirectX 12 Support, 3 Years Warranty\"@en ","price_right":null,"specTableContent_right":null}
```
### Data Fields
- pair_id: unique identifier of a pair (string)
- label: binary label, match or non-match (int)
The following attributes are contained twice, once for the first and once for the second product offer
- id: unique id of the product offer (int)
- category: product category (string)
- cluster_id: id of the product cluster from the original corpus this offer belongs to (int)
- brand: brand of the product (string)
- title: product title (string)
- description: longer product description (string)
- price: price of the product offer (string)
- specTableContent: additional data found in specification tables on the webpage that contains the product offer (string)
### Data Splits
- Computers
- Test set - 1100 pairs
- Small Train set - 2267 pairs
- Small Validation set - 567 pairs
- Medium Train set - 6475 pairs
- Medium Validation set - 1619 pairs
- Large Train set - 26687 pairs
- Large Validation set - 6672 pairs
- XLarge Train set - 54768 pairs
- Xlarge Validation set - 13693 pairs
- Cameras
- Test set - 1100 pairs
- Small Train set - 1508 pairs
- Small Validation set - 378 pairs
- Medium Train set - 4204 pairs
- Medium Validation set - 1051 pairs
- Large Train set - 16028 pairs
- Large Validation set - 4008 pairs
- XLarge Train set - 33821 pairs
- Xlarge Validation set - 8456 pairs
- Watches
- Test set - 1100 pairs
- Small Train set - 1804 pairs
- Small Validation set - 451 pairs
- Medium Train set - 5130 pairs
- Medium Validation set - 1283 pairs
- Large Train set - 21621 pairs
- Large Validation set - 5406 pairs
- XLarge Train set - 49255 pairs
- Xlarge Validation set - 12314 pairs
- Shoes
- Test set - 1100 pairs
- Small Train set - 1650 pairs
- Small Validation set - 413 pairs
- Medium Train set - 4644 pairs
- Medium Validation set - 1161 pairs
- Large Train set - 18391 pairs
- Large Validation set - 4598 pairs
- XLarge Train set - 33943 pairs
- Xlarge Validation set - 8486 pairs
## Dataset Creation
### Annotations
#### Annotation process
- Training and Validation sets: distant supervision via shared schema.org product IDs
- Test sets: Single expert annotator
#### Who are the annotators?
[Ralph Peeters](https://www.uni-mannheim.de/dws/people/researchers/phd-students/ralph-peeters/)
## Additional Information
### Citation Information
```
@inproceedings{primpeli2019wdc,
title={The WDC training dataset and gold standard for large-scale product matching},
author={Primpeli, Anna and Peeters, Ralph and Bizer, Christian},
booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
pages={381--386},
year={2019}
}
```
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JanosAudran/financial-reports-sec | 2023-01-06T17:44:08.000Z | [
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:masked-language-modeling",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
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"source_datasets:extended|other",
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"'finance",
"financial",
"10-K",
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"10k",
"10-k",
"annual",
"reports",
"sec",
"edgar",
"sentiment",
"firm",
"public",
"us'",
"region:us"
] | JanosAudran | The dataset contains the annual report of US public firms filing with the SEC EDGAR system.
Each annual report (10K filing) is broken into 20 sections. Each section is split into individual sentences.
Sentiment labels are provided on a per filing basis from the market reaction around the filing data.
Additional metadata for each filing is included in the dataset. | null | 41 | 294 | 2023-01-02T15:21:14 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
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pretty_name: US public firm Annual Reports (10-K)
size_categories:
- 10M<n<100M
source_datasets:
- extended|other
tags:
- "'finance"
- financial
- 10-K
- 10K
- 10k
- 10-k
- annual
- reports
- sec
- edgar
- sentiment
- firm
- public
- us'
task_categories:
- fill-mask
- text-classification
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---
# Dataset Card for [financial-reports-sec]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Configurations](#dataset-configurations)
- [Usage](#usage)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Summary Statistics](#dataset-summary-statistics)
- [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)
- [References](#references)
- [Citation Information](#citation-information)
## Dataset Description
- **Point of Contact: Aman Khan**
### Dataset Summary
The dataset contains the annual report of US public firms filing with the SEC EDGAR system from 1993-2020. Each annual report (**10K filing**) is broken into 20 sections. Each section is split into individual sentences. Sentiment labels are provided on a **per filing basis** from the market reaction around the filing date for 3 different time windows _[t-1, t+1]_, _[t-1, t+5]_ and _[t-1, t+30]_. Additional metadata for each filing is included in the dataset.
### Dataset Configurations
**Four** configurations are available:
- _**large_lite**_:
- Contains only the basic features needed. Extra metadata is ommitted.
- Features List:
- **cik**
- **sentence**
- **section**
- **labels**
- **filingDate**
- **docID**
- **sentenceID**
- **sentenceCount**
- _**large_full**_:
- All features are included.
- Features List (excluding those already in the lite verison above):
- **name**
- **tickers**
- **exchanges**
- **entityType**
- **sic**
- **stateOfIncorporation**
- **tickerCount**
- **acceptanceDateTime**
- **form**
- **reportDate**
- **returns**
- _**small_lite**_:
- Same as _**large_lite**_ version except that only (200,000/20,000/20,000) sentences are loaded for (train/test/validation) splits.
- _**small_full**_:
- Same as _**large_full**_ version except that only (200,000/20,000/20,000) sentences are loaded for (train/test/validation) splits.
### Usage
```python
import datasets
# Load the lite configuration of the dataset
raw_dataset = datasets.load_dataset("JanosAudran/financial-reports-sec", "large_lite")
# Load a specific split
raw_dataset = datasets.load_dataset("JanosAudran/financial-reports-sec", "small_full", split="train")
```
### Supported Tasks
The tasks the dataset can be used directly for includes:
- _Masked Language Modelling_
- A model like BERT can be fine-tuned on this corpus of financial text.
- _Sentiment Analysis_
- For each annual report a label ["positive", "negative"] is provided based on the market reaction around the filing date (refer to [Annotations](#annotations)).
- _Next Sentence Prediction/Sentence Order Prediction_
- Sentences extracted from the filings are in their original order and as such the dataset can be adapted very easily for either of these tasks.
### Languages
All sentences are in English.
## Dataset Structure
### Data Instances
Refer to dataset preview.
### Data Fields
**Feature Name**
- Description
- Data type
- Example/Structure
**cik**
- 10 digit identifier used by SEC for a firm.
- _string_
- '0000001750'
**sentence**
- A single sentence from the 10-K filing.
- _string_
- 'The finance agreement is secured by a first priority security interest in all insurance policies, all unearned premium, return premiums, dividend payments and loss payments thereof.'
**section**
- The section of the 10-K filing the sentence is located.
- _ClassLabel_
- ```python
ClassLabel(names=['section_1', 'section_10', 'section_11', 'section_12', 'section_13', 'section_14', 'section_15', 'section_1A', 'section_1B', 'section_2','section_3', 'section_4', 'section_5', 'section_6', 'section_7', 'section_7A','section_8', 'section_9', 'section_9A', 'section_9B'], id=None)
```
**labels**
- The sentiment label for the entire filing (_**positve**_ or _**negative**_) based on different time windows.
- _Dict of ClassLables_
- ```python
{
'1d': ClassLabel(names=['positive', 'negative'], id=None),
'5d': ClassLabel(names=['positive', 'negative'], id=None),
'30d': ClassLabel(names=['positive', 'negative'], id=None)
}
```
**filingDate**
- The date the 10-K report was filed with the SEC.
- _string_
- '2021-03-10'
**docID**
- Unique ID for identifying the exact 10-K filing. Unique across all configs and splits. Can be used to identify the document from which the sentence came from.
- _string_
- '0000001750_10-K_2020'
**sentenceID**
- Unique ID for identifying the exact sentence. Unique across all configs and splits.
- _string_
- '0000001750_10-K_2020_section_1_100'
**sentenceCount**
- Integer identiying the running sequence for the sentence. Unique **only** for a given config and split.
- _string_
- 123
**name**
- The name of the filing entity
- _string_
- 'Investar Holding Corp'
**tickers**
- List of ticker symbols for the filing entity.
- _List of strings_
- ['ISTR']
**exchanges**
- List of exchanges for the filing entity.
- _List of strings_
- ['Nasdaq']
**entityType**
- The type of entity as identified in the 10-K filing.
- _string_
- 'operating'
**sic**
- Four digit SIC code for the filing entity.
- _string_
- '6022'
**stateOfIncorporation**
- Two character code for the state of incorporation for the filing entity.
- _string_
- 'LA'
**tickerCount**
- _**Internal use**_. Count of ticker symbols. Always 1.
- _int_
- 1
**acceptanceDateTime**
- The full timestamp of when the filing was accepted into the SEC EDGAR system.
- _string_
- '2021-03-10T14:26:11.000Z'
**form**
- The type of filing. Always 10-K in the dataset.
- _string_
- '10-K'
**reportDate**
- The last date in the fiscal year for which the entity is filing the report.
- _string_
- '2020-12-31'
**returns**
- _**Internal use**_. The prices and timestamps used to calculate the sentiment labels.
- _Dict_
- ```python
{'1d': {
'closePriceEndDate': 21.45746421813965,
'closePriceStartDate': 20.64960479736328,
'endDate': '2021-03-11T00:00:00-05:00',
'startDate': '2021-03-09T00:00:00-05:00',
'ret': 0.03912226855754852
},
'5d': {
'closePriceEndDate': 21.743167877197266,
'closePriceStartDate': 20.64960479736328,
'endDate': '2021-03-15T00:00:00-04:00',
'startDate': '2021-03-09T00:00:00-05:00',
'ret': 0.052958063781261444
},
'30d': {
'closePriceEndDate': 20.63919448852539,
'closePriceStartDate': 20.64960479736328,
'endDate': '2021-04-09T00:00:00-04:00',
'startDate': '2021-03-09T00:00:00-05:00',
'ret': -0.0005041408003307879}}
```
### Data Splits
| Config | train | validation | test |
| ---------- | ---------: | ---------: | --------: |
| large_full | 67,316,227 | 1,585,561 | 2,965,174 |
| large_lite | 67,316,227 | 1,585,561 | 2,965,174 |
| small_full | 200,000 | 20,000 | 20,000 |
| small_lite | 200,000 | 20,000 | 20,000 |
### Dataset Summary Statistics
| Variable | count | mean | std | min | 1% | 25% | 50% | 75% | 99% | max |
| :-------------------------------- | ---------: | ----: | -----: | -----: | -----: | -----: | ----: | ----: | ----: | --------: |
| Unique Firm Count | 4,677 | | | | | | | | | |
| Filings Count | 55,349 | | | | | | | | | |
| Sentence Count | 71,866,962 | | | | | | | | | |
| Filings per Firm | 4,677 | 12 | 9 | 1 | 1 | 4 | 11 | 19 | 27 | 28 |
| Return per Filing - 1d | 55,349 | 0.008 | 0.394 | -0.973 | -0.253 | -0.023 | 0 | 0.02 | 0.367 | 77.977 |
| Return per Filing - 5d | 55,349 | 0.013 | 0.584 | -0.99 | -0.333 | -0.034 | 0 | 0.031 | 0.5 | 100 |
| Return per Filing - 30d | 55,349 | 0.191 | 22.924 | -0.999 | -0.548 | -0.068 | 0.001 | 0.074 | 1 | 5,002.748 |
| Sentences per Filing | 55,349 | 1,299 | 654 | 0 | 110 | 839 | 1,268 | 1,681 | 3,135 | 8,286 |
| Sentences by Section - section_1 | 55,349 | 221 | 183 | 0 | 0 | 97 | 180 | 293 | 852 | 2,724 |
| Sentences by Section - section_10 | 55,349 | 24 | 40 | 0 | 0 | 4 | 6 | 20 | 173 | 1,594 |
| Sentences by Section - section_11 | 55,349 | 16 | 47 | 0 | 0 | 3 | 3 | 4 | 243 | 808 |
| Sentences by Section - section_12 | 55,349 | 9 | 14 | 0 | 0 | 3 | 4 | 8 | 56 | 1,287 |
| Sentences by Section - section_13 | 55,349 | 8 | 20 | 0 | 0 | 3 | 3 | 4 | 79 | 837 |
| Sentences by Section - section_14 | 55,349 | 22 | 93 | 0 | 0 | 3 | 3 | 8 | 413 | 3,536 |
| Sentences by Section - section_15 | 55,349 | 177 | 267 | 0 | 0 | 9 | 26 | 315 | 1104 | 4,140 |
| Sentences by Section - section_1A | 55,349 | 197 | 204 | 0 | 0 | 3 | 158 | 292 | 885 | 2,106 |
| Sentences by Section - section_1B | 55,349 | 4 | 31 | 0 | 0 | 1 | 3 | 3 | 13 | 2,414 |
| Sentences by Section - section_2 | 55,349 | 16 | 45 | 0 | 0 | 6 | 8 | 13 | 169 | 1,903 |
| Sentences by Section - section_3 | 55,349 | 14 | 36 | 0 | 0 | 4 | 5 | 12 | 121 | 2,326 |
| Sentences by Section - section_4 | 55,349 | 7 | 17 | 0 | 0 | 3 | 3 | 4 | 66 | 991 |
| Sentences by Section - section_5 | 55,349 | 20 | 41 | 0 | 0 | 10 | 15 | 21 | 87 | 3,816 |
| Sentences by Section - section_6 | 55,349 | 8 | 29 | 0 | 0 | 3 | 4 | 7 | 43 | 2,156 |
| Sentences by Section - section_7 | 55,349 | 265 | 198 | 0 | 0 | 121 | 246 | 373 | 856 | 4,539 |
| Sentences by Section - section_7A | 55,349 | 18 | 52 | 0 | 0 | 3 | 9 | 21 | 102 | 3,596 |
| Sentences by Section - section_8 | 55,349 | 257 | 296 | 0 | 0 | 3 | 182 | 454 | 1105 | 4,431 |
| Sentences by Section - section_9 | 55,349 | 5 | 33 | 0 | 0 | 3 | 3 | 4 | 18 | 2,330 |
| Sentences by Section - section_9A | 55,349 | 17 | 16 | 0 | 0 | 8 | 15 | 23 | 50 | 794 |
| Sentences by Section - section_9B | 55,349 | 4 | 18 | 0 | 0 | 2 | 3 | 4 | 23 | 813 |
| Word count per Sentence | 71,866,962 | 28 | 22 | 1 | 2 | 16 | 24 | 34 | 98 | 8,675 |
## Dataset Creation
### Curation Rationale
To create this dataset multiple sources of information have to be cleaned and processed for data merging. Starting from the raw filings:
- Useful metadata about the filing and firm was added.
- Time windows around the filing date were carefully created.
- Stock price data was then added for the windows.
- Ambiguous/duplicate records were removed.
### Source Data
#### Initial Data Collection and Normalization
Initial data was collected and processed by the authors of the research paper [**EDGAR-CORPUS: Billions of Tokens Make The World Go Round**](#references). Market price and returns data was collected from Yahoo Finance. Additional metadata was collected from SEC.
#### Who are the source language producers?
US public firms filing with the SEC.
### Annotations
#### Annotation process
Labels for sentiment classification are based on buy-and-hold returns over a fixed time window around the filing date with the SEC i.e. when the data becomes public. Returns are chosen for this process as it reflects the combined market intelligence at parsing the new information in the filings. For each filing date **t** the stock price at **t-1** and **t+W** is used to calculate returns. If, the returns are positive a label of **positive** is assigned else a label of **negative** is assigned. Three different windows are used to assign the labels:
- **1d**: _[t-1, t+1]_
- **5d**: _[t-1, t+5]_
- **30d**: _[t-1, t+30]_
The windows are based on calendar days and are adjusted for weekends and holidays. The rationale behind using 3 windows is as follows:
- A very short window may not give enough time for all the information contained in the filing to be reflected in the stock price.
- A very long window may capture other events that drive stock price for the firm.
#### Who are the annotators?
Financial market participants.
### Personal and Sensitive Information
The dataset contains public filings data from SEC. Market returns data was collected from Yahoo Finance.
## Considerations for Using the Data
### Social Impact of Dataset
Low to none.
### Discussion of Biases
The dataset is about financial information of public companies and as such the tone and style of text is in line with financial literature.
### Other Known Limitations
NA
## Additional Information
### Dataset Curators
**Aman Khan**
### Licensing Information
This dataset is provided under Apache 2.0.
### References
- Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, & Prodromos Malakasiotis. (2021). EDGAR-CORPUS [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5589195
### Citation Information
Please use the following to cite this dataset:
```
@ONLINE{financial-reports-sec,
author = "Aman Khan",
title = "Financial Reports SEC",
url = "https://huggingface.co/datasets/JanosAudran/financial-reports-sec"
}
```
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discofuse | 2023-04-05T10:04:50.000Z | [
"task_categories:text2text-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"sentence-fusion",
"arxiv:1902.10526",
"region:us"
] | null | DISCOFUSE is a large scale dataset for discourse-based sentence fusion. | @InProceedings{GevaEtAl2019,
title = {DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion},
author = {Geva, Mor and Malmi, Eric and Szpektor, Idan and Berant, Jonathan},
booktitle = {Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
note = {arXiv preprint arXiv:1902.10526},
year = {2019}
} | 3 | 293 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: DiscoFuse
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: discofuse
tags:
- sentence-fusion
dataset_info:
- config_name: discofuse-sport
features:
- name: connective_string
dtype: string
- name: discourse_type
dtype: string
- name: coherent_second_sentence
dtype: string
- name: has_coref_type_pronoun
dtype: float32
- name: incoherent_first_sentence
dtype: string
- name: incoherent_second_sentence
dtype: string
- name: has_coref_type_nominal
dtype: float32
- name: coherent_first_sentence
dtype: string
splits:
- name: train
num_bytes: 14736279993
num_examples: 43291020
- name: test
num_bytes: 151656323
num_examples: 445521
- name: validation
num_bytes: 150207737
num_examples: 440902
download_size: 4326637746
dataset_size: 15038144053
- config_name: discofuse-wikipedia
features:
- name: connective_string
dtype: string
- name: discourse_type
dtype: string
- name: coherent_second_sentence
dtype: string
- name: has_coref_type_pronoun
dtype: float32
- name: incoherent_first_sentence
dtype: string
- name: incoherent_second_sentence
dtype: string
- name: has_coref_type_nominal
dtype: float32
- name: coherent_first_sentence
dtype: string
splits:
- name: train
num_bytes: 6377924196
num_examples: 16310585
- name: test
num_bytes: 64008158
num_examples: 163657
- name: validation
num_bytes: 65682035
num_examples: 168081
download_size: 1717422334
dataset_size: 6507614389
---
# Dataset Card for "discofuse"
## 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/google-research-datasets/discofuse
- **Paper:** [DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion](https://arxiv.org/abs/1902.10526)
- **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:** 6.04 GB
- **Size of the generated dataset:** 21.55 GB
- **Total amount of disk used:** 27.59 GB
### Dataset Summary
DiscoFuse is a large scale dataset for discourse-based sentence fusion.
### 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
#### discofuse-sport
- **Size of downloaded dataset files:** 4.33 GB
- **Size of the generated dataset:** 15.04 GB
- **Total amount of disk used:** 19.36 GB
An example of 'train' looks as follows.
```
{
"coherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .",
"coherent_second_sentence": "Finally , an HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner .",
"connective_string": "finally ,",
"discourse_type": "PAIR_CONN",
"has_coref_type_nominal": 0.0,
"has_coref_type_pronoun": 0.0,
"incoherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .",
"incoherent_second_sentence": "An HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner ."
}
```
#### discofuse-wikipedia
- **Size of downloaded dataset files:** 1.72 GB
- **Size of the generated dataset:** 6.51 GB
- **Total amount of disk used:** 8.23 GB
An example of 'validation' looks as follows.
```
{
"coherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .",
"coherent_second_sentence": "Finally , an HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner .",
"connective_string": "finally ,",
"discourse_type": "PAIR_CONN",
"has_coref_type_nominal": 0.0,
"has_coref_type_pronoun": 0.0,
"incoherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .",
"incoherent_second_sentence": "An HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner ."
}
```
### Data Fields
The data fields are the same among all splits.
#### discofuse-sport
- `connective_string`: a `string` feature.
- `discourse_type`: a `string` feature.
- `coherent_second_sentence`: a `string` feature.
- `has_coref_type_pronoun`: a `float32` feature.
- `incoherent_first_sentence`: a `string` feature.
- `incoherent_second_sentence`: a `string` feature.
- `has_coref_type_nominal`: a `float32` feature.
- `coherent_first_sentence`: a `string` feature.
#### discofuse-wikipedia
- `connective_string`: a `string` feature.
- `discourse_type`: a `string` feature.
- `coherent_second_sentence`: a `string` feature.
- `has_coref_type_pronoun`: a `float32` feature.
- `incoherent_first_sentence`: a `string` feature.
- `incoherent_second_sentence`: a `string` feature.
- `has_coref_type_nominal`: a `float32` feature.
- `coherent_first_sentence`: a `string` feature.
### Data Splits
| name | train |validation| test |
|-------------------|-------:|---------:|-----:|
|discofuse-sport |43291020| 440902|445521|
|discofuse-wikipedia|16310585| 168081|163657|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The data is licensed under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@InProceedings{GevaEtAl2019,
title = {DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion},
author = {Geva, Mor and Malmi, Eric and Szpektor, Idan and Berant, Jonathan},
booktitle = {Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
note = {arXiv preprint arXiv:1902.10526},
year = {2019}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset. | 9,269 | [
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tomasg25/scientific_lay_summarisation | 2022-10-26T11:11:33.000Z | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"abstractive-summarization",
"scientific-papers",
"lay-summarization",
"PLOS",
"eLife",
"arxiv:2210.09932",
"region:us"
] | tomasg25 | This repository contains the PLOS and eLife datasets, introduced in the EMNLP 2022 paper "[Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
](https://arxiv.org/abs/2210.09932)".
Each dataset contains full biomedical research articles paired with expert-written lay summaries (i.e., non-technical summaries). PLOS articles are derived from various journals published by [the Public Library of Science (PLOS)](https://plos.org/), whereas eLife articles are derived from the [eLife](https://elifesciences.org/) journal. More details/anlaysis on the content of each dataset are provided in the paper.
Both "elife" and "plos" have 6 features:
- "article": the body of the document (including the abstract), sections seperated by "/n".
- "section_headings": the title of each section, seperated by "/n".
- "keywords": keywords describing the topic of the article, seperated by "/n".
- "title" : the title of the article.
- "year" : the year the article was published.
- "summary": the lay summary of the document. | @misc{Goldsack_2022,
doi = {10.48550/ARXIV.2210.09932},
url = {https://arxiv.org/abs/2210.09932},
author = {Goldsack, Tomas and Zhang, Zhihao and Lin, Chenghua and Scarton, Carolina},
title = {Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
} | 12 | 293 | 2022-10-19T14:46:52 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: ScientificLaySummarisation
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
tags:
- abstractive-summarization
- scientific-papers
- lay-summarization
- PLOS
- eLife
task_categories:
- summarization
task_ids: []
---
# Dataset Card for "scientific_lay_summarisation"
- **Repository:** https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation
- **Paper:** [Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature](https://arxiv.org/abs/2210.09932)
- **Size of downloaded dataset files:** 850.44 MB
- **Size of the generated dataset:** 1.32 GB
- **Total amount of disk used:** 2.17 GB
### Dataset Summary
This repository contains the PLOS and eLife datasets, introduced in the EMNLP 2022 paper "[Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
](https://arxiv.org/abs/2210.09932)" .
Each dataset contains full biomedical research articles paired with expert-written lay summaries (i.e., non-technical summaries). PLOS articles are derived from various journals published by [the Public Library of Science (PLOS)](https://plos.org/), whereas eLife articles are derived from the [eLife](https://elifesciences.org/) journal. More details/analyses on the content of each dataset are provided in the paper.
Both "elife" and "plos" have 6 features:
- "article": the body of the document (including the abstract), sections separated by "/n".
- "section_headings": the title of each section, separated by "/n".
- "keywords": keywords describing the topic of the article, separated by "/n".
- "title": the title of the article.
- "year": the year the article was published.
- "summary": the lay summary of the document.
**Note:** The format of both datasets differs from that used in the original repository (given above) in order to make them compatible with the `run_summarization.py` script of Transformers. Specifically, sentence tokenization is removed via " ".join(text), and the abstract and article sections, previously lists of sentences, are combined into a single `string` feature ("article") with each section separated by "\n". For the sentence-tokenized version of the dataset, please use the original git repository.
### Supported Tasks and Leaderboards
Papers with code - [PLOS](https://paperswithcode.com/sota/lay-summarization-on-plos) and [eLife](https://paperswithcode.com/sota/lay-summarization-on-elife).
### Languages
English
## Dataset Structure
### Data Instances
#### plos
- **Size of downloaded dataset files:** 425.22 MB
- **Size of the generated dataset:** 1.05 GB
- **Total amount of disk used:** 1.47 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"summary": "In the kidney , structures known as nephrons are responsible for collecting metabolic waste . Nephrons are composed of a ...",
"article": "Kidney function depends on the nephron , which comprises a 'blood filter , a tubule that is subdivided into functionally ...",
"section_headings": "Abstract\nIntroduction\nResults\nDiscussion\nMaterials and Methods'",
"keywords": "developmental biology\ndanio (zebrafish)\nvertebrates\nteleost fishes\nnephrology",
"title": "The cdx Genes and Retinoic Acid Control the Positioning and Segmentation of the Zebrafish Pronephros",
"year": "2007"
}
```
#### elife
- **Size of downloaded dataset files:** 425.22 MB
- **Size of the generated dataset:** 275.99 MB
- **Total amount of disk used:** 1.47 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"summary": "In the USA , more deaths happen in the winter than the summer . But when deaths occur varies greatly by sex , age , cause of ...",
"article": "In temperate climates , winter deaths exceed summer ones . However , there is limited information on the timing and the ...",
"section_headings": "Abstract\nIntroduction\nResults\nDiscussion\nMaterials and methods",
"keywords": "epidemiology and global health",
"title": "National and regional seasonal dynamics of all-cause and cause-specific mortality in the USA from 1980 to 2016",
"year": "2018"
}
```
### Data Fields
The data fields are the same among all splits.
#### plos
- `article`: a `string` feature.
- `section_headings`: a `string` feature.
- `keywords`: a `string` feature.
- `title` : a `string` feature.
- `year` : a `string` feature.
- `summary`: a `string` feature.
#### elife
- `article`: a `string` feature.
- `section_headings`: a `string` feature.
- `keywords`: a `string` feature.
- `title` : a `string` feature.
- `year` : a `string` feature.
- `summary`: a `string` feature.
### Data Splits
| name |train |validation|test|
|------|-----:|---------:|---:|
|plos | 24773| 1376|1376|
|elife | 4346| 241| 241|
## 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
```
"Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature"
Tomas Goldsack, Zhihao Zhang, Chenghua Lin, Carolina Scarton
EMNLP 2022
``` | 5,937 | [
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qwedsacf/grade-school-math-instructions | 2023-02-11T01:59:26.000Z | [
"region:us"
] | qwedsacf | null | null | 27 | 293 | 2023-02-11T01:32:53 | ---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
splits:
- name: train
num_bytes: 4804916
num_examples: 8792
download_size: 2554896
dataset_size: 4804916
---
# Dataset Card for grade-school-math-instructions
OpenAI's [grade-school-math](https://github.com/openai/grade-school-math) dataset converted into instructions.
## Citation Information
```bibtex
@article{cobbe2021gsm8k,
title={Training Verifiers to Solve Math Word Problems},
author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John},
journal={arXiv preprint arXiv:2110.14168},
year={2021}
}
``` | 852 | [
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orgcatorg/multilingual | 2023-10-18T00:11:33.000Z | [
"region:us"
] | orgcatorg | null | null | 0 | 293 | 2023-09-19T18:55:56 | ---
dataset_info:
- config_name: eng_Latn-lao_Laoo
features:
- name: translation
struct:
- name: eng_Latn
dtype: string
- name: lao_Laoo
dtype: string
splits:
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num_examples: 140265
download_size: 23468883
dataset_size: 42871606
- config_name: eng_Latn-mya_Mymr
features:
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struct:
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dtype: string
- name: mya_Mymr
dtype: string
splits:
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download_size: 34667809
dataset_size: 70235556
- config_name: eng_Latn-tgl_Latn
features:
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struct:
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dtype: string
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dtype: string
splits:
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num_bytes: 759841423
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download_size: 531326540
dataset_size: 759841423
- config_name: eng_Latn-tha_Thai
features:
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struct:
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dtype: string
- name: tha_Thai
dtype: string
splits:
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num_bytes: 433620969
num_examples: 1388326
download_size: 231017202
dataset_size: 433620969
configs:
- config_name: eng_Latn-lao_Laoo
data_files:
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path: eng_Latn-lao_Laoo/train-*
- config_name: eng_Latn-mya_Mymr
data_files:
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path: eng_Latn-mya_Mymr/train-*
- config_name: eng_Latn-tgl_Latn
data_files:
- split: train
path: eng_Latn-tgl_Latn/train-*
- config_name: eng_Latn-tha_Thai
data_files:
- split: train
path: eng_Latn-tha_Thai/train-*
---
# Dataset Card for "multilingual"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,753 | [
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] |
glaiveai/glaive-code-assistant | 2023-09-27T22:51:02.000Z | [
"size_categories:100K<n<1M",
"license:apache-2.0",
"region:us"
] | glaiveai | null | null | 35 | 293 | 2023-09-21T18:56:47 | ---
license: apache-2.0
size_categories:
- 100K<n<1M
---
# Glaive-code-assistant
Glaive-code-assistant is a dataset of ~140k code problems and solutions generated using Glaive’s synthetic data generation platform.
The data is intended to be used to make models act as code assistants, and so the data is structured in a QA format where the questions are worded similar to how real users will ask code related questions.
The data has ~60% python samples.
To report any problems or suggestions in the data, join the [Glaive discord](https://discord.gg/fjQ4uf3yWD) | 566 | [
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Exr0n/wiki-entity-similarity | 2022-08-19T18:51:04.000Z | [
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:mit",
"named entities",
"similarity",
"paraphrasing",
"synonyms",
"wikipedia",
"arxiv:2004.04906",
"arxiv:2202.13581",
"region:us"
] | Exr0n | null | null | 6 | 292 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
pretty_name: 'Wiki Entity Similarity
'
size_categories:
- 10M<n<100M
source_datasets:
- original
tags:
- named entities
- similarity
- paraphrasing
- synonyms
- wikipedia
task_categories: []
task_ids: []
---
# Wiki Entity Similarity
Usage:
```py
from datasets import load_dataset
corpus = load_dataset('Exr0n/wiki-entity-similarity', '2018thresh20corpus', split='train')
assert corpus[0] == {'article': 'A1000 road', 'link_text': 'A1000', 'is_same': 1}
pairs = load_dataset('Exr0n/wiki-entity-similarity', '2018thresh20pairs', split='train')
assert corpus[0] == {'article': 'Rhinobatos', 'link_text': 'Ehinobatos beurleni', 'is_same': 1}
assert len(corpus) == 4_793_180
```
## Corpus (`name=*corpus`)
The corpora in this are generated by aggregating the link text that refers to various articles in context. For instance, if wiki article A refers to article B as C, then C is added to the list of aliases for article B, and the pair (B, C) is included in the dataset.
Following (DPR https://arxiv.org/pdf/2004.04906.pdf), we use the English Wikipedia dump from Dec. 20, 2018 as the source documents for link collection.
The dataset includes three quality levels, distinguished by the minimum number of inbound links required to include an article in the dataset. This filtering is motivated by the heuristic "better articles have more citations."
| Min. Inbound Links | Number of Articles | Number of Distinct Links |
|------------|--------------------|--------------------------|
| 5 | 1,080,073 | 5,787,081 |
| 10 | 605,775 | 4,407,409 |
| 20 | 324,949 | 3,195,545 |
## Training Pairs (`name=*pairs`)
This dataset also includes training pair datasets (with both positive and negative examples) intended for training classifiers. The train/dev/test split is 75/15/10 % of each corpus.
### Training Data Generation
The training pairs in this dataset are generated by taking each example from the corpus as a positive example, and creating a new negative example from the article title of the positive example and a random link text from a different article.
The articles featured in each split are disjoint from the other splits, and each split has the same number of positive (semantically the same) and negative (semantically different) examples.
For more details on the dataset motivation, see [the paper](https://arxiv.org/abs/2202.13581). If you use this dataset in your work, please cite it using the ArXiv reference.
Generation scripts can be found [in the GitHub repo](https://github.com/Exr0nProjects/wiki-entity-similarity).
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diwank/hinglish-dump | 2022-03-05T14:28:55.000Z | [
"license:mit",
"region:us"
] | diwank | Raw merged dump of Hinglish (hi-EN) datasets. | null | 1 | 292 | 2022-03-02T23:29:22 | ---
license: mit
---
# Hinglish Dump
Raw merged dump of Hinglish (hi-EN) datasets.
## Subsets and features
Subsets:
- crowd_transliteration
- hindi_romanized_dump
- hindi_xlit
- hinge
- hinglish_norm
- news2018
```
_FEATURE_NAMES = [
"target_hinglish",
"source_hindi",
"parallel_english",
"annotations",
"raw_input",
"alternates",
]
```
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laion/laion2B-en | 2023-08-13T10:21:14.000Z | [
"license:cc-by-4.0",
"region:us"
] | laion | null | null | 143 | 292 | 2022-03-08T22:49:04 | ---
license: cc-by-4.0
---
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Jackmin108/c4-en-validation | 2023-08-18T22:00:10.000Z | [
"region:us"
] | Jackmin108 | null | null | 0 | 292 | 2023-08-18T21:59:09 | Entry not found | 15 | [
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]
] |
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