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a98c94fac808ebea2f7c871631f960ebf0ca1a1b |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset consists of roughly 480k english (classified using nltk language classifier) lyrics with some more meta data. The meta data was taken from the million playlist challenge @ AICrowd. The lyrics were crawled using the song and artist name with the lyricsgenius python package. There is no guarantee that the lyrics are the correct one though the data was cleaned and verified. The lyrics crawled came with the song name in its payload, if the song names in the payload and from our side don't match (using the package fuzzywuzzy string matching with a score of under 60) then it wasn't included in this set of lyrics. Still some lyrics might be wrong due to the nature of the data.
49'985 rows have a list of genres, crawled from the official Spotify API. This list of genres are from the artist of the song since spotify doesn't provide genres for every individual song.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | brunokreiner/genius-lyrics | [
"region:us"
]
| 2023-01-18T22:39:24+00:00 | {} | 2023-03-07T21:57:02+00:00 | []
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| TAGS
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# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset consists of roughly 480k english (classified using nltk language classifier) lyrics with some more meta data. The meta data was taken from the million playlist challenge @ AICrowd. The lyrics were crawled using the song and artist name with the lyricsgenius python package. There is no guarantee that the lyrics are the correct one though the data was cleaned and verified. The lyrics crawled came with the song name in its payload, if the song names in the payload and from our side don't match (using the package fuzzywuzzy string matching with a score of under 60) then it wasn't included in this set of lyrics. Still some lyrics might be wrong due to the nature of the data.
49'985 rows have a list of genres, crawled from the official Spotify API. This list of genres are from the artist of the song since spotify doesn't provide genres for every individual song.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## 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
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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|
3109be33b36f6282aa9fbc36d6841c3e40cb614c | # FairFace (val set)
Original paper: [Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation](https://openaccess.thecvf.com/content/WACV2021/papers/Karkkainen_FairFace_Face_Attribute_Dataset_for_Balanced_Race_Gender_and_Age_WACV_2021_paper.pdf)
Homepage: https://github.com/joojs/fairface
Bibtex:
```
@inproceedings{karkkainenfairface,
title={FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation},
author={Karkkainen, Kimmo and Joo, Jungseock},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
year={2021},
pages={1548--1558}
}
``` | nlphuji/fairface_val_padding_025 | [
"region:us"
]
| 2023-01-18T22:46:25+00:00 | {} | 2023-01-18T22:57:00+00:00 | []
| []
| TAGS
#region-us
| # FairFace (val set)
Original paper: Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation
Homepage: URL
Bibtex:
| [
"# FairFace (val set)\n\nOriginal paper: Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation\n\nHomepage: URL\n\nBibtex:"
]
| [
"TAGS\n#region-us \n",
"# FairFace (val set)\n\nOriginal paper: Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation\n\nHomepage: URL\n\nBibtex:"
]
| [
6,
44
]
| [
"passage: TAGS\n#region-us \n# FairFace (val set)\n\nOriginal paper: Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation\n\nHomepage: URL\n\nBibtex:"
]
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|
a4496d6922555370b95ffd53d4a418e832cfc771 | # Dataset Card for "pexel_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pexel_images | [
"region:us"
]
| 2023-01-18T23:42:55+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27590932.0, "num_examples": 80}], "download_size": 27589857, "dataset_size": 27590932.0}} | 2023-01-18T23:43:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pexel_images"
More Information needed | [
"# Dataset Card for \"pexel_images\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pexel_images\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pexel_images\"\n\nMore Information needed"
]
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|
c764741cdbf1a67ea7b3659988f04f007b89b2dc | # Dataset Card for "pexel_images_prior_reg_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pexel_images_prior_reg_images | [
"region:us"
]
| 2023-01-19T00:04:10+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 459796785.0, "num_examples": 1000}], "download_size": 459802313, "dataset_size": 459796785.0}} | 2023-01-19T01:41:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pexel_images_prior_reg_images"
More Information needed | [
"# Dataset Card for \"pexel_images_prior_reg_images\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pexel_images_prior_reg_images\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pexel_images_prior_reg_images\"\n\nMore Information needed"
]
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|
e58b4e08b0b07b8df5615ee6dcad7019345538e9 | # Dataset Card for "portrait_dreambooth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/portrait_dreambooth | [
"region:us"
]
| 2023-01-19T00:17:14+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 123049169.0, "num_examples": 286}, {"name": "validation", "num_bytes": 7122908.0, "num_examples": 20}], "download_size": 123406667, "dataset_size": 130172077.0}} | 2023-01-19T00:17:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "portrait_dreambooth"
More Information needed | [
"# Dataset Card for \"portrait_dreambooth\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"portrait_dreambooth\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"portrait_dreambooth\"\n\nMore Information needed"
]
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|
09555eaa60091bd3f6df8cbd74a4f976df14fe6d | # Dataset Card for "pexel_images_lots"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pexel_images_lots | [
"region:us"
]
| 2023-01-19T00:57:13+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2466579957.125, "num_examples": 7999}], "download_size": 2418558487, "dataset_size": 2466579957.125}} | 2023-01-19T21:21:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pexel_images_lots"
More Information needed | [
"# Dataset Card for \"pexel_images_lots\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pexel_images_lots\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pexel_images_lots\"\n\nMore Information needed"
]
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|
bc857fc726710c8a4e43362ccf6259d3129213cf | annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: Tech Channels Metadata
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- youtube
- video
- video metadata
- tech
- science and tech
task_categories:
- other
task_ids: []
| alexignite/YouTube_channel_data | [
"region:us"
]
| 2023-01-19T02:15:52+00:00 | {} | 2023-01-19T16:04:04+00:00 | []
| []
| TAGS
#region-us
| annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: Tech Channels Metadata
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- youtube
- video
- video metadata
- tech
- science and tech
task_categories:
- other
task_ids: []
| []
| [
"TAGS\n#region-us \n"
]
| [
6
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|
8a17207bcc901d031abd94bbb22276e770826229 |
# Dataset Card for Douban Dushu (豆瓣读书).
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
This dataset contains book reviews from DouBan Dushu.
DouBan DuShu is a Chinese website where users can share their reviews about various kinds of books. Most of the users in this website are unprofessional book reviewers. Therefore, the comments are usually spoken Chinese or even Internet slang.
- **Repository:** https://github.com/JaniceZhao/Douban-Dushu-Dataset
- **Paper:** LSICC: A Large Scale Informal Chinese Corpus
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Chinese
## Dataset Structure
### Data Instances
```
{
'tag': '日本文学',
'book_name': '厨房',
'user_name': '林大东',
'date': '2013-03-12',
'comment': '满月没有另外两篇好看',
'star': 5,
'vote_count': 0
}
```
### Data Fields
```
{
"tag": datasets.Value("string"),
"book_name": datasets.Value("string"),
"user_name": datasets.Value("string"),
"date": datasets.Value("string"),
"comment": datasets.Value("string"),
"star": datasets.Value("int32"),
"vote_count": datasets.Value("int32"),
}
```
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
https://drive.google.com/drive/folders/1Me0aswzCCMtJt3clWiA39J5i-tbREgze
#### 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
@article{zhao2018lsicc,
title={LSICC: A Large Scale Informal Chinese Corpus},
author={Zhao, Jianyu and Ji, Zhuoran},
journal={arXiv preprint arXiv:1811.10167},
year={2018}
}
### Contributions
Thanks to [@larrylawl](https://github.com/larrylawl) for adding this dataset. | larrylawl/douban-dushu | [
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"language:zh",
"license:cc-by-4.0",
"region:us"
]
| 2023-01-19T03:13:13+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["zh"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": [], "task_categories": [], "task_ids": [], "pretty_name": "Book revies from DouBan Dushu.", "tags": []} | 2023-01-19T03:14:57+00:00 | []
| [
"zh"
]
| TAGS
#annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10M<n<100M #language-Chinese #license-cc-by-4.0 #region-us
|
# Dataset Card for Douban Dushu (豆瓣读书).
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
This dataset contains book reviews from DouBan Dushu.
DouBan DuShu is a Chinese website where users can share their reviews about various kinds of books. Most of the users in this website are unprofessional book reviewers. Therefore, the comments are usually spoken Chinese or even Internet slang.
- Repository: URL
- Paper: LSICC: A Large Scale Informal Chinese Corpus
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Chinese
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
URL
#### 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
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
@article{zhao2018lsicc,
title={LSICC: A Large Scale Informal Chinese Corpus},
author={Zhao, Jianyu and Ji, Zhuoran},
journal={arXiv preprint arXiv:1811.10167},
year={2018}
}
### Contributions
Thanks to @larrylawl for adding this dataset. | [
"# Dataset Card for Douban Dushu (豆瓣读书).",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\nThis dataset contains book reviews from DouBan Dushu. \n\nDouBan DuShu is a Chinese website where users can share their reviews about various kinds of books. Most of the users in this website are unprofessional book reviewers. Therefore, the comments are usually spoken Chinese or even Internet slang.\n\n- Repository: URL\n- Paper: LSICC: A Large Scale Informal Chinese Corpus",
"### Dataset Summary",
"### Supported Tasks and Leaderboards",
"### Languages\n\nChinese",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data\n\nURL",
"#### 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",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\n\n\n\n\n@article{zhao2018lsicc,\n title={LSICC: A Large Scale Informal Chinese Corpus},\n author={Zhao, Jianyu and Ji, Zhuoran},\n journal={arXiv preprint arXiv:1811.10167},\n year={2018}\n}",
"### Contributions\n\nThanks to @larrylawl for adding this dataset."
]
| [
"TAGS\n#annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10M<n<100M #language-Chinese #license-cc-by-4.0 #region-us \n",
"# Dataset Card for Douban Dushu (豆瓣读书).",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\nThis dataset contains book reviews from DouBan Dushu. \n\nDouBan DuShu is a Chinese website where users can share their reviews about various kinds of books. Most of the users in this website are unprofessional book reviewers. Therefore, the comments are usually spoken Chinese or even Internet slang.\n\n- Repository: URL\n- Paper: LSICC: A Large Scale Informal Chinese Corpus",
"### Dataset Summary",
"### Supported Tasks and Leaderboards",
"### Languages\n\nChinese",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data\n\nURL",
"#### 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",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\n\n\n\n\n@article{zhao2018lsicc,\n title={LSICC: A Large Scale Informal Chinese Corpus},\n author={Zhao, Jianyu and Ji, Zhuoran},\n journal={arXiv preprint arXiv:1811.10167},\n year={2018}\n}",
"### Contributions\n\nThanks to @larrylawl for adding this dataset."
]
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"passage: TAGS\n#annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10M<n<100M #language-Chinese #license-cc-by-4.0 #region-us \n# Dataset Card for Douban Dushu (豆瓣读书).## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\nThis dataset contains book reviews from DouBan Dushu. \n\nDouBan DuShu is a Chinese website where users can share their reviews about various kinds of books. Most of the users in this website are unprofessional book reviewers. Therefore, the comments are usually spoken Chinese or even Internet slang.\n\n- Repository: URL\n- Paper: LSICC: A Large Scale Informal Chinese Corpus### Dataset Summary### Supported Tasks and Leaderboards### Languages\n\nChinese## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data\n\nURL#### 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### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators"
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|
9d4f21ac57d11ed4f9ea64854fdc9f5618e61acc |
# Dataset Card for naamapadam
## 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:** https://github.com/AI4Bharat/indicner
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** Anoop Kunchukuttan
### Dataset Summary
Naamapadam is the largest publicly available Named Entity Annotated dataset for 11 Indic languages. This corpora was created by projecting named entities from English side to the Indic language side of the English-Indic languages parallel corpus. The dataset additionally contains manually labelled test set for 8 Indic languages containing 500-1000 sentences.
### Supported Tasks and Leaderboards
**Tasks:** NER on Indian languages.
**Leaderboards:** Currently there is no Leaderboard for this dataset.
### Languages
- `Assamese (as)`
- `Bengali (bn)`
- `Gujarati (gu)`
- `Kannada (kn)`
- `Hindi (hi)`
- `Malayalam (ml)`
- `Marathi (mr)`
- `Oriya (or)`
- `Punjabi (pa)`
- `Tamil (ta)`
- `Telugu (te)`
## Dataset Structure
### Data Instances
{'words': ['उन्हेनें', 'शिकांगों','में','बोरोडिन','की','पत्नी','को','तथा','वाशिंगटन','में','रूसी','व्यापार','संघ','को','पैसे','भेजे','।'],
'ner': [0, 3, 0, 1, 0, 0, 0, 0, 3, 0, 5, 6, 6, 0, 0, 0, 0],
}
### Data Fields
- `words`: Raw tokens in the dataset.
- `ner`: the NER tags for this dataset.
### Data Splits
(to be updated, see paper for correct numbers)
| Language | Train | Validation | Test |
|---:|---:|---:|---:|
| as | 10266 | 52 | 51 |
| bn | 961679 | 4859 | 607 |
| gu | 472845 | 2389 | 50 |
| hi | 985787 | 13460 | 437 |
| kn | 471763 | 2381 | 1019 |
| ml | 716652 | 3618 | 974 |
| mr | 455248 | 2300 | 1080 |
| or | 196793 | 993 | 994 |
| pa | 463534 | 2340 | 2342 |
| ta | 497882 | 2795 | 49 |
| te | 507741 | 2700 | 53 |
## Usage
You should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:
```code
pip install datasets
```
To use the dataset, please use:<br/>
```python
from datasets import load_dataset
hiner = load_dataset('ai4bharat/naamapadam')
```
## Dataset Creation
We use the parallel corpus from the Samanantar Dataset between English and the 11 major Indian languages to create the NER dataset. We annotate the English portion of the parallel corpus with existing state-of-the-art NER model. We use word-level alignments learned from the parallel corpus to project the entity labels from English to the Indian language.
### Curation Rationale
naamapadam was built from [Samanantar dataset](https://indicnlp.ai4bharat.org/samanantar/). This dataset was built for the task of Named Entity Recognition in Indic languages. The dataset was introduced to introduce new resources to the Indic languages language that was under-served for Natural Language Processing.
### Source Data
[Samanantar dataset](https://indicnlp.ai4bharat.org/samanantar/)
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
NER annotations were done following the CoNLL-2003 guidelines.
#### Who are the annotators?
The annotations for the testset have been done by volunteers who are proficient in the respective languages. We would like to thank all the volunteers:
- Anil Mhaske
- Anoop Kunchukuttan
- Archana Mhaske
- Arnav Mhaske
- Gowtham Ramesh
- Harshit Kedia
- Nitin Kedia
- Rudramurthy V
- Sangeeta Rajagopal
- Sumanth Doddapaneni
- Vindhya DS
- Yash Madhani
- Kabir Ahuja
- Shallu Rani
- Armin Virk
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to provide a large-scale Named Entity Recognition dataset for Indic languages. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data.
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
<!-- <a rel="license" float="left" href="http://creativecommons.org/publicdomain/zero/1.0/">
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</a>
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**CC0 License Statement**
<a rel="license" float="left" href="https://creativecommons.org/about/cclicenses/">
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100"/>
</a>
<br>
<br>
- We do not own any of the text from which this data has been extracted.
- We license the actual packaging of the mined data under the [Creative Commons CC0 license (“no rights reserved”)](http://creativecommons.org/publicdomain/zero/1.0).
- To the extent possible under law, <a rel="dct:publisher" href="https://ai4bharat.iitm.ac.in/"> <span property="dct:title">AI4Bharat</span></a> has waived all copyright and related or neighboring rights to <span property="dct:title">Naamapadam</span> manually collected data and existing sources.
- This work is published from: India.
### Citation Information
If you are using the Naampadam corpus, please cite the following article:
```
@misc{mhaske2022naamapadam,
doi = {10.48550/ARXIV.2212.10168},
url = {https://arxiv.org/abs/2212.10168},
author = {Mhaske, Arnav and Kedia, Harshit and Doddapaneni, Sumanth and Khapra, Mitesh M. and Kumar, Pratyush and Murthy, Rudra and Kunchukuttan, Anoop},
title = {Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages}
publisher = {arXiv},
year = {2022},
}
```
<!-- Contributors -->
### Contributors
- Arnav Mhaske <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
- Harshit Kedia <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
- Sumanth Doddapaneni <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
- Mitesh M. Khapra <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
- Pratyush Kumar <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub>
- Rudra Murthy <sub> ([AI4Bharat](https://ai4bharat.org), [IBM](https://www.ibm.com))</sub>
- Anoop Kunchukuttan <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub>
This work is the outcome of a volunteer effort as part of the [AI4Bharat initiative](https://ai4bharat.iitm.ac.in).
<!-- Contact -->
### Contact
- Anoop Kunchukuttan ([[email protected]](mailto:[email protected]))
- Rudra Murthy V ([[email protected]](mailto:[email protected])) | ai4bharat/naamapadam | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
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"2212.10168"
]
| [
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| TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Assamese #language-Bengali #language-Gujarati #language-Hindi #language-Kannada #language-Malayalam #language-Marathi #language-Oriya (macrolanguage) #language-Panjabi #language-Tamil #language-Telugu #license-cc0-1.0 #arxiv-2212.10168 #region-us
| Dataset Card for naamapadam
===========================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
Dataset Description
-------------------
* Homepage:
* Repository: URL
* Paper:
* Leaderboard:
* Point of Contact: Anoop Kunchukuttan
### Dataset Summary
Naamapadam is the largest publicly available Named Entity Annotated dataset for 11 Indic languages. This corpora was created by projecting named entities from English side to the Indic language side of the English-Indic languages parallel corpus. The dataset additionally contains manually labelled test set for 8 Indic languages containing 500-1000 sentences.
### Supported Tasks and Leaderboards
Tasks: NER on Indian languages.
Leaderboards: Currently there is no Leaderboard for this dataset.
### Languages
* 'Assamese (as)'
* 'Bengali (bn)'
* 'Gujarati (gu)'
* 'Kannada (kn)'
* 'Hindi (hi)'
* 'Malayalam (ml)'
* 'Marathi (mr)'
* 'Oriya (or)'
* 'Punjabi (pa)'
* 'Tamil (ta)'
* 'Telugu (te)'
Dataset Structure
-----------------
### Data Instances
{'words': ['उन्हेनें', 'शिकांगों','में','बोरोडिन','की','पत्नी','को','तथा','वाशिंगटन','में','रूसी','व्यापार','संघ','को','पैसे','भेजे','।'],
'ner': [0, 3, 0, 1, 0, 0, 0, 0, 3, 0, 5, 6, 6, 0, 0, 0, 0],
}
### Data Fields
* 'words': Raw tokens in the dataset.
* 'ner': the NER tags for this dataset.
### Data Splits
(to be updated, see paper for correct numbers)
Usage
-----
You should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:
To use the dataset, please use:
Dataset Creation
----------------
We use the parallel corpus from the Samanantar Dataset between English and the 11 major Indian languages to create the NER dataset. We annotate the English portion of the parallel corpus with existing state-of-the-art NER model. We use word-level alignments learned from the parallel corpus to project the entity labels from English to the Indian language.
### Curation Rationale
naamapadam was built from Samanantar dataset. This dataset was built for the task of Named Entity Recognition in Indic languages. The dataset was introduced to introduce new resources to the Indic languages language that was under-served for Natural Language Processing.
### Source Data
Samanantar dataset
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
NER annotations were done following the CoNLL-2003 guidelines.
#### Who are the annotators?
The annotations for the testset have been done by volunteers who are proficient in the respective languages. We would like to thank all the volunteers:
* Anil Mhaske
* Anoop Kunchukuttan
* Archana Mhaske
* Arnav Mhaske
* Gowtham Ramesh
* Harshit Kedia
* Nitin Kedia
* Rudramurthy V
* Sangeeta Rajagopal
* Sumanth Doddapaneni
* Vindhya DS
* Yash Madhani
* Kabir Ahuja
* Shallu Rani
* Armin Virk
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
The purpose of this dataset is to provide a large-scale Named Entity Recognition dataset for Indic languages. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data.
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
CC0 License Statement
<a rel="license" float="left" href="URL
<img src="URL style="border-style: none;" alt="CC0" width="100"/>
* We do not own any of the text from which this data has been extracted.
* We license the actual packaging of the mined data under the Creative Commons CC0 license (“no rights reserved”).
* To the extent possible under law, <a rel="dct:publisher" href="URL AI4Bharat has waived all copyright and related or neighboring rights to Naamapadam manually collected data and existing sources.
* This work is published from: India.
If you are using the Naampadam corpus, please cite the following article:
### Contributors
* Arnav Mhaske (AI4Bharat, IITM)
* Harshit Kedia (AI4Bharat, IITM)
* Sumanth Doddapaneni (AI4Bharat, IITM)
* Mitesh M. Khapra (AI4Bharat, IITM)
* Pratyush Kumar (AI4Bharat, Microsoft, IITM)
* Rudra Murthy (AI4Bharat, IBM)
* Anoop Kunchukuttan (AI4Bharat, Microsoft, IITM)
This work is the outcome of a volunteer effort as part of the AI4Bharat initiative.
### Contact
* Anoop Kunchukuttan (anoop.kunchukuttan@URL)
* Rudra Murthy V (rmurthyv@URL)
| [
"### Dataset Summary\n\n\nNaamapadam is the largest publicly available Named Entity Annotated dataset for 11 Indic languages. This corpora was created by projecting named entities from English side to the Indic language side of the English-Indic languages parallel corpus. The dataset additionally contains manually labelled test set for 8 Indic languages containing 500-1000 sentences.",
"### Supported Tasks and Leaderboards\n\n\nTasks: NER on Indian languages.\n\n\nLeaderboards: Currently there is no Leaderboard for this dataset.",
"### Languages\n\n\n* 'Assamese (as)'\n* 'Bengali (bn)'\n* 'Gujarati (gu)'\n* 'Kannada (kn)'\n* 'Hindi (hi)'\n* 'Malayalam (ml)'\n* 'Marathi (mr)'\n* 'Oriya (or)'\n* 'Punjabi (pa)'\n* 'Tamil (ta)'\n* 'Telugu (te)'\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\n{'words': ['उन्हेनें', 'शिकांगों','में','बोरोडिन','की','पत्नी','को','तथा','वाशिंगटन','में','रूसी','व्यापार','संघ','को','पैसे','भेजे','।'],\n'ner': [0, 3, 0, 1, 0, 0, 0, 0, 3, 0, 5, 6, 6, 0, 0, 0, 0],\n}",
"### Data Fields\n\n\n* 'words': Raw tokens in the dataset.\n* 'ner': the NER tags for this dataset.",
"### Data Splits\n\n\n(to be updated, see paper for correct numbers)\n\n\n\nUsage\n-----\n\n\nYou should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:\n\n\nTo use the dataset, please use: \n\n\n\nDataset Creation\n----------------\n\n\nWe use the parallel corpus from the Samanantar Dataset between English and the 11 major Indian languages to create the NER dataset. We annotate the English portion of the parallel corpus with existing state-of-the-art NER model. We use word-level alignments learned from the parallel corpus to project the entity labels from English to the Indian language.",
"### Curation Rationale\n\n\nnaamapadam was built from Samanantar dataset. This dataset was built for the task of Named Entity Recognition in Indic languages. The dataset was introduced to introduce new resources to the Indic languages language that was under-served for Natural Language Processing.",
"### Source Data\n\n\nSamanantar dataset",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process\n\n\nNER annotations were done following the CoNLL-2003 guidelines.",
"#### Who are the annotators?\n\n\nThe annotations for the testset have been done by volunteers who are proficient in the respective languages. We would like to thank all the volunteers:\n\n\n* Anil Mhaske\n* Anoop Kunchukuttan\n* Archana Mhaske\n* Arnav Mhaske\n* Gowtham Ramesh\n* Harshit Kedia\n* Nitin Kedia\n* Rudramurthy V\n* Sangeeta Rajagopal\n* Sumanth Doddapaneni\n* Vindhya DS\n* Yash Madhani\n* Kabir Ahuja\n* Shallu Rani\n* Armin Virk",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nThe purpose of this dataset is to provide a large-scale Named Entity Recognition dataset for Indic languages. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data.",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCC0 License Statement\n<a rel=\"license\" float=\"left\" href=\"URL\n<img src=\"URL style=\"border-style: none;\" alt=\"CC0\" width=\"100\"/>\n\n \n\n \n\n\n\n* We do not own any of the text from which this data has been extracted.\n* We license the actual packaging of the mined data under the Creative Commons CC0 license (“no rights reserved”).\n* To the extent possible under law, <a rel=\"dct:publisher\" href=\"URL AI4Bharat has waived all copyright and related or neighboring rights to Naamapadam manually collected data and existing sources.\n* This work is published from: India.\n\n\nIf you are using the Naampadam corpus, please cite the following article:",
"### Contributors\n\n\n* Arnav Mhaske (AI4Bharat, IITM)\n* Harshit Kedia (AI4Bharat, IITM)\n* Sumanth Doddapaneni (AI4Bharat, IITM)\n* Mitesh M. Khapra (AI4Bharat, IITM)\n* Pratyush Kumar (AI4Bharat, Microsoft, IITM)\n* Rudra Murthy (AI4Bharat, IBM)\n* Anoop Kunchukuttan (AI4Bharat, Microsoft, IITM)\n\n\nThis work is the outcome of a volunteer effort as part of the AI4Bharat initiative.",
"### Contact\n\n\n* Anoop Kunchukuttan (anoop.kunchukuttan@URL)\n* Rudra Murthy V (rmurthyv@URL)"
]
| [
"TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Assamese #language-Bengali #language-Gujarati #language-Hindi #language-Kannada #language-Malayalam #language-Marathi #language-Oriya (macrolanguage) #language-Panjabi #language-Tamil #language-Telugu #license-cc0-1.0 #arxiv-2212.10168 #region-us \n",
"### Dataset Summary\n\n\nNaamapadam is the largest publicly available Named Entity Annotated dataset for 11 Indic languages. This corpora was created by projecting named entities from English side to the Indic language side of the English-Indic languages parallel corpus. The dataset additionally contains manually labelled test set for 8 Indic languages containing 500-1000 sentences.",
"### Supported Tasks and Leaderboards\n\n\nTasks: NER on Indian languages.\n\n\nLeaderboards: Currently there is no Leaderboard for this dataset.",
"### Languages\n\n\n* 'Assamese (as)'\n* 'Bengali (bn)'\n* 'Gujarati (gu)'\n* 'Kannada (kn)'\n* 'Hindi (hi)'\n* 'Malayalam (ml)'\n* 'Marathi (mr)'\n* 'Oriya (or)'\n* 'Punjabi (pa)'\n* 'Tamil (ta)'\n* 'Telugu (te)'\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\n{'words': ['उन्हेनें', 'शिकांगों','में','बोरोडिन','की','पत्नी','को','तथा','वाशिंगटन','में','रूसी','व्यापार','संघ','को','पैसे','भेजे','।'],\n'ner': [0, 3, 0, 1, 0, 0, 0, 0, 3, 0, 5, 6, 6, 0, 0, 0, 0],\n}",
"### Data Fields\n\n\n* 'words': Raw tokens in the dataset.\n* 'ner': the NER tags for this dataset.",
"### Data Splits\n\n\n(to be updated, see paper for correct numbers)\n\n\n\nUsage\n-----\n\n\nYou should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:\n\n\nTo use the dataset, please use: \n\n\n\nDataset Creation\n----------------\n\n\nWe use the parallel corpus from the Samanantar Dataset between English and the 11 major Indian languages to create the NER dataset. We annotate the English portion of the parallel corpus with existing state-of-the-art NER model. We use word-level alignments learned from the parallel corpus to project the entity labels from English to the Indian language.",
"### Curation Rationale\n\n\nnaamapadam was built from Samanantar dataset. This dataset was built for the task of Named Entity Recognition in Indic languages. The dataset was introduced to introduce new resources to the Indic languages language that was under-served for Natural Language Processing.",
"### Source Data\n\n\nSamanantar dataset",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process\n\n\nNER annotations were done following the CoNLL-2003 guidelines.",
"#### Who are the annotators?\n\n\nThe annotations for the testset have been done by volunteers who are proficient in the respective languages. We would like to thank all the volunteers:\n\n\n* Anil Mhaske\n* Anoop Kunchukuttan\n* Archana Mhaske\n* Arnav Mhaske\n* Gowtham Ramesh\n* Harshit Kedia\n* Nitin Kedia\n* Rudramurthy V\n* Sangeeta Rajagopal\n* Sumanth Doddapaneni\n* Vindhya DS\n* Yash Madhani\n* Kabir Ahuja\n* Shallu Rani\n* Armin Virk",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nThe purpose of this dataset is to provide a large-scale Named Entity Recognition dataset for Indic languages. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data.",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nCC0 License Statement\n<a rel=\"license\" float=\"left\" href=\"URL\n<img src=\"URL style=\"border-style: none;\" alt=\"CC0\" width=\"100\"/>\n\n \n\n \n\n\n\n* We do not own any of the text from which this data has been extracted.\n* We license the actual packaging of the mined data under the Creative Commons CC0 license (“no rights reserved”).\n* To the extent possible under law, <a rel=\"dct:publisher\" href=\"URL AI4Bharat has waived all copyright and related or neighboring rights to Naamapadam manually collected data and existing sources.\n* This work is published from: India.\n\n\nIf you are using the Naampadam corpus, please cite the following article:",
"### Contributors\n\n\n* Arnav Mhaske (AI4Bharat, IITM)\n* Harshit Kedia (AI4Bharat, IITM)\n* Sumanth Doddapaneni (AI4Bharat, IITM)\n* Mitesh M. Khapra (AI4Bharat, IITM)\n* Pratyush Kumar (AI4Bharat, Microsoft, IITM)\n* Rudra Murthy (AI4Bharat, IBM)\n* Anoop Kunchukuttan (AI4Bharat, Microsoft, IITM)\n\n\nThis work is the outcome of a volunteer effort as part of the AI4Bharat initiative.",
"### Contact\n\n\n* Anoop Kunchukuttan (anoop.kunchukuttan@URL)\n* Rudra Murthy V (rmurthyv@URL)"
]
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"passage: TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-original #language-Assamese #language-Bengali #language-Gujarati #language-Hindi #language-Kannada #language-Malayalam #language-Marathi #language-Oriya (macrolanguage) #language-Panjabi #language-Tamil #language-Telugu #license-cc0-1.0 #arxiv-2212.10168 #region-us \n### Dataset Summary\n\n\nNaamapadam is the largest publicly available Named Entity Annotated dataset for 11 Indic languages. This corpora was created by projecting named entities from English side to the Indic language side of the English-Indic languages parallel corpus. The dataset additionally contains manually labelled test set for 8 Indic languages containing 500-1000 sentences.### Supported Tasks and Leaderboards\n\n\nTasks: NER on Indian languages.\n\n\nLeaderboards: Currently there is no Leaderboard for this dataset.### Languages\n\n\n* 'Assamese (as)'\n* 'Bengali (bn)'\n* 'Gujarati (gu)'\n* 'Kannada (kn)'\n* 'Hindi (hi)'\n* 'Malayalam (ml)'\n* 'Marathi (mr)'\n* 'Oriya (or)'\n* 'Punjabi (pa)'\n* 'Tamil (ta)'\n* 'Telugu (te)'\n\n\nDataset Structure\n-----------------",
"passage: ### Data Instances\n\n\n{'words': ['उन्हेनें', 'शिकांगों','में','बोरोडिन','की','पत्नी','को','तथा','वाशिंगटन','में','रूसी','व्यापार','संघ','को','पैसे','भेजे','।'],\n'ner': [0, 3, 0, 1, 0, 0, 0, 0, 3, 0, 5, 6, 6, 0, 0, 0, 0],\n}### Data Fields\n\n\n* 'words': Raw tokens in the dataset.\n* 'ner': the NER tags for this dataset.### Data Splits\n\n\n(to be updated, see paper for correct numbers)\n\n\n\nUsage\n-----\n\n\nYou should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:\n\n\nTo use the dataset, please use: \n\n\n\nDataset Creation\n----------------\n\n\nWe use the parallel corpus from the Samanantar Dataset between English and the 11 major Indian languages to create the NER dataset. We annotate the English portion of the parallel corpus with existing state-of-the-art NER model. We use word-level alignments learned from the parallel corpus to project the entity labels from English to the Indian language.### Curation Rationale\n\n\nnaamapadam was built from Samanantar dataset. This dataset was built for the task of Named Entity Recognition in Indic languages. The dataset was introduced to introduce new resources to the Indic languages language that was under-served for Natural Language Processing.### Source Data\n\n\nSamanantar dataset#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process\n\n\nNER annotations were done following the CoNLL-2003 guidelines.#### Who are the annotators?\n\n\nThe annotations for the testset have been done by volunteers who are proficient in the respective languages. We would like to thank all the volunteers:\n\n\n* Anil Mhaske\n* Anoop Kunchukuttan\n* Archana Mhaske\n* Arnav Mhaske\n* Gowtham Ramesh\n* Harshit Kedia\n* Nitin Kedia\n* Rudramurthy V\n* Sangeeta Rajagopal\n* Sumanth Doddapaneni\n* Vindhya DS\n* Yash Madhani\n* Kabir Ahuja\n* Shallu Rani\n* Armin Virk### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------"
]
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|
0464588cd231df7fdda12d4f08dbcd53997a9f2d | # Dataset Card for "openwebtext-tokenized-9b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | NeelNanda/openwebtext-tokenized-9b | [
"region:us"
]
| 2023-01-19T03:18:45+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "uint16"}], "splits": [{"name": "train", "num_bytes": 18125188776, "num_examples": 8832938}], "download_size": 17426592454, "dataset_size": 18125188776}} | 2023-01-19T07:23:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "openwebtext-tokenized-9b"
More Information needed | [
"# Dataset Card for \"openwebtext-tokenized-9b\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"openwebtext-tokenized-9b\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"openwebtext-tokenized-9b\"\n\nMore Information needed"
]
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|
a9a51e65841e7c3ab02c5d6a808d319b145f6f9e |
# Princess Jai Lee Embedding
Fine-tuned textual inversion based on a character from [3ee Games](https://3ee.com), Princess Jai Lee.
## Embedding Usage
Use the token ```jaileefunkprincess```
All sample images also use the bad prompt embedding: https://huggingface.co/datasets/Nerfgun3/bad_prompt#version-2
---
☕ If you enjoy this model, buy me a coffee [](https://ko-fi.com/3eegames)
---
## 🧾 Prompt example:
**The queen has returned**
```Perfectly-centered close up portrait of a real life godly woman (jaileefunkprincess :1.1)with long purple hair and wearing shining armor descending from heaven, lifelike, super highly detailed, professional digital painting, artstation, concept art, Unreal Engine 5, Photorealism, HD quality, 8k resolution, cinema 4d, 3D, beautiful, cinematic, art by artgerm and greg rutkowski and alphonse mucha and loish and WLOP, dynamic pose```
Negative prompt:
```(bad_prompt_version2:0.8), lowres, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ((ugly)), ((duplicate)), ((morbid)), ((mutilated)), out of frame, extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((mutation))), (((deformed))), ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), extra limbs, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck)))```
_Steps: 80, Sampler: DPM adaptive, CFG scale: 10.5, Seed: 945244310, Size: 512x512, Model hash: d0b457ae_ (Model hash: protogen-x53-photorealism-official-release - https://civitai.com/models/3816/protogen-x53-photorealism-official-release)
---
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
- You can't use the model to deliberately produce nor share illegal or harmful outputs or content
- The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
- You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) | zuleo/princess-jai-lee | [
"license:creativeml-openrail-m",
"stable-diffusion",
"embedding",
"text-to-image",
"image-to-image",
"art",
"artistic",
"region:us"
]
| 2023-01-19T03:27:07+00:00 | {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "embedding", "text-to-image", "image-to-image", "art", "artistic"]} | 2023-01-19T04:11:30+00:00 | []
| []
| TAGS
#license-creativeml-openrail-m #stable-diffusion #embedding #text-to-image #image-to-image #art #artistic #region-us
|
# Princess Jai Lee Embedding
Fine-tuned textual inversion based on a character from 3ee Games, Princess Jai Lee.
!Detailed Samples
## Embedding Usage
Use the token
All sample images also use the bad prompt embedding: URL
---
If you enjoy this model, buy me a coffee 
Please read the full license here | [
"# Princess Jai Lee Embedding\n\nFine-tuned textual inversion based on a character from 3ee Games, Princess Jai Lee.\n\n!Detailed Samples",
"## Embedding Usage\n\nUse the token \n\nAll sample images also use the bad prompt embedding: URL\n\n---\n\n If you enjoy this model, buy me a coffee ![Buy a coffee](URL\n\n---",
"## Prompt example:\n\nThe queen has returned\n\n\n\nNegative prompt:\n\n\n\n_Steps: 80, Sampler: DPM adaptive, CFG scale: 10.5, Seed: 945244310, Size: 512x512, Model hash: d0b457ae_ (Model hash: protogen-x53-photorealism-official-release - URL\n\n---",
"## License\n\nThis model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n- You can't use the model to deliberately produce nor share illegal or harmful outputs or content \n- The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n- You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here"
]
| [
"TAGS\n#license-creativeml-openrail-m #stable-diffusion #embedding #text-to-image #image-to-image #art #artistic #region-us \n",
"# Princess Jai Lee Embedding\n\nFine-tuned textual inversion based on a character from 3ee Games, Princess Jai Lee.\n\n!Detailed Samples",
"## Embedding Usage\n\nUse the token \n\nAll sample images also use the bad prompt embedding: URL\n\n---\n\n If you enjoy this model, buy me a coffee ![Buy a coffee](URL\n\n---",
"## Prompt example:\n\nThe queen has returned\n\n\n\nNegative prompt:\n\n\n\n_Steps: 80, Sampler: DPM adaptive, CFG scale: 10.5, Seed: 945244310, Size: 512x512, Model hash: d0b457ae_ (Model hash: protogen-x53-photorealism-official-release - URL\n\n---",
"## License\n\nThis model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\nThe CreativeML OpenRAIL License specifies: \n\n- You can't use the model to deliberately produce nor share illegal or harmful outputs or content \n- The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n- You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\nPlease read the full license here"
]
| [
45,
34,
44,
84,
179
]
| [
"passage: TAGS\n#license-creativeml-openrail-m #stable-diffusion #embedding #text-to-image #image-to-image #art #artistic #region-us \n# Princess Jai Lee Embedding\n\nFine-tuned textual inversion based on a character from 3ee Games, Princess Jai Lee.\n\n!Detailed Samples## Embedding Usage\n\nUse the token \n\nAll sample images also use the bad prompt embedding: URL\n\n---\n\n If you enjoy this model, buy me a coffee \nPlease read the full license here"
]
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|
f953fdcae786181a33ebce6f988ca9b84f3caa6e | # Dataset Card for 1000 Website Screenshots with Metadata
## Dataset Description
- **Homepage:** [silatus.com](https://silatus.com/datasets)
- **Point of Contact:** [[email protected]](mailto:[email protected])
### Dataset Summary
Silatus is sharing, for free, a segment of a dataset that we are using to train a generative AI model for text-to-mockup conversions. This dataset was collected in December 2022 and early January 2023, so it contains very recent data from 1,000 of the world's most popular websites. You can get our larger 10,000 website dataset for free at: [https://silatus.com/datasets](https://silatus.com/datasets)
This dataset includes:
**High-res screenshots**
- 1024x1024px
- Loaded Javascript
- Loaded Images
**Text metadata**
- Site title
- Navbar content
- Full page text data
- Page description
**Visual metadata**
- Content (images, videos, inputs, buttons) absolute & relative positions
- Color profile
- Base font | silatus/1k_Website_Screenshots_and_Metadata | [
"task_categories:text-to-image",
"task_categories:image-classification",
"task_categories:image-segmentation",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-sa-4.0",
"screenshots",
"metadata",
"websites",
"webpages",
"region:us"
]
| 2023-01-19T04:33:07+00:00 | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image", "image-classification", "image-segmentation"], "pretty_name": "1000 Website Screenshots with Metadata", "tags": ["screenshots", "metadata", "websites", "webpages"]} | 2023-01-19T05:20:33+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #task_categories-image-classification #task_categories-image-segmentation #size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-4.0 #screenshots #metadata #websites #webpages #region-us
| # Dataset Card for 1000 Website Screenshots with Metadata
## Dataset Description
- Homepage: URL
- Point of Contact: datasets@URL
### Dataset Summary
Silatus is sharing, for free, a segment of a dataset that we are using to train a generative AI model for text-to-mockup conversions. This dataset was collected in December 2022 and early January 2023, so it contains very recent data from 1,000 of the world's most popular websites. You can get our larger 10,000 website dataset for free at: URL
This dataset includes:
High-res screenshots
- 1024x1024px
- Loaded Javascript
- Loaded Images
Text metadata
- Site title
- Navbar content
- Full page text data
- Page description
Visual metadata
- Content (images, videos, inputs, buttons) absolute & relative positions
- Color profile
- Base font | [
"# Dataset Card for 1000 Website Screenshots with Metadata",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: datasets@URL",
"### Dataset Summary\n\nSilatus is sharing, for free, a segment of a dataset that we are using to train a generative AI model for text-to-mockup conversions. This dataset was collected in December 2022 and early January 2023, so it contains very recent data from 1,000 of the world's most popular websites. You can get our larger 10,000 website dataset for free at: URL\n\nThis dataset includes:\n\nHigh-res screenshots\n\n- 1024x1024px\n- Loaded Javascript\n- Loaded Images\n\nText metadata\n\n- Site title\n- Navbar content\n- Full page text data\n- Page description\n\nVisual metadata\n\n- Content (images, videos, inputs, buttons) absolute & relative positions\n- Color profile\n- Base font"
]
| [
"TAGS\n#task_categories-text-to-image #task_categories-image-classification #task_categories-image-segmentation #size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-4.0 #screenshots #metadata #websites #webpages #region-us \n",
"# Dataset Card for 1000 Website Screenshots with Metadata",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: datasets@URL",
"### Dataset Summary\n\nSilatus is sharing, for free, a segment of a dataset that we are using to train a generative AI model for text-to-mockup conversions. This dataset was collected in December 2022 and early January 2023, so it contains very recent data from 1,000 of the world's most popular websites. You can get our larger 10,000 website dataset for free at: URL\n\nThis dataset includes:\n\nHigh-res screenshots\n\n- 1024x1024px\n- Loaded Javascript\n- Loaded Images\n\nText metadata\n\n- Site title\n- Navbar content\n- Full page text data\n- Page description\n\nVisual metadata\n\n- Content (images, videos, inputs, buttons) absolute & relative positions\n- Color profile\n- Base font"
]
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"passage: TAGS\n#task_categories-text-to-image #task_categories-image-classification #task_categories-image-segmentation #size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-4.0 #screenshots #metadata #websites #webpages #region-us \n# Dataset Card for 1000 Website Screenshots with Metadata## Dataset Description\n\n- Homepage: URL\n- Point of Contact: datasets@URL### Dataset Summary\n\nSilatus is sharing, for free, a segment of a dataset that we are using to train a generative AI model for text-to-mockup conversions. This dataset was collected in December 2022 and early January 2023, so it contains very recent data from 1,000 of the world's most popular websites. You can get our larger 10,000 website dataset for free at: URL\n\nThis dataset includes:\n\nHigh-res screenshots\n\n- 1024x1024px\n- Loaded Javascript\n- Loaded Images\n\nText metadata\n\n- Site title\n- Navbar content\n- Full page text data\n- Page description\n\nVisual metadata\n\n- Content (images, videos, inputs, buttons) absolute & relative positions\n- Color profile\n- Base font"
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35caabd7685b451d5b872964683d202788950979 |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** https://laion.ai/
- **Repository:** https://github.com/kayjay-is-here/changemyview-converter
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
This is a collection of subreddit data from r/changemyview that has been formatted for use within OpenAssistant's training models.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
[More Information Needed]
### Data Instances
[More Information Needed]
### Data Fields
`INSTRUCTION`: The title of the post and the accompanying body text.
`RESPONSE`: A list of all the posts that contain text that argues against `INSTRUCTION`
`SOURCE`: A permalink to the reddit post of `INSTRUCTION`
`METADATA`: Metadata of the post, such as the ML scored toxicity score
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | kjl3080/OA_CMV_Arguments | [
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| 2023-01-19T05:18:11+00:00 | {} | 2023-01-21T02:16:59+00:00 | []
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# Dataset Card for Dataset Name
## Dataset Description
- Homepage: URL
- Repository: URL
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- Leaderboard:
- Point of Contact: yoko.nasana2@URL
### Dataset Summary
This is a collection of subreddit data from r/changemyview that has been formatted for use within OpenAssistant's training models.
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English
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'RESPONSE': A list of all the posts that contain text that argues against 'INSTRUCTION'
'SOURCE': A permalink to the reddit post of 'INSTRUCTION'
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### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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|
4c0b2776d46015c384a6b28b16014a6c82a65587 |
# Dataset Card for Dataset Name
## Dataset Description
- **Repository:** https://github.com/americanas-tech/b2w-reviews01
- **Paper:** http://comissoes.sbc.org.br/ce-pln/stil2019/proceedings-stil-2019-Final-Publicacao.pdf
- **Point of Contact:** Livy Real
### Dataset Summary
B2W-Reviews01 is an open corpus of product reviews. It contains more than 130k e-commerce customer reviews, collected from the Americanas.com website between January and May, 2018. B2W-Reviews01 offers rich information about the reviewer profile, such as gender, age, and geographical location. The corpus also has two different review rates:
* the usual 5-point scale rate, represented by stars in most e-commerce websites,
* a "recommend to a friend" label, a "yes or no" question representing the willingness of the customer to recommend the product to someone else.
### Supported Tasks and Leaderboards
* Sentiment Analysis
* Topic Modeling
### Languages
* Portuguese
## Dataset Structure
### Data Instances
```
{'submission_date': '2018-01-02 06:23:22',
'reviewer_id': '6adc7901926fc1697d34181fbd88895976b4f3f31f0102d90217d248a1fad156',
'product_id': '123911277',
'product_name': 'Triciclo Gangorra Belfix Cabeça Cachorro Rosa',
'product_brand': 'belfix',
'site_category_lv1': 'Brinquedos',
'site_category_lv2': 'Mini Veículos',
'review_title': 'O produto não foi entregue',
'overall_rating': 1,
'recommend_to_a_friend': 'Yes',
'review_text': 'Incrível o descaso com o consumidor. O produto não chegou, apesar de já ter sido pago. Não recebo qualquer informação sobre onde se encontra o produto, ou qualquer compensação do vendedor. Não recomendo.',
'reviewer_birth_year': 1981,
'reviewer_gender': 'M',
'reviewer_state': 'RJ'}
```
### Data Fields
* **submission_date**: the date and time when the review was submitted. `"%Y-%m-%d %H:%M:%S"`.
* **reviewer_id**: a unique identifier for the reviewer.
* **product_id**: a unique identifier for the product being reviewed.
* **product_name**: the name of the product being reviewed.
* **product_brand**: the brand of the product being reviewed.
* **site_category_lv1**: the highest level category for the product on the site where the review is being submitted.
* **site_category_lv2**: the second level category for the product on the site where the review is being submitted.
* **review_title**: the title of the review.
* **overall_rating**: the overall star rating given by the reviewer on a scale of 1 to 5.
* **recommend_to_a_friend**: whether or not the reviewer would recommend the product to a friend (Yes/No).
* **review_text**: the full text of the review.
* **reviewer_birth_year**: the birth year of the reviewer.
* **reviewer_gender**: the gender of the reviewer (F/M).
* **reviewer_state**: the Brazilian state of the reviewer (e.g. RJ).
### Data Splits
| name |train|
|---------|----:|
|b2w-reviews01|132373|
### Citation Information
```
@inproceedings{real2019b2w,
title={B2W-reviews01: an open product reviews corpus},
author={Real, Livy and Oshiro, Marcio and Mafra, Alexandre},
booktitle={STIL-Symposium in Information and Human Language Technology},
year={2019}
}
```
### Contributions
Thanks to [@ruanchaves](https://github.com/ruanchaves) for adding this dataset. | ruanchaves/b2w-reviews01 | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-scoring",
"task_ids:intent-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"language:pt",
"license:cc-by-4.0",
"reviews",
"doi:10.57967/hf/0282",
"region:us"
]
| 2023-01-19T07:55:43+00:00 | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["pt"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100M<n<1B"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis", "sentiment-scoring", "intent-classification", "topic-classification"], "pretty_name": "B2W-Reviews01", "tags": ["reviews"]} | 2023-01-20T18:22:37+00:00 | []
| [
"pt"
]
| TAGS
#task_categories-text-classification #task_ids-sentiment-analysis #task_ids-sentiment-scoring #task_ids-intent-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100M<n<1B #source_datasets-original #language-Portuguese #license-cc-by-4.0 #reviews #doi-10.57967/hf/0282 #region-us
| Dataset Card for Dataset Name
=============================
Dataset Description
-------------------
* Repository: URL
* Paper: URL
* Point of Contact: Livy Real
### Dataset Summary
B2W-Reviews01 is an open corpus of product reviews. It contains more than 130k e-commerce customer reviews, collected from the URL website between January and May, 2018. B2W-Reviews01 offers rich information about the reviewer profile, such as gender, age, and geographical location. The corpus also has two different review rates:
* the usual 5-point scale rate, represented by stars in most e-commerce websites,
* a "recommend to a friend" label, a "yes or no" question representing the willingness of the customer to recommend the product to someone else.
### Supported Tasks and Leaderboards
* Sentiment Analysis
* Topic Modeling
### Languages
* Portuguese
Dataset Structure
-----------------
### Data Instances
### Data Fields
* submission\_date: the date and time when the review was submitted. '"%Y-%m-%d %H:%M:%S"'.
* reviewer\_id: a unique identifier for the reviewer.
* product\_id: a unique identifier for the product being reviewed.
* product\_name: the name of the product being reviewed.
* product\_brand: the brand of the product being reviewed.
* site\_category\_lv1: the highest level category for the product on the site where the review is being submitted.
* site\_category\_lv2: the second level category for the product on the site where the review is being submitted.
* review\_title: the title of the review.
* overall\_rating: the overall star rating given by the reviewer on a scale of 1 to 5.
* recommend\_to\_a\_friend: whether or not the reviewer would recommend the product to a friend (Yes/No).
* review\_text: the full text of the review.
* reviewer\_birth\_year: the birth year of the reviewer.
* reviewer\_gender: the gender of the reviewer (F/M).
* reviewer\_state: the Brazilian state of the reviewer (e.g. RJ).
### Data Splits
### Contributions
Thanks to @ruanchaves for adding this dataset.
| [
"### Dataset Summary\n\n\nB2W-Reviews01 is an open corpus of product reviews. It contains more than 130k e-commerce customer reviews, collected from the URL website between January and May, 2018. B2W-Reviews01 offers rich information about the reviewer profile, such as gender, age, and geographical location. The corpus also has two different review rates:\n\n\n* the usual 5-point scale rate, represented by stars in most e-commerce websites,\n* a \"recommend to a friend\" label, a \"yes or no\" question representing the willingness of the customer to recommend the product to someone else.",
"### Supported Tasks and Leaderboards\n\n\n* Sentiment Analysis\n* Topic Modeling",
"### Languages\n\n\n* Portuguese\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields\n\n\n* submission\\_date: the date and time when the review was submitted. '\"%Y-%m-%d %H:%M:%S\"'.\n* reviewer\\_id: a unique identifier for the reviewer.\n* product\\_id: a unique identifier for the product being reviewed.\n* product\\_name: the name of the product being reviewed.\n* product\\_brand: the brand of the product being reviewed.\n* site\\_category\\_lv1: the highest level category for the product on the site where the review is being submitted.\n* site\\_category\\_lv2: the second level category for the product on the site where the review is being submitted.\n* review\\_title: the title of the review.\n* overall\\_rating: the overall star rating given by the reviewer on a scale of 1 to 5.\n* recommend\\_to\\_a\\_friend: whether or not the reviewer would recommend the product to a friend (Yes/No).\n* review\\_text: the full text of the review.\n* reviewer\\_birth\\_year: the birth year of the reviewer.\n* reviewer\\_gender: the gender of the reviewer (F/M).\n* reviewer\\_state: the Brazilian state of the reviewer (e.g. RJ).",
"### Data Splits",
"### Contributions\n\n\nThanks to @ruanchaves for adding this dataset."
]
| [
"TAGS\n#task_categories-text-classification #task_ids-sentiment-analysis #task_ids-sentiment-scoring #task_ids-intent-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100M<n<1B #source_datasets-original #language-Portuguese #license-cc-by-4.0 #reviews #doi-10.57967/hf/0282 #region-us \n",
"### Dataset Summary\n\n\nB2W-Reviews01 is an open corpus of product reviews. It contains more than 130k e-commerce customer reviews, collected from the URL website between January and May, 2018. B2W-Reviews01 offers rich information about the reviewer profile, such as gender, age, and geographical location. The corpus also has two different review rates:\n\n\n* the usual 5-point scale rate, represented by stars in most e-commerce websites,\n* a \"recommend to a friend\" label, a \"yes or no\" question representing the willingness of the customer to recommend the product to someone else.",
"### Supported Tasks and Leaderboards\n\n\n* Sentiment Analysis\n* Topic Modeling",
"### Languages\n\n\n* Portuguese\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields\n\n\n* submission\\_date: the date and time when the review was submitted. '\"%Y-%m-%d %H:%M:%S\"'.\n* reviewer\\_id: a unique identifier for the reviewer.\n* product\\_id: a unique identifier for the product being reviewed.\n* product\\_name: the name of the product being reviewed.\n* product\\_brand: the brand of the product being reviewed.\n* site\\_category\\_lv1: the highest level category for the product on the site where the review is being submitted.\n* site\\_category\\_lv2: the second level category for the product on the site where the review is being submitted.\n* review\\_title: the title of the review.\n* overall\\_rating: the overall star rating given by the reviewer on a scale of 1 to 5.\n* recommend\\_to\\_a\\_friend: whether or not the reviewer would recommend the product to a friend (Yes/No).\n* review\\_text: the full text of the review.\n* reviewer\\_birth\\_year: the birth year of the reviewer.\n* reviewer\\_gender: the gender of the reviewer (F/M).\n* reviewer\\_state: the Brazilian state of the reviewer (e.g. RJ).",
"### Data Splits",
"### Contributions\n\n\nThanks to @ruanchaves for adding this dataset."
]
| [
136,
140,
19,
15,
6,
303,
5,
17
]
| [
"passage: TAGS\n#task_categories-text-classification #task_ids-sentiment-analysis #task_ids-sentiment-scoring #task_ids-intent-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100M<n<1B #source_datasets-original #language-Portuguese #license-cc-by-4.0 #reviews #doi-10.57967/hf/0282 #region-us \n### Dataset Summary\n\n\nB2W-Reviews01 is an open corpus of product reviews. It contains more than 130k e-commerce customer reviews, collected from the URL website between January and May, 2018. B2W-Reviews01 offers rich information about the reviewer profile, such as gender, age, and geographical location. The corpus also has two different review rates:\n\n\n* the usual 5-point scale rate, represented by stars in most e-commerce websites,\n* a \"recommend to a friend\" label, a \"yes or no\" question representing the willingness of the customer to recommend the product to someone else.### Supported Tasks and Leaderboards\n\n\n* Sentiment Analysis\n* Topic Modeling### Languages\n\n\n* Portuguese\n\n\nDataset Structure\n-----------------### Data Instances"
]
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|
cb482d3e7fb66d38f2684acad7cb96fc3fc88207 | # Dataset Card for "pexel"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pexel | [
"region:us"
]
| 2023-01-19T08:09:54+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 653318190.0, "num_examples": 1560}, {"name": "validation", "num_bytes": 7122908.0, "num_examples": 20}], "download_size": 653521442, "dataset_size": 660441098.0}} | 2023-01-19T08:11:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pexel"
More Information needed | [
"# Dataset Card for \"pexel\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pexel\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pexel\"\n\nMore Information needed"
]
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|
ea34fa84ef87766f2b34baf2909f80ce804671a8 | # Dataset Card for "KnowledgeNet"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [knowledge-net](https://github.com/diffbot/knowledge-net)
- **Paper:** [KnowledgeNet: A Benchmark Dataset for Knowledge Base Population](https://aclanthology.org/D19-1069/)
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 6.1 MB
### Dataset Summary
KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts
expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus
enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks
that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction).
For instance, the dataset contains text expressing the fact (Gennaro Basile; RESIDENCE; Moravia), in the passage:
"Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn,
in Moravia, and lived about 1756..."
For a description of the dataset and baseline systems, please refer to their
[EMNLP paper](https://github.com/diffbot/knowledge-net/blob/master/knowledgenet-emnlp-cameraready.pdf).
Note: This Datasetreader currently only supports the `train` split and does not contain negative examples.
In addition to the original format this repository also provides two version (`knet_re`, `knet_tokenized`) that are
easier to use for simple relation extraction. You can load them with
`datasets.load_dataset("DFKI-SLT/knowledge_net", name="<config>")`.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language in the dataset is English.
## Dataset Structure
### Data Instances
#### knet
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 10.16 MB
An example of 'train' looks as follows:
```json
{
"fold": 2,
"documentId": "8313",
"source": "DBpedia Abstract",
"documentText": "Gennaro Basile\n\nGennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn, in Moravia, and lived about 1756. His best picture is the altar-piece in the chapel of the chateau at Seeberg, in Salzburg. Most of his works remained in Moravia.",
"passages": [
{
"passageId": "8313:16:114",
"passageStart": 16,
"passageEnd": 114,
"passageText": "Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries.",
"exhaustivelyAnnotatedProperties": [
{
"propertyId": "12",
"propertyName": "PLACE_OF_BIRTH",
"propertyDescription": "Describes the relationship between a person and the location where she/he was born."
}
],
"facts": [
{
"factId": "8313:16:30:63:69:12",
"propertyId": "12",
"humanReadable": "<Gennaro Basile> <PLACE_OF_BIRTH> <Naples>",
"annotatedPassage": "<Gennaro Basile> was an Italian painter, born in <Naples> but active in the German-speaking countries.",
"subjectStart": 16,
"subjectEnd": 30,
"subjectText": "Gennaro Basile",
"subjectUri": "http://www.wikidata.org/entity/Q19517888",
"objectStart": 63,
"objectEnd": 69,
"objectText": "Naples",
"objectUri": "http://www.wikidata.org/entity/Q2634"
}
]
},
{
"passageId": "8313:115:169",
"passageStart": 115,
"passageEnd": 169,
"passageText": "He settled at Brünn, in Moravia, and lived about 1756.",
"exhaustivelyAnnotatedProperties": [
{
"propertyId": "11",
"propertyName": "PLACE_OF_RESIDENCE",
"propertyDescription": "Describes the relationship between a person and the location where she/he lives/lived."
},
{
"propertyId": "12",
"propertyName": "PLACE_OF_BIRTH",
"propertyDescription": "Describes the relationship between a person and the location where she/he was born."
}
],
"facts": [
{
"factId": "8313:115:117:129:134:11",
"propertyId": "11",
"humanReadable": "<He> <PLACE_OF_RESIDENCE> <Brünn>",
"annotatedPassage": "<He> settled at <Brünn>, in Moravia, and lived about 1756.",
"subjectStart": 115,
"subjectEnd": 117,
"subjectText": "He",
"subjectUri": "http://www.wikidata.org/entity/Q19517888",
"objectStart": 129,
"objectEnd": 134,
"objectText": "Brünn",
"objectUri": "http://www.wikidata.org/entity/Q14960"
},
{
"factId": "8313:115:117:139:146:11",
"propertyId": "11",
"humanReadable": "<He> <PLACE_OF_RESIDENCE> <Moravia>",
"annotatedPassage": "<He> settled at Brünn, in <Moravia>, and lived about 1756.",
"subjectStart": 115,
"subjectEnd": 117,
"subjectText": "He",
"subjectUri": "http://www.wikidata.org/entity/Q19517888",
"objectStart": 139,
"objectEnd": 146,
"objectText": "Moravia",
"objectUri": "http://www.wikidata.org/entity/Q43266"
}
]
}
]
}
```
#### knet_re
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 6.1 MB
An example of 'train' looks as follows:
```json
{
"documentId": "7",
"passageId": "7:23:206",
"factId": "7:23:44:138:160:1",
"passageText": "Tata Chemicals Europe (formerly Brunner Mond (UK) Limited) is a UK-based chemicals company that is a subsidiary of Tata Chemicals Limited, itself a part of the India-based Tata Group.",
"humanReadable": "<Tata Chemicals Europe> <SUBSIDIARY_OF> <Tata Chemicals Limited>",
"annotatedPassage": "<Tata Chemicals Europe> (formerly Brunner Mond (UK) Limited) is a UK-based chemicals company that is a subsidiary of <Tata Chemicals Limited>, itself a part of the India-based Tata Group.",
"subjectStart": 0,
"subjectEnd": 21,
"subjectText": "Tata Chemicals Europe",
"subjectType": 2,
"subjectUri": "",
"objectStart": 115,
"objectEnd": 137,
"objectText": "Tata Chemicals Limited",
"objectType": 2,
"objectUri": "http://www.wikidata.org/entity/Q2331365",
"relation": 13
}
```
#### knet_tokenized
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 4.5 MB
An example of 'train' looks as follows:
```json
{
"doc_id": "7",
"passage_id": "7:23:206",
"fact_id": "7:162:168:183:205:1",
"tokens": ["Tata", "Chemicals", "Europe", "(", "formerly", "Brunner", "Mond", "(", "UK", ")", "Limited", ")", "is", "a", "UK", "-", "based", "chemicals", "company", "that", "is", "a", "subsidiary", "of", "Tata", "Chemicals", "Limited", ",", "itself", "a", "part", "of", "the", "India", "-", "based", "Tata", "Group", "."],
"subj_start": 28,
"subj_end": 29,
"subj_type": 2,
"subj_uri": "http://www.wikidata.org/entity/Q2331365",
"obj_start": 33,
"obj_end": 38,
"obj_type": 2,
"obj_uri": "http://www.wikidata.org/entity/Q331715",
"relation": 13
}
```
### Data Fields
#### knet
- `fold`: the fold, a `int` feature.
- `documentId`: the document id, a `string` feature.
- `source`: the source, a `string` feature.
- `documenText`: the document text, a `string` feature.
- `passages`: the list of passages, a `list` of `dict`.
- `passageId`: the passage id, a `string` feature.
- `passageStart`: the passage start, a `int` feature.
- `passageEnd`: the passage end, a `int` feature.
- `passageText`: the passage text, a `string` feature.
- `exhaustivelyAnnotatedProperties`: the list of exhaustively annotated properties, a `list` of `dict`.
- `propertyId`: the property id, a `string` feature.
- `propertyName`: the property name, a `string` feature.
- `propertyDescription`: the property description, a `string` feature.
- `facts`: the list of facts, a `list` of `dict`.
- `factId`: the fact id, a `string` feature.
- `propertyId`: the property id, a `string` feature.
- `humanReadable`: the human readable annotation, a `string` feature.
- `annotatedPassage`: the annotated passage, a `string` feature.
- `subjectStart`: the subject start, a `int` feature.
- `subjectEnd`: the subject end, a `int` feature.
- `subjectText`: the subject text, a `string` feature.
- `subjectUri`: the subject uri, a `string` feature.
- `objectStart`: the object start, a `int` feature.
- `objectEnd`: the object end, a `int` feature.
- `objectText`: the object text, a `string` feature.
- `objectUri`: the object uri, a `string` feature.
#### knet_re
- `documentId`: the document id, a `string` feature.
- `passageId`: the passage id, a `string` feature.
- `passageText`: the passage text, a `string` feature.
- `factId`: the fact id, a `string` feature.
- `humanReadable`: human-readable annotation, a `string` features.
- `annotatedPassage`: annotated passage, a `string` feature.
- `subjectStart`: the index of the start character of the relation subject mention, an `ìnt` feature.
- `subjectEnd`: the index of the end character of the relation subject mention, exclusive, an `ìnt` feature.
- `subjectText`: the text the subject mention, a `string` feature.
- `subjectType`: the NER type of the subject mention, a `string` classification label.
```json
{"O": 0, "PER": 1, "ORG": 2, "LOC": 3, "DATE": 4}
```
- `subjectUri`: the Wikidata URI of the subject mention, a `string` feature.
- `objectStart`: the index of the start character of the relation object mention, an `ìnt` feature.
- `objectEnd`: the index of the end character of the relation object mention, exclusive, an `ìnt` feature.
- `objectText`: the text the object mention, a `string` feature.
- `objectType`: the NER type of the object mention, a `string` classification label.
```json
{"O": 0, "PER": 1, "ORG": 2, "LOC": 3, "DATE": 4}
```
- `objectUri`: the Wikidata URI of the object mention, a `string` feature.
- `relation`: the relation label of this instance, a `string` classification label.
```json
{"NO_RELATION": 0, "DATE_OF_BIRTH": 1, "DATE_OF_DEATH": 2, "PLACE_OF_RESIDENCE": 3, "PLACE_OF_BIRTH": 4, "NATIONALITY": 5, "EMPLOYEE_OR_MEMBER_OF": 6, "EDUCATED_AT": 7, "POLITICAL_AFFILIATION": 8, "CHILD_OF": 9, "SPOUSE": 10, "DATE_FOUNDED": 11, "HEADQUARTERS": 12, "SUBSIDIARY_OF": 13, "FOUNDED_BY": 14, "CEO": 15}
```
#### knet_tokenized
- `doc_id`: the document id, a `string` feature.
- `passage_id`: the passage id, a `string` feature.
- `factId`: the fact id, a `string` feature.
- `tokens`: the list of tokens of this passage, obtained with spaCy, a `list` of `string` features.
- `subj_start`: the index of the start token of the relation subject mention, an `ìnt` feature.
- `subj_end`: the index of the end token of the relation subject mention, exclusive, an `ìnt` feature.
- `subj_type`: the NER type of the subject mention, a `string` classification label.
```json
{"O": 0, "PER": 1, "ORG": 2, "LOC": 3, "DATE": 4}
```
- `subj_uri`: the Wikidata URI of the subject mention, a `string` feature.
- `obj_start`: the index of the start token of the relation object mention, an `ìnt` feature.
- `obj_end`: the index of the end token of the relation object mention, exclusive, an `ìnt` feature.
- `obj_type`: the NER type of the object mention, a `string` classification label.
```json
{"O": 0, "PER": 1, "ORG": 2, "LOC": 3, "DATE": 4}
```
- `obj_uri`: the Wikidata URI of the object mention, a `string` feature.
- `relation`: the relation label of this instance, a `string` classification label.
```json
{"NO_RELATION": 0, "DATE_OF_BIRTH": 1, "DATE_OF_DEATH": 2, "PLACE_OF_RESIDENCE": 3, "PLACE_OF_BIRTH": 4, "NATIONALITY": 5, "EMPLOYEE_OR_MEMBER_OF": 6, "EDUCATED_AT": 7, "POLITICAL_AFFILIATION": 8, "CHILD_OF": 9, "SPOUSE": 10, "DATE_FOUNDED": 11, "HEADQUARTERS": 12, "SUBSIDIARY_OF": 13, "FOUNDED_BY": 14, "CEO": 15}
```
### Data Splits
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## 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)
are labeled as no_relation.
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{mesquita-etal-2019-knowledgenet,
title = "{K}nowledge{N}et: A Benchmark Dataset for Knowledge Base Population",
author = "Mesquita, Filipe and
Cannaviccio, Matteo and
Schmidek, Jordan and
Mirza, Paramita and
Barbosa, Denilson",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1069",
doi = "10.18653/v1/D19-1069",
pages = "749--758",}
```
### Contributions
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset. | DFKI-SLT/knowledge_net | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:entity-linking-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"knowledgenet",
"region:us"
]
| 2023-01-19T09:15:44+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification", "entity-linking-classification"], "pretty_name": "KnowledgeNet is a dataset for automatically populating a knowledge base", "tags": ["knowledgenet"], "dataset_info": [{"config_name": "knet", "features": [{"name": "fold", "dtype": "int32"}, {"name": "documentId", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "documentText", "dtype": "string"}, {"name": "passages", "sequence": [{"name": "passageId", "dtype": "string"}, {"name": "passageStart", "dtype": "int32"}, {"name": "passageEnd", "dtype": "int32"}, {"name": "passageText", "dtype": "string"}, {"name": "exhaustivelyAnnotatedProperties", "sequence": [{"name": "propertyId", "dtype": "string"}, {"name": "propertyName", "dtype": "string"}, {"name": "propertyDescription", "dtype": "string"}]}, {"name": "facts", "sequence": [{"name": "factId", "dtype": "string"}, {"name": "propertyId", "dtype": "string"}, {"name": "humanReadable", "dtype": "string"}, {"name": "annotatedPassage", "dtype": "string"}, {"name": "subjectStart", "dtype": "int32"}, {"name": "subjectEnd", "dtype": "int32"}, {"name": "subjectText", "dtype": "string"}, {"name": "subjectUri", "dtype": "string"}, {"name": "objectStart", "dtype": "int32"}, {"name": "objectEnd", "dtype": "int32"}, {"name": "objectText", "dtype": "string"}, {"name": "objectUri", "dtype": "string"}]}]}], "splits": [{"name": "train", "num_bytes": 10161415, "num_examples": 3977}], "download_size": 14119313, "dataset_size": 10161415}, {"config_name": "knet_tokenized", "features": [{"name": "doc_id", "dtype": "string"}, {"name": "passage_id", "dtype": "string"}, {"name": "fact_id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "subj_start", "dtype": "int32"}, {"name": "subj_end", "dtype": "int32"}, {"name": "subj_type", "dtype": {"class_label": {"names": {"0": "O", "1": "PER", "2": "ORG", "3": "LOC", "4": "DATE"}}}}, {"name": "subj_uri", "dtype": "string"}, {"name": "obj_start", "dtype": "int32"}, {"name": "obj_end", "dtype": "int32"}, {"name": "obj_type", "dtype": {"class_label": {"names": {"0": "O", "1": "PER", "2": "ORG", "3": "LOC", "4": "DATE"}}}}, {"name": "obj_uri", "dtype": "string"}, {"name": "relation", "dtype": {"class_label": {"names": {"0": "NO_RELATION", "1": "DATE_OF_BIRTH", "2": "DATE_OF_DEATH", "3": "PLACE_OF_RESIDENCE", "4": "PLACE_OF_BIRTH", "5": "NATIONALITY", "6": "EMPLOYEE_OR_MEMBER_OF", "7": "EDUCATED_AT", "8": "POLITICAL_AFFILIATION", "9": "CHILD_OF", "10": "SPOUSE", "11": "DATE_FOUNDED", "12": "HEADQUARTERS", "13": "SUBSIDIARY_OF", "14": "FOUNDED_BY", "15": "CEO"}}}}], "splits": [{"name": "train", "num_bytes": 4511963, "num_examples": 10895}], "download_size": 14119313, "dataset_size": 4511963}, {"config_name": "knet_re", "features": [{"name": "documentId", "dtype": "string"}, {"name": "passageId", "dtype": "string"}, {"name": "factId", "dtype": "string"}, {"name": "passageText", "dtype": "string"}, {"name": "humanReadable", "dtype": "string"}, {"name": "annotatedPassage", "dtype": "string"}, {"name": "subjectStart", "dtype": "int32"}, {"name": "subjectEnd", "dtype": "int32"}, {"name": "subjectText", "dtype": "string"}, {"name": "subjectType", "dtype": {"class_label": {"names": {"0": "O", "1": "PER", "2": "ORG", "3": "LOC", "4": "DATE"}}}}, {"name": "subjectUri", "dtype": "string"}, {"name": "objectStart", "dtype": "int32"}, {"name": "objectEnd", "dtype": "int32"}, {"name": "objectText", "dtype": "string"}, {"name": "objectType", "dtype": {"class_label": {"names": {"0": "O", "1": "PER", "2": "ORG", "3": "LOC", "4": "DATE"}}}}, {"name": "objectUri", "dtype": "string"}, {"name": "relation", "dtype": {"class_label": {"names": {"0": "NO_RELATION", "1": "DATE_OF_BIRTH", "2": "DATE_OF_DEATH", "3": "PLACE_OF_RESIDENCE", "4": "PLACE_OF_BIRTH", "5": "NATIONALITY", "6": "EMPLOYEE_OR_MEMBER_OF", "7": "EDUCATED_AT", "8": "POLITICAL_AFFILIATION", "9": "CHILD_OF", "10": "SPOUSE", "11": "DATE_FOUNDED", "12": "HEADQUARTERS", "13": "SUBSIDIARY_OF", "14": "FOUNDED_BY", "15": "CEO"}}}}], "splits": [{"name": "train", "num_bytes": 6098219, "num_examples": 10895}], "download_size": 14119313, "dataset_size": 6098219}]} | 2023-01-19T09:16:32+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_ids-multi-class-classification #task_ids-entity-linking-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-English #knowledgenet #region-us
| # Dataset Card for "KnowledgeNet"
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Repository: knowledge-net
- Paper: KnowledgeNet: A Benchmark Dataset for Knowledge Base Population
- Size of downloaded dataset files: 12.59 MB
- Size of the generated dataset: 6.1 MB
### Dataset Summary
KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts
expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus
enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks
that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction).
For instance, the dataset contains text expressing the fact (Gennaro Basile; RESIDENCE; Moravia), in the passage:
"Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn,
in Moravia, and lived about 1756..."
For a description of the dataset and baseline systems, please refer to their
EMNLP paper.
Note: This Datasetreader currently only supports the 'train' split and does not contain negative examples.
In addition to the original format this repository also provides two version ('knet_re', 'knet_tokenized') that are
easier to use for simple relation extraction. You can load them with
'datasets.load_dataset("DFKI-SLT/knowledge_net", name="<config>")'.
### Supported Tasks and Leaderboards
### Languages
The language in the dataset is English.
## Dataset Structure
### Data Instances
#### knet
- Size of downloaded dataset files: 12.59 MB
- Size of the generated dataset: 10.16 MB
An example of 'train' looks as follows:
#### knet_re
- Size of downloaded dataset files: 12.59 MB
- Size of the generated dataset: 6.1 MB
An example of 'train' looks as follows:
#### knet_tokenized
- Size of downloaded dataset files: 12.59 MB
- Size of the generated dataset: 4.5 MB
An example of 'train' looks as follows:
### Data Fields
#### knet
- 'fold': the fold, a 'int' feature.
- 'documentId': the document id, a 'string' feature.
- 'source': the source, a 'string' feature.
- 'documenText': the document text, a 'string' feature.
- 'passages': the list of passages, a 'list' of 'dict'.
- 'passageId': the passage id, a 'string' feature.
- 'passageStart': the passage start, a 'int' feature.
- 'passageEnd': the passage end, a 'int' feature.
- 'passageText': the passage text, a 'string' feature.
- 'exhaustivelyAnnotatedProperties': the list of exhaustively annotated properties, a 'list' of 'dict'.
- 'propertyId': the property id, a 'string' feature.
- 'propertyName': the property name, a 'string' feature.
- 'propertyDescription': the property description, a 'string' feature.
- 'facts': the list of facts, a 'list' of 'dict'.
- 'factId': the fact id, a 'string' feature.
- 'propertyId': the property id, a 'string' feature.
- 'humanReadable': the human readable annotation, a 'string' feature.
- 'annotatedPassage': the annotated passage, a 'string' feature.
- 'subjectStart': the subject start, a 'int' feature.
- 'subjectEnd': the subject end, a 'int' feature.
- 'subjectText': the subject text, a 'string' feature.
- 'subjectUri': the subject uri, a 'string' feature.
- 'objectStart': the object start, a 'int' feature.
- 'objectEnd': the object end, a 'int' feature.
- 'objectText': the object text, a 'string' feature.
- 'objectUri': the object uri, a 'string' feature.
#### knet_re
- 'documentId': the document id, a 'string' feature.
- 'passageId': the passage id, a 'string' feature.
- 'passageText': the passage text, a 'string' feature.
- 'factId': the fact id, a 'string' feature.
- 'humanReadable': human-readable annotation, a 'string' features.
- 'annotatedPassage': annotated passage, a 'string' feature.
- 'subjectStart': the index of the start character of the relation subject mention, an 'ìnt' feature.
- 'subjectEnd': the index of the end character of the relation subject mention, exclusive, an 'ìnt' feature.
- 'subjectText': the text the subject mention, a 'string' feature.
- 'subjectType': the NER type of the subject mention, a 'string' classification label.
- 'subjectUri': the Wikidata URI of the subject mention, a 'string' feature.
- 'objectStart': the index of the start character of the relation object mention, an 'ìnt' feature.
- 'objectEnd': the index of the end character of the relation object mention, exclusive, an 'ìnt' feature.
- 'objectText': the text the object mention, a 'string' feature.
- 'objectType': the NER type of the object mention, a 'string' classification label.
- 'objectUri': the Wikidata URI of the object mention, a 'string' feature.
- 'relation': the relation label of this instance, a 'string' classification label.
#### knet_tokenized
- 'doc_id': the document id, a 'string' feature.
- 'passage_id': the passage id, a 'string' feature.
- 'factId': the fact id, a 'string' feature.
- 'tokens': the list of tokens of this passage, obtained with spaCy, a 'list' of 'string' features.
- 'subj_start': the index of the start token of the relation subject mention, an 'ìnt' feature.
- 'subj_end': the index of the end token of the relation subject mention, exclusive, an 'ìnt' feature.
- 'subj_type': the NER type of the subject mention, a 'string' classification label.
- 'subj_uri': the Wikidata URI of the subject mention, a 'string' feature.
- 'obj_start': the index of the start token of the relation object mention, an 'ìnt' feature.
- 'obj_end': the index of the end token of the relation object mention, exclusive, an 'ìnt' feature.
- 'obj_type': the NER type of the object mention, a 'string' classification label.
- 'obj_uri': the Wikidata URI of the object mention, a 'string' feature.
- 'relation': the relation label of this instance, a 'string' classification label.
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
are labeled as no_relation.
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @phucdev for adding this dataset. | [
"# Dataset Card for \"KnowledgeNet\"",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n- Repository: knowledge-net\n- Paper: KnowledgeNet: A Benchmark Dataset for Knowledge Base Population\n- Size of downloaded dataset files: 12.59 MB\n- Size of the generated dataset: 6.1 MB",
"### Dataset Summary\nKnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts \nexpressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus \nenabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks \nthat are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction).\n\nFor instance, the dataset contains text expressing the fact (Gennaro Basile; RESIDENCE; Moravia), in the passage: \n\"Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn, \nin Moravia, and lived about 1756...\"\n\nFor a description of the dataset and baseline systems, please refer to their \nEMNLP paper.\n\nNote: This Datasetreader currently only supports the 'train' split and does not contain negative examples.\nIn addition to the original format this repository also provides two version ('knet_re', 'knet_tokenized') that are \neasier to use for simple relation extraction. You can load them with \n'datasets.load_dataset(\"DFKI-SLT/knowledge_net\", name=\"<config>\")'.",
"### Supported Tasks and Leaderboards",
"### Languages\nThe language in the dataset is English.",
"## Dataset Structure",
"### Data Instances",
"#### knet\n- Size of downloaded dataset files: 12.59 MB\n- Size of the generated dataset: 10.16 MB\n\nAn example of 'train' looks as follows:",
"#### knet_re\n- Size of downloaded dataset files: 12.59 MB\n- Size of the generated dataset: 6.1 MB\n\nAn example of 'train' looks as follows:",
"#### knet_tokenized\n- Size of downloaded dataset files: 12.59 MB\n- Size of the generated dataset: 4.5 MB\n\nAn example of 'train' looks as follows:",
"### Data Fields",
"#### knet\n- 'fold': the fold, a 'int' feature.\n- 'documentId': the document id, a 'string' feature.\n- 'source': the source, a 'string' feature.\n- 'documenText': the document text, a 'string' feature.\n- 'passages': the list of passages, a 'list' of 'dict'.\n - 'passageId': the passage id, a 'string' feature.\n - 'passageStart': the passage start, a 'int' feature.\n - 'passageEnd': the passage end, a 'int' feature.\n - 'passageText': the passage text, a 'string' feature.\n - 'exhaustivelyAnnotatedProperties': the list of exhaustively annotated properties, a 'list' of 'dict'.\n - 'propertyId': the property id, a 'string' feature.\n - 'propertyName': the property name, a 'string' feature.\n - 'propertyDescription': the property description, a 'string' feature.\n - 'facts': the list of facts, a 'list' of 'dict'.\n - 'factId': the fact id, a 'string' feature.\n - 'propertyId': the property id, a 'string' feature.\n - 'humanReadable': the human readable annotation, a 'string' feature.\n - 'annotatedPassage': the annotated passage, a 'string' feature.\n - 'subjectStart': the subject start, a 'int' feature.\n - 'subjectEnd': the subject end, a 'int' feature.\n - 'subjectText': the subject text, a 'string' feature.\n - 'subjectUri': the subject uri, a 'string' feature.\n - 'objectStart': the object start, a 'int' feature.\n - 'objectEnd': the object end, a 'int' feature.\n - 'objectText': the object text, a 'string' feature.\n - 'objectUri': the object uri, a 'string' feature.",
"#### knet_re\n- 'documentId': the document id, a 'string' feature.\n- 'passageId': the passage id, a 'string' feature.\n- 'passageText': the passage text, a 'string' feature.\n- 'factId': the fact id, a 'string' feature.\n- 'humanReadable': human-readable annotation, a 'string' features.\n- 'annotatedPassage': annotated passage, a 'string' feature.\n- 'subjectStart': the index of the start character of the relation subject mention, an 'ìnt' feature.\n- 'subjectEnd': the index of the end character of the relation subject mention, exclusive, an 'ìnt' feature.\n- 'subjectText': the text the subject mention, a 'string' feature.\n- 'subjectType': the NER type of the subject mention, a 'string' classification label.\n\n\n\n- 'subjectUri': the Wikidata URI of the subject mention, a 'string' feature.\n- 'objectStart': the index of the start character of the relation object mention, an 'ìnt' feature.\n- 'objectEnd': the index of the end character of the relation object mention, exclusive, an 'ìnt' feature.\n- 'objectText': the text the object mention, a 'string' feature.\n- 'objectType': the NER type of the object mention, a 'string' classification label.\n\n\n\n- 'objectUri': the Wikidata URI of the object mention, a 'string' feature.\n- 'relation': the relation label of this instance, a 'string' classification label.",
"#### knet_tokenized\n- 'doc_id': the document id, a 'string' feature.\n- 'passage_id': the passage id, a 'string' feature.\n- 'factId': the fact id, a 'string' feature.\n- 'tokens': the list of tokens of this passage, obtained with spaCy, a 'list' of 'string' features.\n- 'subj_start': the index of the start token of the relation subject mention, an 'ìnt' feature.\n- 'subj_end': the index of the end token of the relation subject mention, exclusive, an 'ìnt' feature.\n- 'subj_type': the NER type of the subject mention, a 'string' classification label.\n\n\n\n\n- 'subj_uri': the Wikidata URI of the subject mention, a 'string' feature.\n- 'obj_start': the index of the start token of the relation object mention, an 'ìnt' feature.\n- 'obj_end': the index of the end token of the relation object mention, exclusive, an 'ìnt' feature.\n- 'obj_type': the NER type of the object mention, a 'string' classification label.\n\n\n\n- 'obj_uri': the Wikidata URI of the object mention, a 'string' feature.\n- 'relation': the relation label of this instance, a 'string' classification label.",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process\n\nare labeled as no_relation.",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\nThanks to @phucdev for adding this dataset."
]
| [
"TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-entity-linking-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-English #knowledgenet #region-us \n",
"# Dataset Card for \"KnowledgeNet\"",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n- Repository: knowledge-net\n- Paper: KnowledgeNet: A Benchmark Dataset for Knowledge Base Population\n- Size of downloaded dataset files: 12.59 MB\n- Size of the generated dataset: 6.1 MB",
"### Dataset Summary\nKnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts \nexpressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus \nenabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks \nthat are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction).\n\nFor instance, the dataset contains text expressing the fact (Gennaro Basile; RESIDENCE; Moravia), in the passage: \n\"Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn, \nin Moravia, and lived about 1756...\"\n\nFor a description of the dataset and baseline systems, please refer to their \nEMNLP paper.\n\nNote: This Datasetreader currently only supports the 'train' split and does not contain negative examples.\nIn addition to the original format this repository also provides two version ('knet_re', 'knet_tokenized') that are \neasier to use for simple relation extraction. You can load them with \n'datasets.load_dataset(\"DFKI-SLT/knowledge_net\", name=\"<config>\")'.",
"### Supported Tasks and Leaderboards",
"### Languages\nThe language in the dataset is English.",
"## Dataset Structure",
"### Data Instances",
"#### knet\n- Size of downloaded dataset files: 12.59 MB\n- Size of the generated dataset: 10.16 MB\n\nAn example of 'train' looks as follows:",
"#### knet_re\n- Size of downloaded dataset files: 12.59 MB\n- Size of the generated dataset: 6.1 MB\n\nAn example of 'train' looks as follows:",
"#### knet_tokenized\n- Size of downloaded dataset files: 12.59 MB\n- Size of the generated dataset: 4.5 MB\n\nAn example of 'train' looks as follows:",
"### Data Fields",
"#### knet\n- 'fold': the fold, a 'int' feature.\n- 'documentId': the document id, a 'string' feature.\n- 'source': the source, a 'string' feature.\n- 'documenText': the document text, a 'string' feature.\n- 'passages': the list of passages, a 'list' of 'dict'.\n - 'passageId': the passage id, a 'string' feature.\n - 'passageStart': the passage start, a 'int' feature.\n - 'passageEnd': the passage end, a 'int' feature.\n - 'passageText': the passage text, a 'string' feature.\n - 'exhaustivelyAnnotatedProperties': the list of exhaustively annotated properties, a 'list' of 'dict'.\n - 'propertyId': the property id, a 'string' feature.\n - 'propertyName': the property name, a 'string' feature.\n - 'propertyDescription': the property description, a 'string' feature.\n - 'facts': the list of facts, a 'list' of 'dict'.\n - 'factId': the fact id, a 'string' feature.\n - 'propertyId': the property id, a 'string' feature.\n - 'humanReadable': the human readable annotation, a 'string' feature.\n - 'annotatedPassage': the annotated passage, a 'string' feature.\n - 'subjectStart': the subject start, a 'int' feature.\n - 'subjectEnd': the subject end, a 'int' feature.\n - 'subjectText': the subject text, a 'string' feature.\n - 'subjectUri': the subject uri, a 'string' feature.\n - 'objectStart': the object start, a 'int' feature.\n - 'objectEnd': the object end, a 'int' feature.\n - 'objectText': the object text, a 'string' feature.\n - 'objectUri': the object uri, a 'string' feature.",
"#### knet_re\n- 'documentId': the document id, a 'string' feature.\n- 'passageId': the passage id, a 'string' feature.\n- 'passageText': the passage text, a 'string' feature.\n- 'factId': the fact id, a 'string' feature.\n- 'humanReadable': human-readable annotation, a 'string' features.\n- 'annotatedPassage': annotated passage, a 'string' feature.\n- 'subjectStart': the index of the start character of the relation subject mention, an 'ìnt' feature.\n- 'subjectEnd': the index of the end character of the relation subject mention, exclusive, an 'ìnt' feature.\n- 'subjectText': the text the subject mention, a 'string' feature.\n- 'subjectType': the NER type of the subject mention, a 'string' classification label.\n\n\n\n- 'subjectUri': the Wikidata URI of the subject mention, a 'string' feature.\n- 'objectStart': the index of the start character of the relation object mention, an 'ìnt' feature.\n- 'objectEnd': the index of the end character of the relation object mention, exclusive, an 'ìnt' feature.\n- 'objectText': the text the object mention, a 'string' feature.\n- 'objectType': the NER type of the object mention, a 'string' classification label.\n\n\n\n- 'objectUri': the Wikidata URI of the object mention, a 'string' feature.\n- 'relation': the relation label of this instance, a 'string' classification label.",
"#### knet_tokenized\n- 'doc_id': the document id, a 'string' feature.\n- 'passage_id': the passage id, a 'string' feature.\n- 'factId': the fact id, a 'string' feature.\n- 'tokens': the list of tokens of this passage, obtained with spaCy, a 'list' of 'string' features.\n- 'subj_start': the index of the start token of the relation subject mention, an 'ìnt' feature.\n- 'subj_end': the index of the end token of the relation subject mention, exclusive, an 'ìnt' feature.\n- 'subj_type': the NER type of the subject mention, a 'string' classification label.\n\n\n\n\n- 'subj_uri': the Wikidata URI of the subject mention, a 'string' feature.\n- 'obj_start': the index of the start token of the relation object mention, an 'ìnt' feature.\n- 'obj_end': the index of the end token of the relation object mention, exclusive, an 'ìnt' feature.\n- 'obj_type': the NER type of the object mention, a 'string' classification label.\n\n\n\n- 'obj_uri': the Wikidata URI of the object mention, a 'string' feature.\n- 'relation': the relation label of this instance, a 'string' classification label.",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process\n\nare labeled as no_relation.",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\nThanks to @phucdev for adding this dataset."
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"passage: TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-entity-linking-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-English #knowledgenet #region-us \n# Dataset Card for \"KnowledgeNet\"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n- Repository: knowledge-net\n- Paper: KnowledgeNet: A Benchmark Dataset for Knowledge Base Population\n- Size of downloaded dataset files: 12.59 MB\n- Size of the generated dataset: 6.1 MB",
"passage: ### Dataset Summary\nKnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts \nexpressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus \nenabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks \nthat are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction).\n\nFor instance, the dataset contains text expressing the fact (Gennaro Basile; RESIDENCE; Moravia), in the passage: \n\"Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn, \nin Moravia, and lived about 1756...\"\n\nFor a description of the dataset and baseline systems, please refer to their \nEMNLP paper.\n\nNote: This Datasetreader currently only supports the 'train' split and does not contain negative examples.\nIn addition to the original format this repository also provides two version ('knet_re', 'knet_tokenized') that are \neasier to use for simple relation extraction. You can load them with \n'datasets.load_dataset(\"DFKI-SLT/knowledge_net\", name=\"<config>\")'.### Supported Tasks and Leaderboards### Languages\nThe language in the dataset is English.## Dataset Structure### Data Instances#### knet\n- Size of downloaded dataset files: 12.59 MB\n- Size of the generated dataset: 10.16 MB\n\nAn example of 'train' looks as follows:#### knet_re\n- Size of downloaded dataset files: 12.59 MB\n- Size of the generated dataset: 6.1 MB\n\nAn example of 'train' looks as follows:#### knet_tokenized\n- Size of downloaded dataset files: 12.59 MB\n- Size of the generated dataset: 4.5 MB\n\nAn example of 'train' looks as follows:### Data Fields",
"passage: #### knet\n- 'fold': the fold, a 'int' feature.\n- 'documentId': the document id, a 'string' feature.\n- 'source': the source, a 'string' feature.\n- 'documenText': the document text, a 'string' feature.\n- 'passages': the list of passages, a 'list' of 'dict'.\n - 'passageId': the passage id, a 'string' feature.\n - 'passageStart': the passage start, a 'int' feature.\n - 'passageEnd': the passage end, a 'int' feature.\n - 'passageText': the passage text, a 'string' feature.\n - 'exhaustivelyAnnotatedProperties': the list of exhaustively annotated properties, a 'list' of 'dict'.\n - 'propertyId': the property id, a 'string' feature.\n - 'propertyName': the property name, a 'string' feature.\n - 'propertyDescription': the property description, a 'string' feature.\n - 'facts': the list of facts, a 'list' of 'dict'.\n - 'factId': the fact id, a 'string' feature.\n - 'propertyId': the property id, a 'string' feature.\n - 'humanReadable': the human readable annotation, a 'string' feature.\n - 'annotatedPassage': the annotated passage, a 'string' feature.\n - 'subjectStart': the subject start, a 'int' feature.\n - 'subjectEnd': the subject end, a 'int' feature.\n - 'subjectText': the subject text, a 'string' feature.\n - 'subjectUri': the subject uri, a 'string' feature.\n - 'objectStart': the object start, a 'int' feature.\n - 'objectEnd': the object end, a 'int' feature.\n - 'objectText': the object text, a 'string' feature.\n - 'objectUri': the object uri, a 'string' feature.#### knet_re\n- 'documentId': the document id, a 'string' feature.\n- 'passageId': the passage id, a 'string' feature.\n- 'passageText': the passage text, a 'string' feature.\n- 'factId': the fact id, a 'string' feature.\n- 'humanReadable': human-readable annotation, a 'string' features.\n- 'annotatedPassage': annotated passage, a 'string' feature.\n- 'subjectStart': the index of the start character of the relation subject mention, an 'ìnt' feature.\n- 'subjectEnd': the index of the end character of the relation subject mention, exclusive, an 'ìnt' feature.\n- 'subjectText': the text the subject mention, a 'string' feature.\n- 'subjectType': the NER type of the subject mention, a 'string' classification label.\n\n\n\n- 'subjectUri': the Wikidata URI of the subject mention, a 'string' feature.\n- 'objectStart': the index of the start character of the relation object mention, an 'ìnt' feature.\n- 'objectEnd': the index of the end character of the relation object mention, exclusive, an 'ìnt' feature.\n- 'objectText': the text the object mention, a 'string' feature.\n- 'objectType': the NER type of the object mention, a 'string' classification label.\n\n\n\n- 'objectUri': the Wikidata URI of the object mention, a 'string' feature.\n- 'relation': the relation label of this instance, a 'string' classification label."
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|
f3c058abf7fc79723797d978e70bd3e1ffc79966 | # Dataset Card for CrossRE
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [CrossNER](https://github.com/zliucr/CrossNER)
- **Paper:** [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://arxiv.org/abs/2012.04373)
### Dataset Summary
CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains
(Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for
different domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five
domains.
For details, see the paper:
[CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://arxiv.org/abs/2012.04373)
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language data in CrossNER is in English (BCP-47 en)
## Dataset Structure
### Data Instances
#### conll2003
- **Size of downloaded dataset files:** 2.69 MB
- **Size of the generated dataset:** 5.26 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."],
"ner_tags": [49, 0, 41, 0, 0, 0, 41, 0, 0]
}
```
#### politics
- **Size of downloaded dataset files:** 0.72 MB
- **Size of the generated dataset:** 1.04 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["Parties", "with", "mainly", "Eurosceptic", "views", "are", "the", "ruling", "United", "Russia", ",", "and", "opposition", "parties", "the", "Communist", "Party", "of", "the", "Russian", "Federation", "and", "Liberal", "Democratic", "Party", "of", "Russia", "."],
"ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 55, 56, 0, 0, 0, 0, 0, 55, 56, 56, 56, 56, 56, 0, 55, 56, 56, 56, 56, 0]
}
```
#### science
- **Size of downloaded dataset files:** 0.49 MB
- **Size of the generated dataset:** 0.73 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."],
"ner_tags": [0, 0, 0, 0, 15, 16, 0, 15, 16, 0, 0, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
```
#### music
- **Size of downloaded dataset files:** 0.41 MB
- **Size of the generated dataset:** 0.65 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."],
"ner_tags": [0, 0, 0, 0, 35, 36, 36, 0, 0, 0, 0, 0, 0, 29, 30, 30, 30, 30, 0]
}
```
#### literature
- **Size of downloaded dataset files:** 0.33 MB
- **Size of the generated dataset:** 0.58 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "."],
"ner_tags": [0, 0, 0, 0, 0, 0, 0, 51, 52, 52, 0, 0, 21, 22, 0, 0, 0, 77, 78, 0, 77, 0, 0, 0, 0, 0, 77, 78, 0, 77, 0, 0, 0, 21, 0, 21, 0, 0, 41, 0, 0, 0, 0, 0, 0, 51, 52, 0, 0, 41, 0, 0, 0, 0, 0, 51, 0, 0]
}
```
#### ai
- **Size of downloaded dataset files:** 0.29 MB
- **Size of the generated dataset:** 0.48 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"tokens": ["Popular", "approaches", "of", "opinion-based", "recommender", "system", "utilize", "various", "techniques", "including", "text", "mining", ",", "information", "retrieval", ",", "sentiment", "analysis", "(", "see", "also", "Multimodal", "sentiment", "analysis", ")", "and", "deep", "learning", "X.Y.", "Feng", ",", "H.", "Zhang", ",", "Y.J.", "Ren", ",", "P.H.", "Shang", ",", "Y.", "Zhu", ",", "Y.C.", "Liang", ",", "R.C.", "Guan", ",", "D.", "Xu", ",", "(", "2019", ")", ",", ",", "21", "(", "5", ")", ":", "e12957", "."],
"ner_tags": [0, 0, 0, 59, 60, 60, 0, 0, 0, 0, 31, 32, 0, 71, 72, 0, 71, 72, 0, 0, 0, 71, 72, 72, 0, 0, 31, 32, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: the instance id of this sentence, a `string` feature.
- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
- `ner_tags`: the list of entity tags, a `list` of classification labels.
```json
{"O": 0, "B-academicjournal": 1, "I-academicjournal": 2, "B-album": 3, "I-album": 4, "B-algorithm": 5, "I-algorithm": 6, "B-astronomicalobject": 7, "I-astronomicalobject": 8, "B-award": 9, "I-award": 10, "B-band": 11, "I-band": 12, "B-book": 13, "I-book": 14, "B-chemicalcompound": 15, "I-chemicalcompound": 16, "B-chemicalelement": 17, "I-chemicalelement": 18, "B-conference": 19, "I-conference": 20, "B-country": 21, "I-country": 22, "B-discipline": 23, "I-discipline": 24, "B-election": 25, "I-election": 26, "B-enzyme": 27, "I-enzyme": 28, "B-event": 29, "I-event": 30, "B-field": 31, "I-field": 32, "B-literarygenre": 33, "I-literarygenre": 34, "B-location": 35, "I-location": 36, "B-magazine": 37, "I-magazine": 38, "B-metrics": 39, "I-metrics": 40, "B-misc": 41, "I-misc": 42, "B-musicalartist": 43, "I-musicalartist": 44, "B-musicalinstrument": 45, "I-musicalinstrument": 46, "B-musicgenre": 47, "I-musicgenre": 48, "B-organisation": 49, "I-organisation": 50, "B-person": 51, "I-person": 52, "B-poem": 53, "I-poem": 54, "B-politicalparty": 55, "I-politicalparty": 56, "B-politician": 57, "I-politician": 58, "B-product": 59, "I-product": 60, "B-programlang": 61, "I-programlang": 62, "B-protein": 63, "I-protein": 64, "B-researcher": 65, "I-researcher": 66, "B-scientist": 67, "I-scientist": 68, "B-song": 69, "I-song": 70, "B-task": 71, "I-task": 72, "B-theory": 73, "I-theory": 74, "B-university": 75, "I-university": 76, "B-writer": 77, "I-writer": 78}
```
### Data Splits
| | Train | Dev | Test |
|--------------|--------|-------|-------|
| conll2003 | 14,987 | 3,466 | 3,684 |
| politics | 200 | 541 | 651 |
| science | 200 | 450 | 543 |
| music | 100 | 380 | 456 |
| literature | 100 | 400 | 416 |
| ai | 100 | 350 | 431 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{liu2020crossner,
title={CrossNER: Evaluating Cross-Domain Named Entity Recognition},
author={Zihan Liu and Yan Xu and Tiezheng Yu and Wenliang Dai and Ziwei Ji and Samuel Cahyawijaya and Andrea Madotto and Pascale Fung},
year={2020},
eprint={2012.04373},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset. | DFKI-SLT/cross_ner | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|conll2003",
"language:en",
"cross domain",
"ai",
"news",
"music",
"literature",
"politics",
"science",
"arxiv:2012.04373",
"region:us"
]
| 2023-01-19T09:17:08+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|conll2003"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "CrossNER is a cross-domain dataset for named entity recognition", "tags": ["cross domain", "ai", "news", "music", "literature", "politics", "science"], "dataset_info": [{"config_name": "ai", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-academicjournal", "2": "I-academicjournal", "3": "B-album", "4": "I-album", "5": "B-algorithm", "6": "I-algorithm", "7": "B-astronomicalobject", "8": "I-astronomicalobject", "9": "B-award", "10": "I-award", "11": "B-band", "12": "I-band", "13": "B-book", "14": "I-book", "15": "B-chemicalcompound", "16": "I-chemicalcompound", "17": "B-chemicalelement", "18": "I-chemicalelement", "19": "B-conference", "20": "I-conference", "21": "B-country", "22": "I-country", "23": "B-discipline", "24": "I-discipline", "25": "B-election", "26": "I-election", "27": "B-enzyme", "28": "I-enzyme", "29": "B-event", "30": "I-event", "31": "B-field", "32": "I-field", "33": "B-literarygenre", "34": "I-literarygenre", "35": "B-location", "36": "I-location", "37": "B-magazine", "38": "I-magazine", "39": "B-metrics", "40": "I-metrics", "41": "B-misc", "42": "I-misc", "43": "B-musicalartist", "44": "I-musicalartist", "45": "B-musicalinstrument", "46": "I-musicalinstrument", "47": "B-musicgenre", "48": "I-musicgenre", "49": "B-organisation", "50": "I-organisation", "51": "B-person", "52": "I-person", "53": "B-poem", "54": "I-poem", "55": "B-politicalparty", "56": "I-politicalparty", "57": "B-politician", "58": "I-politician", "59": "B-product", "60": "I-product", "61": "B-programlang", "62": "I-programlang", "63": "B-protein", "64": "I-protein", "65": "B-researcher", "66": "I-researcher", "67": "B-scientist", "68": "I-scientist", "69": "B-song", "70": "I-song", "71": "B-task", "72": "I-task", "73": "B-theory", "74": "I-theory", "75": "B-university", "76": "I-university", "77": "B-writer", "78": "I-writer"}}}}], "splits": [{"name": "train", "num_bytes": 65080, "num_examples": 100}, {"name": "validation", "num_bytes": 189453, "num_examples": 350}, {"name": "test", "num_bytes": 225691, "num_examples": 431}], "download_size": 289173, "dataset_size": 480224}, {"config_name": "literature", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-academicjournal", "2": "I-academicjournal", "3": "B-album", "4": "I-album", "5": "B-algorithm", "6": "I-algorithm", "7": "B-astronomicalobject", "8": "I-astronomicalobject", "9": "B-award", "10": "I-award", "11": "B-band", "12": "I-band", "13": "B-book", "14": "I-book", "15": "B-chemicalcompound", "16": "I-chemicalcompound", "17": "B-chemicalelement", "18": "I-chemicalelement", "19": "B-conference", "20": "I-conference", "21": "B-country", "22": "I-country", "23": "B-discipline", "24": "I-discipline", "25": "B-election", "26": "I-election", "27": "B-enzyme", "28": "I-enzyme", "29": "B-event", "30": "I-event", "31": "B-field", "32": "I-field", "33": "B-literarygenre", "34": "I-literarygenre", "35": "B-location", "36": "I-location", "37": "B-magazine", "38": "I-magazine", "39": "B-metrics", "40": "I-metrics", "41": "B-misc", "42": "I-misc", "43": "B-musicalartist", "44": "I-musicalartist", "45": "B-musicalinstrument", "46": "I-musicalinstrument", "47": "B-musicgenre", "48": "I-musicgenre", "49": "B-organisation", "50": "I-organisation", "51": "B-person", "52": "I-person", "53": "B-poem", "54": "I-poem", "55": "B-politicalparty", "56": "I-politicalparty", "57": "B-politician", "58": "I-politician", "59": "B-product", "60": "I-product", "61": "B-programlang", "62": "I-programlang", "63": "B-protein", "64": "I-protein", "65": "B-researcher", "66": "I-researcher", "67": "B-scientist", "68": "I-scientist", "69": "B-song", "70": "I-song", "71": "B-task", "72": "I-task", "73": "B-theory", "74": "I-theory", "75": "B-university", "76": "I-university", "77": "B-writer", "78": "I-writer"}}}}], "splits": [{"name": "train", "num_bytes": 63181, "num_examples": 100}, {"name": "validation", "num_bytes": 244076, "num_examples": 400}, {"name": "test", "num_bytes": 270092, "num_examples": 416}], "download_size": 334380, "dataset_size": 577349}, {"config_name": "music", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-academicjournal", "2": "I-academicjournal", "3": "B-album", "4": "I-album", "5": "B-algorithm", "6": "I-algorithm", "7": "B-astronomicalobject", "8": "I-astronomicalobject", "9": "B-award", "10": "I-award", "11": "B-band", "12": "I-band", "13": "B-book", "14": "I-book", "15": "B-chemicalcompound", "16": "I-chemicalcompound", "17": "B-chemicalelement", "18": "I-chemicalelement", "19": "B-conference", "20": "I-conference", "21": "B-country", "22": "I-country", "23": "B-discipline", "24": "I-discipline", "25": "B-election", "26": "I-election", "27": "B-enzyme", "28": "I-enzyme", "29": "B-event", "30": "I-event", "31": "B-field", "32": "I-field", "33": "B-literarygenre", "34": "I-literarygenre", "35": "B-location", "36": "I-location", "37": "B-magazine", "38": "I-magazine", "39": "B-metrics", "40": "I-metrics", "41": "B-misc", "42": "I-misc", "43": "B-musicalartist", "44": "I-musicalartist", "45": "B-musicalinstrument", "46": "I-musicalinstrument", "47": "B-musicgenre", "48": "I-musicgenre", "49": "B-organisation", "50": "I-organisation", "51": "B-person", "52": "I-person", "53": "B-poem", "54": "I-poem", "55": "B-politicalparty", "56": "I-politicalparty", "57": "B-politician", "58": "I-politician", "59": "B-product", "60": "I-product", "61": "B-programlang", "62": "I-programlang", "63": "B-protein", "64": "I-protein", "65": "B-researcher", "66": "I-researcher", "67": "B-scientist", "68": "I-scientist", "69": "B-song", "70": "I-song", "71": "B-task", "72": "I-task", "73": "B-theory", "74": "I-theory", "75": "B-university", "76": "I-university", "77": "B-writer", "78": "I-writer"}}}}], "splits": [{"name": "train", "num_bytes": 65077, "num_examples": 100}, {"name": "validation", "num_bytes": 259702, "num_examples": 380}, {"name": "test", "num_bytes": 327195, "num_examples": 465}], "download_size": 414065, "dataset_size": 651974}, {"config_name": "conll2003", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-academicjournal", "2": "I-academicjournal", "3": "B-album", "4": "I-album", "5": "B-algorithm", "6": "I-algorithm", "7": "B-astronomicalobject", "8": "I-astronomicalobject", "9": "B-award", "10": "I-award", "11": "B-band", "12": "I-band", "13": "B-book", "14": "I-book", "15": "B-chemicalcompound", "16": "I-chemicalcompound", "17": "B-chemicalelement", "18": "I-chemicalelement", "19": "B-conference", "20": "I-conference", "21": "B-country", "22": "I-country", "23": "B-discipline", "24": "I-discipline", "25": "B-election", "26": "I-election", "27": "B-enzyme", "28": "I-enzyme", "29": "B-event", "30": "I-event", "31": "B-field", "32": "I-field", "33": "B-literarygenre", "34": "I-literarygenre", "35": "B-location", "36": "I-location", "37": "B-magazine", "38": "I-magazine", "39": "B-metrics", "40": "I-metrics", "41": "B-misc", "42": "I-misc", "43": "B-musicalartist", "44": "I-musicalartist", "45": "B-musicalinstrument", "46": "I-musicalinstrument", "47": "B-musicgenre", "48": "I-musicgenre", "49": "B-organisation", "50": "I-organisation", "51": "B-person", "52": "I-person", "53": "B-poem", "54": "I-poem", "55": "B-politicalparty", "56": "I-politicalparty", "57": "B-politician", "58": "I-politician", "59": "B-product", "60": "I-product", "61": "B-programlang", "62": "I-programlang", "63": "B-protein", "64": "I-protein", "65": "B-researcher", "66": "I-researcher", "67": "B-scientist", "68": "I-scientist", "69": "B-song", "70": "I-song", "71": "B-task", "72": "I-task", "73": "B-theory", "74": "I-theory", "75": "B-university", "76": "I-university", "77": "B-writer", "78": "I-writer"}}}}], "splits": [{"name": "train", "num_bytes": 3561081, "num_examples": 14041}, {"name": "validation", "num_bytes": 891431, "num_examples": 3250}, {"name": "test", "num_bytes": 811470, "num_examples": 3453}], "download_size": 2694794, "dataset_size": 5263982}, {"config_name": "politics", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-academicjournal", "2": "I-academicjournal", "3": "B-album", "4": "I-album", "5": "B-algorithm", "6": "I-algorithm", "7": "B-astronomicalobject", "8": "I-astronomicalobject", "9": "B-award", "10": "I-award", "11": "B-band", "12": "I-band", "13": "B-book", "14": "I-book", "15": "B-chemicalcompound", "16": "I-chemicalcompound", "17": "B-chemicalelement", "18": "I-chemicalelement", "19": "B-conference", "20": "I-conference", "21": "B-country", "22": "I-country", "23": "B-discipline", "24": "I-discipline", "25": "B-election", "26": "I-election", "27": "B-enzyme", "28": "I-enzyme", "29": "B-event", "30": "I-event", "31": "B-field", "32": "I-field", "33": "B-literarygenre", "34": "I-literarygenre", "35": "B-location", "36": "I-location", "37": "B-magazine", "38": "I-magazine", "39": "B-metrics", "40": "I-metrics", "41": "B-misc", "42": "I-misc", "43": "B-musicalartist", "44": "I-musicalartist", "45": "B-musicalinstrument", "46": "I-musicalinstrument", "47": "B-musicgenre", "48": "I-musicgenre", "49": "B-organisation", "50": "I-organisation", "51": "B-person", "52": "I-person", "53": "B-poem", "54": "I-poem", "55": "B-politicalparty", "56": "I-politicalparty", "57": "B-politician", "58": "I-politician", "59": "B-product", "60": "I-product", "61": "B-programlang", "62": "I-programlang", "63": "B-protein", "64": "I-protein", "65": "B-researcher", "66": "I-researcher", "67": "B-scientist", "68": "I-scientist", "69": "B-song", "70": "I-song", "71": "B-task", "72": "I-task", "73": "B-theory", "74": "I-theory", "75": "B-university", "76": "I-university", "77": "B-writer", "78": "I-writer"}}}}], "splits": [{"name": "train", "num_bytes": 143507, "num_examples": 200}, {"name": "validation", "num_bytes": 422760, "num_examples": 541}, {"name": "test", "num_bytes": 472690, "num_examples": 651}], "download_size": 724168, "dataset_size": 1038957}, {"config_name": "science", "features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-academicjournal", "2": "I-academicjournal", "3": "B-album", "4": "I-album", "5": "B-algorithm", "6": "I-algorithm", "7": "B-astronomicalobject", "8": "I-astronomicalobject", "9": "B-award", "10": "I-award", "11": "B-band", "12": "I-band", "13": "B-book", "14": "I-book", "15": "B-chemicalcompound", "16": "I-chemicalcompound", "17": "B-chemicalelement", "18": "I-chemicalelement", "19": "B-conference", "20": "I-conference", "21": "B-country", "22": "I-country", "23": "B-discipline", "24": "I-discipline", "25": "B-election", "26": "I-election", "27": "B-enzyme", "28": "I-enzyme", "29": "B-event", "30": "I-event", "31": "B-field", "32": "I-field", "33": "B-literarygenre", "34": "I-literarygenre", "35": "B-location", "36": "I-location", "37": "B-magazine", "38": "I-magazine", "39": "B-metrics", "40": "I-metrics", "41": "B-misc", "42": "I-misc", "43": "B-musicalartist", "44": "I-musicalartist", "45": "B-musicalinstrument", "46": "I-musicalinstrument", "47": "B-musicgenre", "48": "I-musicgenre", "49": "B-organisation", "50": "I-organisation", "51": "B-person", "52": "I-person", "53": "B-poem", "54": "I-poem", "55": "B-politicalparty", "56": "I-politicalparty", "57": "B-politician", "58": "I-politician", "59": "B-product", "60": "I-product", "61": "B-programlang", "62": "I-programlang", "63": "B-protein", "64": "I-protein", "65": "B-researcher", "66": "I-researcher", "67": "B-scientist", "68": "I-scientist", "69": "B-song", "70": "I-song", "71": "B-task", "72": "I-task", "73": "B-theory", "74": "I-theory", "75": "B-university", "76": "I-university", "77": "B-writer", "78": "I-writer"}}}}], "splits": [{"name": "train", "num_bytes": 121928, "num_examples": 200}, {"name": "validation", "num_bytes": 276118, "num_examples": 450}, {"name": "test", "num_bytes": 334181, "num_examples": 543}], "download_size": 485191, "dataset_size": 732227}]} | 2023-01-19T09:17:38+00:00 | [
"2012.04373"
]
| [
"en"
]
| TAGS
#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|conll2003 #language-English #cross domain #ai #news #music #literature #politics #science #arxiv-2012.04373 #region-us
| Dataset Card for CrossRE
========================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Repository: CrossNER
* Paper: CrossNER: Evaluating Cross-Domain Named Entity Recognition
### Dataset Summary
CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains
(Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for
different domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five
domains.
For details, see the paper:
CrossNER: Evaluating Cross-Domain Named Entity Recognition
### Supported Tasks and Leaderboards
### Languages
The language data in CrossNER is in English (BCP-47 en)
Dataset Structure
-----------------
### Data Instances
#### conll2003
* Size of downloaded dataset files: 2.69 MB
* Size of the generated dataset: 5.26 MB
An example of 'train' looks as follows:
#### politics
* Size of downloaded dataset files: 0.72 MB
* Size of the generated dataset: 1.04 MB
An example of 'train' looks as follows:
#### science
* Size of downloaded dataset files: 0.49 MB
* Size of the generated dataset: 0.73 MB
An example of 'train' looks as follows:
#### music
* Size of downloaded dataset files: 0.41 MB
* Size of the generated dataset: 0.65 MB
An example of 'train' looks as follows:
#### literature
* Size of downloaded dataset files: 0.33 MB
* Size of the generated dataset: 0.58 MB
An example of 'train' looks as follows:
#### ai
* Size of downloaded dataset files: 0.29 MB
* Size of the generated dataset: 0.48 MB
An example of 'train' looks as follows:
### Data Fields
The data fields are the same among all splits.
* 'id': the instance id of this sentence, a 'string' feature.
* 'tokens': the list of tokens of this sentence, a 'list' of 'string' features.
* 'ner\_tags': the list of entity tags, a 'list' of classification labels.
### Data Splits
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
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @phucdev for adding this dataset.
| [
"### Dataset Summary\n\n\nCrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains\n(Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for\ndifferent domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five\ndomains.\n\n\nFor details, see the paper:\nCrossNER: Evaluating Cross-Domain Named Entity Recognition",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nThe language data in CrossNER is in English (BCP-47 en)\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### conll2003\n\n\n* Size of downloaded dataset files: 2.69 MB\n* Size of the generated dataset: 5.26 MB\n\n\nAn example of 'train' looks as follows:",
"#### politics\n\n\n* Size of downloaded dataset files: 0.72 MB\n* Size of the generated dataset: 1.04 MB\n\n\nAn example of 'train' looks as follows:",
"#### science\n\n\n* Size of downloaded dataset files: 0.49 MB\n* Size of the generated dataset: 0.73 MB\n\n\nAn example of 'train' looks as follows:",
"#### music\n\n\n* Size of downloaded dataset files: 0.41 MB\n* Size of the generated dataset: 0.65 MB\n\n\nAn example of 'train' looks as follows:",
"#### literature\n\n\n* Size of downloaded dataset files: 0.33 MB\n* Size of the generated dataset: 0.58 MB\n\n\nAn example of 'train' looks as follows:",
"#### ai\n\n\n* Size of downloaded dataset files: 0.29 MB\n* Size of the generated dataset: 0.48 MB\n\n\nAn example of 'train' looks as follows:",
"### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'id': the instance id of this sentence, a 'string' feature.\n* 'tokens': the list of tokens of this sentence, a 'list' of 'string' features.\n* 'ner\\_tags': the list of entity tags, a 'list' of classification labels.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### 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\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @phucdev for adding this dataset."
]
| [
"TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|conll2003 #language-English #cross domain #ai #news #music #literature #politics #science #arxiv-2012.04373 #region-us \n",
"### Dataset Summary\n\n\nCrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains\n(Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for\ndifferent domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five\ndomains.\n\n\nFor details, see the paper:\nCrossNER: Evaluating Cross-Domain Named Entity Recognition",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nThe language data in CrossNER is in English (BCP-47 en)\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### conll2003\n\n\n* Size of downloaded dataset files: 2.69 MB\n* Size of the generated dataset: 5.26 MB\n\n\nAn example of 'train' looks as follows:",
"#### politics\n\n\n* Size of downloaded dataset files: 0.72 MB\n* Size of the generated dataset: 1.04 MB\n\n\nAn example of 'train' looks as follows:",
"#### science\n\n\n* Size of downloaded dataset files: 0.49 MB\n* Size of the generated dataset: 0.73 MB\n\n\nAn example of 'train' looks as follows:",
"#### music\n\n\n* Size of downloaded dataset files: 0.41 MB\n* Size of the generated dataset: 0.65 MB\n\n\nAn example of 'train' looks as follows:",
"#### literature\n\n\n* Size of downloaded dataset files: 0.33 MB\n* Size of the generated dataset: 0.58 MB\n\n\nAn example of 'train' looks as follows:",
"#### ai\n\n\n* Size of downloaded dataset files: 0.29 MB\n* Size of the generated dataset: 0.48 MB\n\n\nAn example of 'train' looks as follows:",
"### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'id': the instance id of this sentence, a 'string' feature.\n* 'tokens': the list of tokens of this sentence, a 'list' of 'string' features.\n* 'ner\\_tags': the list of entity tags, a 'list' of classification labels.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### 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\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @phucdev for adding this dataset."
]
| [
119,
107,
10,
26,
6,
41,
41,
41,
41,
39,
40,
85,
11,
7,
4,
10,
10,
5,
5,
9,
18,
7,
8,
14,
6,
6,
17
]
| [
"passage: TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|conll2003 #language-English #cross domain #ai #news #music #literature #politics #science #arxiv-2012.04373 #region-us \n### Dataset Summary\n\n\nCrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains\n(Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for\ndifferent domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five\ndomains.\n\n\nFor details, see the paper:\nCrossNER: Evaluating Cross-Domain Named Entity Recognition### Supported Tasks and Leaderboards### Languages\n\n\nThe language data in CrossNER is in English (BCP-47 en)\n\n\nDataset Structure\n-----------------### Data Instances#### conll2003\n\n\n* Size of downloaded dataset files: 2.69 MB\n* Size of the generated dataset: 5.26 MB\n\n\nAn example of 'train' looks as follows:#### politics\n\n\n* Size of downloaded dataset files: 0.72 MB\n* Size of the generated dataset: 1.04 MB\n\n\nAn example of 'train' looks as follows:#### science\n\n\n* Size of downloaded dataset files: 0.49 MB\n* Size of the generated dataset: 0.73 MB\n\n\nAn example of 'train' looks as follows:#### music\n\n\n* Size of downloaded dataset files: 0.41 MB\n* Size of the generated dataset: 0.65 MB\n\n\nAn example of 'train' looks as follows:#### literature\n\n\n* Size of downloaded dataset files: 0.33 MB\n* Size of the generated dataset: 0.58 MB\n\n\nAn example of 'train' looks as follows:"
]
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|
eb583481a1fba449b36686456c60afa80cf8c7c3 | # Dataset Card for CrossRE
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [CrossRE](https://github.com/mainlp/CrossRE)
- **Paper:** [CrossRE: A Cross-Domain Dataset for Relation Extraction](https://arxiv.org/abs/2210.09345)
### Dataset Summary
CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes
multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and
artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain
dataset for NER which contains domain-specific entity types.
The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL,
GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and
RELATED-TO.
For details, see the paper: https://arxiv.org/abs/2210.09345
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language data in CrossRE is in English (BCP-47 en)
## Dataset Structure
### Data Instances
#### news
- **Size of downloaded dataset files:** 0.24 MB
- **Size of the generated dataset:** 0.22 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "news-train-1",
"sentence": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."],
"ner": [
{"id-start": 0, "id-end": 0, "entity-type": "organisation"},
{"id-start": 2, "id-end": 3, "entity-type": "misc"},
{"id-start": 6, "id-end": 7, "entity-type": "misc"}
],
"relations": [
{"id_1-start": 0, "id_1-end": 0, "id_2-start": 2, "id_2-end": 3, "relation-type": "opposite", "Exp": "rejects", "Un": False, "SA": False},
{"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "opposite", "Exp": "calls_for_boycot_of", "Un": False, "SA": False},
{"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "topic", "Exp": "", "Un": False, "SA": False}
]
}
```
#### politics
- **Size of downloaded dataset files:** 0.73 MB
- **Size of the generated dataset:** 0.65 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "politics-train-1",
"sentence": ["Parties", "with", "mainly", "Eurosceptic", "views", "are", "the", "ruling", "United", "Russia", ",", "and", "opposition", "parties", "the", "Communist", "Party", "of", "the", "Russian", "Federation", "and", "Liberal", "Democratic", "Party", "of", "Russia", "."],
"ner": [
{"id-start": 8, "id-end": 9, "entity-type": "politicalparty"},
{"id-start": 15, "id-end": 20, "entity-type": "politicalparty"},
{"id-start": 22, "id-end": 26, "entity-type": "politicalparty"}
],
"relations": [
{"id_1-start": 8, "id_1-end": 9, "id_2-start": 15, "id_2-end": 20, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False},
{"id_1-start": 8, "id_1-end": 9, "id_2-start": 22, "id_2-end": 26, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False}
]
}
```
#### science
- **Size of downloaded dataset files:** 0.59 MB
- **Size of the generated dataset:** 0.54 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "science-train-1",
"sentence": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."],
"ner": [
{"id-start": 4, "id-end": 5, "entity-type": "chemicalcompound"},
{"id-start": 7, "id-end": 8, "entity-type": "chemicalcompound"},
{"id-start": 11, "id-end": 11, "entity-type": "chemicalcompound"}
],
"relations": []
}
```
#### music
- **Size of downloaded dataset files:** 0.73 MB
- **Size of the generated dataset:** 0.64 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "music-train-1",
"sentence": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."],
"ner": [
{"id-start": 4, "id-end": 6, "entity-type": "location"},
{"id-start": 13, "id-end": 17, "entity-type": "event"}
],
"relations": [
{"id_1-start": 13, "id_1-end": 17, "id_2-start": 4, "id_2-end": 6, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}
]
}
```
#### literature
- **Size of downloaded dataset files:** 0.64 MB
- **Size of the generated dataset:** 0.57 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "literature-train-1",
"sentence": ["In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "."],
"ner": [
{"id-start": 7, "id-end": 9, "entity-type": "person"},
{"id-start": 12, "id-end": 13, "entity-type": "country"},
{"id-start": 17, "id-end": 18, "entity-type": "writer"},
{"id-start": 20, "id-end": 20, "entity-type": "writer"},
{"id-start": 26, "id-end": 27, "entity-type": "writer"},
{"id-start": 29, "id-end": 29, "entity-type": "writer"},
{"id-start": 33, "id-end": 33, "entity-type": "country"},
{"id-start": 35, "id-end": 35, "entity-type": "country"},
{"id-start": 38, "id-end": 38, "entity-type": "misc"},
{"id-start": 45, "id-end": 46, "entity-type": "person"},
{"id-start": 49, "id-end": 50, "entity-type": "misc"},
{"id-start": 55, "id-end": 55, "entity-type": "person"}
],
"relations": [
{"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "role", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 17, "id_1-end": 18, "id_2-start": 26, "id_2-end": 27, "relation-type": "social", "Exp": "family", "Un": False, "SA": False},
{"id_1-start": 20, "id_1-end": 20, "id_2-start": 17, "id_2-end": 18, "relation-type": "named", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 33, "id_2-end": 33, "relation-type": "physical", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 35, "id_2-end": 35, "relation-type": "physical", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 38, "id_2-end": 38, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 45, "id_2-end": 46, "relation-type": "social", "Exp": "greeted_by", "Un": False, "SA": False},
{"id_1-start": 29, "id_1-end": 29, "id_2-start": 26, "id_2-end": 27, "relation-type": "named", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 45, "id_1-end": 46, "id_2-start": 55, "id_2-end": 55, "relation-type": "social", "Exp": "marriage", "Un": False, "SA": False},
{"id_1-start": 49, "id_1-end": 50, "id_2-start": 45, "id_2-end": 46, "relation-type": "named", "Exp": "", "Un": False, "SA": False}
]
}
```
#### ai
- **Size of downloaded dataset files:** 0.51 MB
- **Size of the generated dataset:** 0.46 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "ai-train-1",
"sentence": ["Popular", "approaches", "of", "opinion-based", "recommender", "system", "utilize", "various", "techniques", "including", "text", "mining", ",", "information", "retrieval", ",", "sentiment", "analysis", "(", "see", "also", "Multimodal", "sentiment", "analysis", ")", "and", "deep", "learning", "X.Y.", "Feng", ",", "H.", "Zhang", ",", "Y.J.", "Ren", ",", "P.H.", "Shang", ",", "Y.", "Zhu", ",", "Y.C.", "Liang", ",", "R.C.", "Guan", ",", "D.", "Xu", ",", "(", "2019", ")", ",", ",", "21", "(", "5", ")", ":", "e12957", "."],
"ner": [
{"id-start": 3, "id-end": 5, "entity-type": "product"},
{"id-start": 10, "id-end": 11, "entity-type": "field"},
{"id-start": 13, "id-end": 14, "entity-type": "task"},
{"id-start": 16, "id-end": 17, "entity-type": "task"},
{"id-start": 21, "id-end": 23, "entity-type": "task"},
{"id-start": 26, "id-end": 27, "entity-type": "field"},
{"id-start": 28, "id-end": 29, "entity-type": "researcher"},
{"id-start": 31, "id-end": 32, "entity-type": "researcher"},
{"id-start": 34, "id-end": 35, "entity-type": "researcher"},
{"id-start": 37, "id-end": 38, "entity-type": "researcher"},
{"id-start": 40, "id-end": 41, "entity-type": "researcher"},
{"id-start": 43, "id-end": 44, "entity-type": "researcher"},
{"id-start": 46, "id-end": 47, "entity-type": "researcher"},
{"id-start": 49, "id-end": 50, "entity-type": "researcher"}
],
"relations": [
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "type-of", "Exp": "", "Un": False, "SA": False}
]
}
```
### Data Fields
The data fields are the same among all splits.
- `doc_key`: the instance id of this sentence, a `string` feature.
- `sentence`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features.
- `ner`: the list of named entities in this sentence, a `list` of `dict` features.
- `id-start`: the start index of the entity, a `int` feature.
- `id-end`: the end index of the entity, a `int` feature.
- `entity-type`: the type of the entity, a `string` feature.
- `relations`: the list of relations in this sentence, a `list` of `dict` features.
- `id_1-start`: the start index of the first entity, a `int` feature.
- `id_1-end`: the end index of the first entity, a `int` feature.
- `id_2-start`: the start index of the second entity, a `int` feature.
- `id_2-end`: the end index of the second entity, a `int` feature.
- `relation-type`: the type of the relation, a `string` feature.
- `Exp`: the explanation of the relation type assigned, a `string` feature.
- `Un`: uncertainty of the annotator, a `bool` feature.
- `SA`: existence of syntax ambiguity which poses a challenge for the annotator, a `bool` feature.
### Data Splits
#### Sentences
| | Train | Dev | Test | Total |
|--------------|---------|---------|---------|---------|
| news | 164 | 350 | 400 | 914 |
| politics | 101 | 350 | 400 | 851 |
| science | 103 | 351 | 400 | 854 |
| music | 100 | 350 | 399 | 849 |
| literature | 100 | 400 | 416 | 916 |
| ai | 100 | 350 | 431 | 881 |
| ------------ | ------- | ------- | ------- | ------- |
| total | 668 | 2,151 | 2,46 | 5,265 |
#### Relations
| | Train | Dev | Test | Total |
|--------------|---------|---------|---------|---------|
| news | 175 | 300 | 396 | 871 |
| politics | 502 | 1,616 | 1,831 | 3,949 |
| science | 355 | 1,340 | 1,393 | 3,088 |
| music | 496 | 1,861 | 2,333 | 4,690 |
| literature | 397 | 1,539 | 1,591 | 3,527 |
| ai | 350 | 1,006 | 1,127 | 2,483 |
| ------------ | ------- | ------- | ------- | ------- |
| total | 2,275 | 7,662 | 8,671 | 18,608 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{bassignana-plank-2022-crossre,
title = "Cross{RE}: A {C}ross-{D}omain {D}ataset for {R}elation {E}xtraction",
author = "Bassignana, Elisa and Plank, Barbara",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics"
}
```
### Contributions
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset. | DFKI-SLT/cross_re | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|cross_ner",
"language:en",
"cross domain",
"ai",
"news",
"music",
"literature",
"politics",
"science",
"arxiv:2210.09345",
"region:us"
]
| 2023-01-19T09:18:42+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|cross_ner"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "CrossRE is a cross-domain dataset for relation extraction", "tags": ["cross domain", "ai", "news", "music", "literature", "politics", "science"], "dataset_info": [{"config_name": "ai", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "ner", "sequence": [{"name": "id-start", "dtype": "int32"}, {"name": "id-end", "dtype": "int32"}, {"name": "entity-type", "dtype": "string"}]}, {"name": "relations", "sequence": [{"name": "id_1-start", "dtype": "int32"}, {"name": "id_1-end", "dtype": "int32"}, {"name": "id_2-start", "dtype": "int32"}, {"name": "id_2-end", "dtype": "int32"}, {"name": "relation-type", "dtype": "string"}, {"name": "Exp", "dtype": "string"}, {"name": "Un", "dtype": "bool"}, {"name": "SA", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 62411, "num_examples": 100}, {"name": "validation", "num_bytes": 183717, "num_examples": 350}, {"name": "test", "num_bytes": 217353, "num_examples": 431}], "download_size": 508107, "dataset_size": 463481}, {"config_name": "literature", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "ner", "sequence": [{"name": "id-start", "dtype": "int32"}, {"name": "id-end", "dtype": "int32"}, {"name": "entity-type", "dtype": "string"}]}, {"name": "relations", "sequence": [{"name": "id_1-start", "dtype": "int32"}, {"name": "id_1-end", "dtype": "int32"}, {"name": "id_2-start", "dtype": "int32"}, {"name": "id_2-end", "dtype": "int32"}, {"name": "relation-type", "dtype": "string"}, {"name": "Exp", "dtype": "string"}, {"name": "Un", "dtype": "bool"}, {"name": "SA", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 62699, "num_examples": 100}, {"name": "validation", "num_bytes": 246214, "num_examples": 400}, {"name": "test", "num_bytes": 264450, "num_examples": 416}], "download_size": 635130, "dataset_size": 573363}, {"config_name": "music", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "ner", "sequence": [{"name": "id-start", "dtype": "int32"}, {"name": "id-end", "dtype": "int32"}, {"name": "entity-type", "dtype": "string"}]}, {"name": "relations", "sequence": [{"name": "id_1-start", "dtype": "int32"}, {"name": "id_1-end", "dtype": "int32"}, {"name": "id_2-start", "dtype": "int32"}, {"name": "id_2-end", "dtype": "int32"}, {"name": "relation-type", "dtype": "string"}, {"name": "Exp", "dtype": "string"}, {"name": "Un", "dtype": "bool"}, {"name": "SA", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 69846, "num_examples": 100}, {"name": "validation", "num_bytes": 261497, "num_examples": 350}, {"name": "test", "num_bytes": 312165, "num_examples": 399}], "download_size": 726956, "dataset_size": 643508}, {"config_name": "news", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "ner", "sequence": [{"name": "id-start", "dtype": "int32"}, {"name": "id-end", "dtype": "int32"}, {"name": "entity-type", "dtype": "string"}]}, {"name": "relations", "sequence": [{"name": "id_1-start", "dtype": "int32"}, {"name": "id_1-end", "dtype": "int32"}, {"name": "id_2-start", "dtype": "int32"}, {"name": "id_2-end", "dtype": "int32"}, {"name": "relation-type", "dtype": "string"}, {"name": "Exp", "dtype": "string"}, {"name": "Un", "dtype": "bool"}, {"name": "SA", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 49102, "num_examples": 164}, {"name": "validation", "num_bytes": 77952, "num_examples": 350}, {"name": "test", "num_bytes": 96301, "num_examples": 400}], "download_size": 239763, "dataset_size": 223355}, {"config_name": "politics", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "ner", "sequence": [{"name": "id-start", "dtype": "int32"}, {"name": "id-end", "dtype": "int32"}, {"name": "entity-type", "dtype": "string"}]}, {"name": "relations", "sequence": [{"name": "id_1-start", "dtype": "int32"}, {"name": "id_1-end", "dtype": "int32"}, {"name": "id_2-start", "dtype": "int32"}, {"name": "id_2-end", "dtype": "int32"}, {"name": "relation-type", "dtype": "string"}, {"name": "Exp", "dtype": "string"}, {"name": "Un", "dtype": "bool"}, {"name": "SA", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 76004, "num_examples": 101}, {"name": "validation", "num_bytes": 277633, "num_examples": 350}, {"name": "test", "num_bytes": 295294, "num_examples": 400}], "download_size": 726427, "dataset_size": 648931}, {"config_name": "science", "features": [{"name": "doc_key", "dtype": "string"}, {"name": "sentence", "sequence": "string"}, {"name": "ner", "sequence": [{"name": "id-start", "dtype": "int32"}, {"name": "id-end", "dtype": "int32"}, {"name": "entity-type", "dtype": "string"}]}, {"name": "relations", "sequence": [{"name": "id_1-start", "dtype": "int32"}, {"name": "id_1-end", "dtype": "int32"}, {"name": "id_2-start", "dtype": "int32"}, {"name": "id_2-end", "dtype": "int32"}, {"name": "relation-type", "dtype": "string"}, {"name": "Exp", "dtype": "string"}, {"name": "Un", "dtype": "bool"}, {"name": "SA", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 63876, "num_examples": 103}, {"name": "validation", "num_bytes": 224402, "num_examples": 351}, {"name": "test", "num_bytes": 249075, "num_examples": 400}], "download_size": 594058, "dataset_size": 537353}]} | 2023-01-19T09:19:12+00:00 | [
"2210.09345"
]
| [
"en"
]
| TAGS
#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|cross_ner #language-English #cross domain #ai #news #music #literature #politics #science #arxiv-2210.09345 #region-us
| Dataset Card for CrossRE
========================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Repository: CrossRE
* Paper: CrossRE: A Cross-Domain Dataset for Relation Extraction
### Dataset Summary
CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes
multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and
artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain
dataset for NER which contains domain-specific entity types.
The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL,
GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and
RELATED-TO.
For details, see the paper: URL
### Supported Tasks and Leaderboards
### Languages
The language data in CrossRE is in English (BCP-47 en)
Dataset Structure
-----------------
### Data Instances
#### news
* Size of downloaded dataset files: 0.24 MB
* Size of the generated dataset: 0.22 MB
An example of 'train' looks as follows:
#### politics
* Size of downloaded dataset files: 0.73 MB
* Size of the generated dataset: 0.65 MB
An example of 'train' looks as follows:
#### science
* Size of downloaded dataset files: 0.59 MB
* Size of the generated dataset: 0.54 MB
An example of 'train' looks as follows:
#### music
* Size of downloaded dataset files: 0.73 MB
* Size of the generated dataset: 0.64 MB
An example of 'train' looks as follows:
#### literature
* Size of downloaded dataset files: 0.64 MB
* Size of the generated dataset: 0.57 MB
An example of 'train' looks as follows:
#### ai
* Size of downloaded dataset files: 0.51 MB
* Size of the generated dataset: 0.46 MB
An example of 'train' looks as follows:
### Data Fields
The data fields are the same among all splits.
* 'doc\_key': the instance id of this sentence, a 'string' feature.
* 'sentence': the list of tokens of this sentence, obtained with spaCy, a 'list' of 'string' features.
* 'ner': the list of named entities in this sentence, a 'list' of 'dict' features.
+ 'id-start': the start index of the entity, a 'int' feature.
+ 'id-end': the end index of the entity, a 'int' feature.
+ 'entity-type': the type of the entity, a 'string' feature.
* 'relations': the list of relations in this sentence, a 'list' of 'dict' features.
+ 'id\_1-start': the start index of the first entity, a 'int' feature.
+ 'id\_1-end': the end index of the first entity, a 'int' feature.
+ 'id\_2-start': the start index of the second entity, a 'int' feature.
+ 'id\_2-end': the end index of the second entity, a 'int' feature.
+ 'relation-type': the type of the relation, a 'string' feature.
+ 'Exp': the explanation of the relation type assigned, a 'string' feature.
+ 'Un': uncertainty of the annotator, a 'bool' feature.
+ 'SA': existence of syntax ambiguity which poses a challenge for the annotator, a 'bool' feature.
### Data Splits
#### Sentences
#### Relations
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
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @phucdev for adding this dataset.
| [
"### Dataset Summary\n\n\nCrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes\nmultilabel annotations. It includes the following domains: news, politics, natural science, music, literature and\nartificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain\ndataset for NER which contains domain-specific entity types.\nThe dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL,\nGENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and\nRELATED-TO.\n\n\nFor details, see the paper: URL",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nThe language data in CrossRE is in English (BCP-47 en)\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### news\n\n\n* Size of downloaded dataset files: 0.24 MB\n* Size of the generated dataset: 0.22 MB\n\n\nAn example of 'train' looks as follows:",
"#### politics\n\n\n* Size of downloaded dataset files: 0.73 MB\n* Size of the generated dataset: 0.65 MB\n\n\nAn example of 'train' looks as follows:",
"#### science\n\n\n* Size of downloaded dataset files: 0.59 MB\n* Size of the generated dataset: 0.54 MB\n\n\nAn example of 'train' looks as follows:",
"#### music\n\n\n* Size of downloaded dataset files: 0.73 MB\n* Size of the generated dataset: 0.64 MB\n\n\nAn example of 'train' looks as follows:",
"#### literature\n\n\n* Size of downloaded dataset files: 0.64 MB\n* Size of the generated dataset: 0.57 MB\n\n\nAn example of 'train' looks as follows:",
"#### ai\n\n\n* Size of downloaded dataset files: 0.51 MB\n* Size of the generated dataset: 0.46 MB\n\n\nAn example of 'train' looks as follows:",
"### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'doc\\_key': the instance id of this sentence, a 'string' feature.\n* 'sentence': the list of tokens of this sentence, obtained with spaCy, a 'list' of 'string' features.\n* 'ner': the list of named entities in this sentence, a 'list' of 'dict' features.\n\t+ 'id-start': the start index of the entity, a 'int' feature.\n\t+ 'id-end': the end index of the entity, a 'int' feature.\n\t+ 'entity-type': the type of the entity, a 'string' feature.\n* 'relations': the list of relations in this sentence, a 'list' of 'dict' features.\n\t+ 'id\\_1-start': the start index of the first entity, a 'int' feature.\n\t+ 'id\\_1-end': the end index of the first entity, a 'int' feature.\n\t+ 'id\\_2-start': the start index of the second entity, a 'int' feature.\n\t+ 'id\\_2-end': the end index of the second entity, a 'int' feature.\n\t+ 'relation-type': the type of the relation, a 'string' feature.\n\t+ 'Exp': the explanation of the relation type assigned, a 'string' feature.\n\t+ 'Un': uncertainty of the annotator, a 'bool' feature.\n\t+ 'SA': existence of syntax ambiguity which poses a challenge for the annotator, a 'bool' feature.",
"### Data Splits",
"#### Sentences",
"#### Relations\n\n\n\nDataset Creation\n----------------",
"### 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\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @phucdev for adding this dataset."
]
| [
"TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|cross_ner #language-English #cross domain #ai #news #music #literature #politics #science #arxiv-2210.09345 #region-us \n",
"### Dataset Summary\n\n\nCrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes\nmultilabel annotations. It includes the following domains: news, politics, natural science, music, literature and\nartificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain\ndataset for NER which contains domain-specific entity types.\nThe dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL,\nGENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and\nRELATED-TO.\n\n\nFor details, see the paper: URL",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nThe language data in CrossRE is in English (BCP-47 en)\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### news\n\n\n* Size of downloaded dataset files: 0.24 MB\n* Size of the generated dataset: 0.22 MB\n\n\nAn example of 'train' looks as follows:",
"#### politics\n\n\n* Size of downloaded dataset files: 0.73 MB\n* Size of the generated dataset: 0.65 MB\n\n\nAn example of 'train' looks as follows:",
"#### science\n\n\n* Size of downloaded dataset files: 0.59 MB\n* Size of the generated dataset: 0.54 MB\n\n\nAn example of 'train' looks as follows:",
"#### music\n\n\n* Size of downloaded dataset files: 0.73 MB\n* Size of the generated dataset: 0.64 MB\n\n\nAn example of 'train' looks as follows:",
"#### literature\n\n\n* Size of downloaded dataset files: 0.64 MB\n* Size of the generated dataset: 0.57 MB\n\n\nAn example of 'train' looks as follows:",
"#### ai\n\n\n* Size of downloaded dataset files: 0.51 MB\n* Size of the generated dataset: 0.46 MB\n\n\nAn example of 'train' looks as follows:",
"### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'doc\\_key': the instance id of this sentence, a 'string' feature.\n* 'sentence': the list of tokens of this sentence, obtained with spaCy, a 'list' of 'string' features.\n* 'ner': the list of named entities in this sentence, a 'list' of 'dict' features.\n\t+ 'id-start': the start index of the entity, a 'int' feature.\n\t+ 'id-end': the end index of the entity, a 'int' feature.\n\t+ 'entity-type': the type of the entity, a 'string' feature.\n* 'relations': the list of relations in this sentence, a 'list' of 'dict' features.\n\t+ 'id\\_1-start': the start index of the first entity, a 'int' feature.\n\t+ 'id\\_1-end': the end index of the first entity, a 'int' feature.\n\t+ 'id\\_2-start': the start index of the second entity, a 'int' feature.\n\t+ 'id\\_2-end': the end index of the second entity, a 'int' feature.\n\t+ 'relation-type': the type of the relation, a 'string' feature.\n\t+ 'Exp': the explanation of the relation type assigned, a 'string' feature.\n\t+ 'Un': uncertainty of the annotator, a 'bool' feature.\n\t+ 'SA': existence of syntax ambiguity which poses a challenge for the annotator, a 'bool' feature.",
"### Data Splits",
"#### Sentences",
"#### Relations\n\n\n\nDataset Creation\n----------------",
"### 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\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @phucdev for adding this dataset."
]
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115,
201,
10,
26,
6,
39,
42,
39,
41,
40,
40,
370,
5,
4,
10,
7,
4,
10,
10,
5,
5,
9,
18,
7,
8,
14,
6,
6,
17
]
| [
"passage: TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|cross_ner #language-English #cross domain #ai #news #music #literature #politics #science #arxiv-2210.09345 #region-us \n### Dataset Summary\n\n\nCrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes\nmultilabel annotations. It includes the following domains: news, politics, natural science, music, literature and\nartificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain\ndataset for NER which contains domain-specific entity types.\nThe dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL,\nGENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and\nRELATED-TO.\n\n\nFor details, see the paper: URL### Supported Tasks and Leaderboards### Languages\n\n\nThe language data in CrossRE is in English (BCP-47 en)\n\n\nDataset Structure\n-----------------### Data Instances#### news\n\n\n* Size of downloaded dataset files: 0.24 MB\n* Size of the generated dataset: 0.22 MB\n\n\nAn example of 'train' looks as follows:#### politics\n\n\n* Size of downloaded dataset files: 0.73 MB\n* Size of the generated dataset: 0.65 MB\n\n\nAn example of 'train' looks as follows:#### science\n\n\n* Size of downloaded dataset files: 0.59 MB\n* Size of the generated dataset: 0.54 MB\n\n\nAn example of 'train' looks as follows:",
"passage: #### music\n\n\n* Size of downloaded dataset files: 0.73 MB\n* Size of the generated dataset: 0.64 MB\n\n\nAn example of 'train' looks as follows:#### literature\n\n\n* Size of downloaded dataset files: 0.64 MB\n* Size of the generated dataset: 0.57 MB\n\n\nAn example of 'train' looks as follows:#### ai\n\n\n* Size of downloaded dataset files: 0.51 MB\n* Size of the generated dataset: 0.46 MB\n\n\nAn example of 'train' looks as follows:### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'doc\\_key': the instance id of this sentence, a 'string' feature.\n* 'sentence': the list of tokens of this sentence, obtained with spaCy, a 'list' of 'string' features.\n* 'ner': the list of named entities in this sentence, a 'list' of 'dict' features.\n\t+ 'id-start': the start index of the entity, a 'int' feature.\n\t+ 'id-end': the end index of the entity, a 'int' feature.\n\t+ 'entity-type': the type of the entity, a 'string' feature.\n* 'relations': the list of relations in this sentence, a 'list' of 'dict' features.\n\t+ 'id\\_1-start': the start index of the first entity, a 'int' feature.\n\t+ 'id\\_1-end': the end index of the first entity, a 'int' feature.\n\t+ 'id\\_2-start': the start index of the second entity, a 'int' feature.\n\t+ 'id\\_2-end': the end index of the second entity, a 'int' feature.\n\t+ 'relation-type': the type of the relation, a 'string' feature.\n\t+ 'Exp': the explanation of the relation type assigned, a 'string' feature.\n\t+ 'Un': uncertainty of the annotator, a 'bool' feature.\n\t+ 'SA': existence of syntax ambiguity which poses a challenge for the annotator, a 'bool' feature.### Data Splits#### Sentences#### Relations\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations"
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|
2384dc23f2c9f694a60cd73c93f7f16042c729c8 | # Dataset Card for E3C
## Dataset Description
- **Public:** True
- **Tasks:** NER
This dataset is an annotated corpus of clinical texts from E3C using Large Language Models (LLM). | bio-datasets/e3c-llm | [
"region:us"
]
| 2023-01-19T10:16:10+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens_offsets", "sequence": {"sequence": "int32"}}, {"name": "clinical_entity_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-CLINENTITY", "2": "I-CLINENTITY"}}}}], "config_name": "e3c-llm", "splits": [{"name": "en_layer1", "num_bytes": 768555, "num_examples": 1520}, {"name": "en_layer2_validation", "num_bytes": 175089, "num_examples": 334}, {"name": "fr_layer1", "num_bytes": 758368, "num_examples": 1109}, {"name": "eu_layer2", "num_bytes": 503182, "num_examples": 1594}, {"name": "eu_layer2_validation", "num_bytes": 131870, "num_examples": 468}, {"name": "it_layer2", "num_bytes": 1590730, "num_examples": 2436}, {"name": "es_layer2_validation", "num_bytes": 166201, "num_examples": 261}, {"name": "fr_layer2_validation", "num_bytes": 170233, "num_examples": 293}, {"name": "es_layer2", "num_bytes": 1506040, "num_examples": 2347}, {"name": "en_layer2", "num_bytes": 1539228, "num_examples": 2873}, {"name": "fr_layer2", "num_bytes": 1583560, "num_examples": 2389}, {"name": "eu_layer1", "num_bytes": 910983, "num_examples": 3126}, {"name": "it_layer1", "num_bytes": 768769, "num_examples": 1145}, {"name": "es_layer1", "num_bytes": 754628, "num_examples": 1134}, {"name": "it_layer2_validation", "num_bytes": 172651, "num_examples": 275}], "download_size": 0, "dataset_size": 11500087}} | 2023-04-13T13:18:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for E3C
## Dataset Description
- Public: True
- Tasks: NER
This dataset is an annotated corpus of clinical texts from E3C using Large Language Models (LLM). | [
"# Dataset Card for E3C",
"## Dataset Description\n\n- Public: True\n- Tasks: NER\n\nThis dataset is an annotated corpus of clinical texts from E3C using Large Language Models (LLM)."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for E3C",
"## Dataset Description\n\n- Public: True\n- Tasks: NER\n\nThis dataset is an annotated corpus of clinical texts from E3C using Large Language Models (LLM)."
]
| [
6,
8,
41
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for E3C## Dataset Description\n\n- Public: True\n- Tasks: NER\n\nThis dataset is an annotated corpus of clinical texts from E3C using Large Language Models (LLM)."
]
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|
d1d6639940c26de0034ab46343c31e430e466b16 | # Dataset Card for "research-paper-tokenized-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dipudl/research-paper-tokenized-dataset | [
"region:us"
]
| 2023-01-19T10:48:13+00:00 | {"dataset_info": {"features": [{"name": "labels", "sequence": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 1087419951, "num_examples": 861228}], "download_size": 376657164, "dataset_size": 1087419951}} | 2023-01-19T19:55:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "research-paper-tokenized-dataset"
More Information needed | [
"# Dataset Card for \"research-paper-tokenized-dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"research-paper-tokenized-dataset\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"research-paper-tokenized-dataset\"\n\nMore Information needed"
]
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|
9c19883835e644f8e7e204ef4d1b96f446c9dc03 | # Dataset Card for "Canadian_Cropland_Dataset"
## Dataset Description
- **Paper** [Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification](https://openreview.net/pdf/3b9f82b0ce8f1e195c4c20df9637afd8ed9ea339.pdf)
- **Split** 2017, RGB
- **GitHub** [Canadian-cropland-dataset](https://github.com/bioinfoUQAM/Canadian-cropland-dataset)
## Split Information
This HuggingFace dataset repository contains just the 2017, RGB split.
### Licensing Information
[Montreal Data License](https://github.com/bioinfoUQAM/Canadian-cropland-dataset/blob/main/DATA_LICENSE)
## Citation Information
[Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification](https://openreview.net/pdf/3b9f82b0ce8f1e195c4c20df9637afd8ed9ea339.pdf)
```
@inproceedings{jacques2021towards,
title = {Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification},
author = {Jacques, Amanda A Boatswain and Diallo, Abdoulaye Banir{\'e} and Lord, Etienne},
year = 2021,
booktitle = {42nd Canadian Symposium on Remote Sensing: Understanding Our World: Remote Sensing for a Sustainable Future}
}
``` | jonathan-roberts1/Canadian_Cropland | [
"region:us"
]
| 2023-01-19T11:18:15+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "BARLEY", "1": "CANOLA", "2": "CORN", "3": "MIXEDWOOD", "4": "OAT", "5": "ORCHARD", "6": "PASTURE", "7": "POTATO", "8": "SOYBEAN", "9": "SPRING_WHEAT"}}}}], "splits": [{"name": "train", "num_bytes": 68287123.977, "num_examples": 14111}], "download_size": 66338711, "dataset_size": 68287123.977}, "viewer": true} | 2023-03-31T13:45:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Canadian_Cropland_Dataset"
## Dataset Description
- Paper Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification
- Split 2017, RGB
- GitHub Canadian-cropland-dataset
## Split Information
This HuggingFace dataset repository contains just the 2017, RGB split.
### Licensing Information
Montreal Data License
Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification
| [
"# Dataset Card for \"Canadian_Cropland_Dataset\"",
"## Dataset Description\n\n- Paper Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification\n- Split 2017, RGB\n- GitHub Canadian-cropland-dataset",
"## Split Information\n\nThis HuggingFace dataset repository contains just the 2017, RGB split.",
"### Licensing Information\n\nMontreal Data License\n\n\n\nTowards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Canadian_Cropland_Dataset\"",
"## Dataset Description\n\n- Paper Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification\n- Split 2017, RGB\n- GitHub Canadian-cropland-dataset",
"## Split Information\n\nThis HuggingFace dataset repository contains just the 2017, RGB split.",
"### Licensing Information\n\nMontreal Data License\n\n\n\nTowards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification"
]
| [
6,
17,
44,
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Canadian_Cropland_Dataset\"## Dataset Description\n\n- Paper Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification\n- Split 2017, RGB\n- GitHub Canadian-cropland-dataset## Split Information\n\nThis HuggingFace dataset repository contains just the 2017, RGB split.### Licensing Information\n\nMontreal Data License\n\n\n\nTowards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification"
]
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|
2b239befc81b6e3f035ce6bd52f5f4d60f5625f7 | # Flickr30k
Original paper: [From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions](https://aclanthology.org/Q14-1006)
Homepage: https://shannon.cs.illinois.edu/DenotationGraph/
Bibtex:
```
@article{young2014image,
title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions},
author={Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia},
journal={Transactions of the Association for Computational Linguistics},
volume={2},
pages={67--78},
year={2014},
publisher={MIT Press}
}
``` | nlphuji/flickr30k | [
"region:us"
]
| 2023-01-19T12:00:06+00:00 | {} | 2023-01-19T17:40:41+00:00 | []
| []
| TAGS
#region-us
| # Flickr30k
Original paper: From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Homepage: URL
Bibtex:
| [
"# Flickr30k\n\nOriginal paper: From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions\n\nHomepage: URL\n\nBibtex:"
]
| [
"TAGS\n#region-us \n",
"# Flickr30k\n\nOriginal paper: From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions\n\nHomepage: URL\n\nBibtex:"
]
| [
6,
38
]
| [
"passage: TAGS\n#region-us \n# Flickr30k\n\nOriginal paper: From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions\n\nHomepage: URL\n\nBibtex:"
]
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|
6953f48444104119a04092a33114977f06b82afc | # Dataset Card for "Taiwan-mandarin"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ryL/Taiwan-mandarin | [
"region:us"
]
| 2023-01-19T12:49:07+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 332633731.0, "num_examples": 846}, {"name": "test", "num_bytes": 82886161.0, "num_examples": 228}, {"name": "validation", "num_bytes": 98951893.0, "num_examples": 230}], "download_size": 513794624, "dataset_size": 514471785.0}} | 2023-01-19T12:53:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Taiwan-mandarin"
More Information needed | [
"# Dataset Card for \"Taiwan-mandarin\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Taiwan-mandarin\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Taiwan-mandarin\"\n\nMore Information needed"
]
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|
8d1c564ad3e67365b0ab35db507f667dac33d95d | # AutoTrain Dataset for project: enchondroma-vs-low-grade-chondrosarcoma-histology
## Dataset Description
This dataset has been automatically processed by AutoTrain for project enchondroma-vs-low-grade-chondrosarcoma-histology.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<1024x1024 RGB PIL image>",
"target": 0
},
{
"image": "<1024x1024 RGB PIL image>",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Enchondroma', 'Low-grade Chondrosarcoma'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 458 |
| valid | 115 |
| itslogannye/autotrain-data-enchondroma-vs-low-grade-chondrosarcoma-histology | [
"task_categories:image-classification",
"region:us"
]
| 2023-01-19T13:18:59+00:00 | {"task_categories": ["image-classification"]} | 2023-01-19T13:20:53+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: enchondroma-vs-low-grade-chondrosarcoma-histology
================================================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project enchondroma-vs-low-grade-chondrosarcoma-histology.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-image-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
806b513a1530a9f8d333c80f6f0a669dcb62cc3e | # Dataset Card for "relbert/semeval2012_relational_similarity"
## Dataset Description
- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
- **Paper:** [https://aclanthology.org/S12-1047/](https://aclanthology.org/S12-1047/)
- **Dataset:** SemEval2012 relational similarity dataset
### Dataset Summary
Relational similarity dataset from [SemEval2012 task 2](https://aclanthology.org/S12-1047/), compiled to fine-tune [RelBERT](https://github.com/asahi417/relbert) model.
The dataset contains a list of positive and negative word pair from 89 pre-defined relations.
The relation types are constructed on top of following 10 parent relation types.
```shell
{
1: "Class Inclusion", # Hypernym
2: "Part-Whole", # Meronym, Substance Meronym
3: "Similar", # Synonym, Co-hypornym
4: "Contrast", # Antonym
5: "Attribute", # Attribute, Event
6: "Non Attribute",
7: "Case Relation",
8: "Cause-Purpose",
9: "Space-Time",
10: "Representation"
}
```
Each of the parent relation is further grouped into child relation types where the definition can be found [here](https://drive.google.com/file/d/0BzcZKTSeYL8VenY0QkVpZVpxYnc/view?resourcekey=0-ZP-UARfJj39PcLroibHPHw).
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```shell
{
'relation_type': '8d',
'positives': [ [ "breathe", "live" ], [ "study", "learn" ], [ "speak", "communicate" ], ... ]
'negatives': [ [ "starving", "hungry" ], [ "clean", "bathe" ], [ "hungry", "starving" ], ... ]
}
```
### Data Splits
|train|validation|
|----:|---------:|
| 79 | 79 |
## Citation Information
```
@inproceedings{jurgens-etal-2012-semeval,
title = "{S}em{E}val-2012 Task 2: Measuring Degrees of Relational Similarity",
author = "Jurgens, David and
Mohammad, Saif and
Turney, Peter and
Holyoak, Keith",
booktitle = "*{SEM} 2012: The First Joint Conference on Lexical and Computational Semantics {--} Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation ({S}em{E}val 2012)",
month = "7-8 " # jun,
year = "2012",
address = "Montr{\'e}al, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S12-1047",
pages = "356--364",
}
``` | relbert/semeval2012_relational_similarity | [
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:other",
"region:us"
]
| 2023-01-19T14:19:11+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "pretty_name": "SemEval2012 relational similarity dataset"} | 2023-02-02T15:38:26+00:00 | []
| [
"en"
]
| TAGS
#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us
| Dataset Card for "relbert/semeval2012\_relational\_similarity"
==============================================================
Dataset Description
-------------------
* Repository: RelBERT
* Paper: URL
* Dataset: SemEval2012 relational similarity dataset
### Dataset Summary
Relational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.
The dataset contains a list of positive and negative word pair from 89 pre-defined relations.
The relation types are constructed on top of following 10 parent relation types.
Each of the parent relation is further grouped into child relation types where the definition can be found here.
Dataset Structure
-----------------
### Data Instances
An example of 'train' looks as follows.
### Data Splits
| [
"### Dataset Summary\n\n\nRelational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.\nThe dataset contains a list of positive and negative word pair from 89 pre-defined relations.\nThe relation types are constructed on top of following 10 parent relation types.\n\n\nEach of the parent relation is further grouped into child relation types where the definition can be found here.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example of 'train' looks as follows.",
"### Data Splits"
]
| [
"TAGS\n#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us \n",
"### Dataset Summary\n\n\nRelational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.\nThe dataset contains a list of positive and negative word pair from 89 pre-defined relations.\nThe relation types are constructed on top of following 10 parent relation types.\n\n\nEach of the parent relation is further grouped into child relation types where the definition can be found here.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example of 'train' looks as follows.",
"### Data Splits"
]
| [
33,
98,
18,
5
]
| [
"passage: TAGS\n#multilinguality-monolingual #size_categories-n<1K #language-English #license-other #region-us \n### Dataset Summary\n\n\nRelational similarity dataset from SemEval2012 task 2, compiled to fine-tune RelBERT model.\nThe dataset contains a list of positive and negative word pair from 89 pre-defined relations.\nThe relation types are constructed on top of following 10 parent relation types.\n\n\nEach of the parent relation is further grouped into child relation types where the definition can be found here.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of 'train' looks as follows.### Data Splits"
]
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|
b8dc14cad20ebc9ab92d65482a47c282f2644664 | # Dataset Card for "Uniref90_temp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Oshan/Uniref90_temp | [
"region:us"
]
| 2023-01-19T14:47:03+00:00 | {"dataset_info": {"features": [{"name": "cluster_id", "dtype": "string"}, {"name": "cluster_size", "dtype": "int64"}, {"name": "org_id", "sequence": "int64"}, {"name": "clust_memb", "sequence": "string"}, {"name": "aa_seq", "dtype": "string"}, {"name": "taxon_id", "dtype": "int64"}, {"name": "aa_len", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 693591, "num_examples": 1500}], "download_size": 561305, "dataset_size": 693591}} | 2023-01-19T14:47:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Uniref90_temp"
More Information needed | [
"# Dataset Card for \"Uniref90_temp\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Uniref90_temp\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Uniref90_temp\"\n\nMore Information needed"
]
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|
a120deecad6a6538fd804a462b16ff80a2539015 | # Dataset Card for "financial-text-combo-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nickmuchi/financial-text-combo-classification | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"size_categories:10K<n<100K",
"language:en",
"finance",
"region:us"
]
| 2023-01-19T15:12:10+00:00 | {"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification", "sentiment-classification"], "pretty_name": "FinTextComboClassification", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1989291, "num_examples": 17971}, {"name": "validation", "num_bytes": 414441, "num_examples": 3863}], "download_size": 1463828, "dataset_size": 2403732}, "tags": ["finance"]} | 2023-01-27T23:21:24+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #size_categories-10K<n<100K #language-English #finance #region-us
| # Dataset Card for "financial-text-combo-classification"
More Information needed | [
"# Dataset Card for \"financial-text-combo-classification\"\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #size_categories-10K<n<100K #language-English #finance #region-us \n",
"# Dataset Card for \"financial-text-combo-classification\"\n\nMore Information needed"
]
| [
59,
20
]
| [
"passage: TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #size_categories-10K<n<100K #language-English #finance #region-us \n# Dataset Card for \"financial-text-combo-classification\"\n\nMore Information needed"
]
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|
ba1fd5ec6e3defb78de8c83ee57a21091e6ff4e5 |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | bando168/Himnusz | [
"region:us"
]
| 2023-01-19T15:23:58+00:00 | {} | 2023-01-19T15:38:55+00:00 | []
| []
| TAGS
#region-us
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# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## 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
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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|
8649b09350949e08a7d7872730ff2617fda0a0d6 | # Dataset Card for "pexel_images_lots_with_generated_captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pexel_images_lots_with_generated_captions | [
"region:us"
]
| 2023-01-19T15:56:11+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "generated_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2467169489.125, "num_examples": 7999}], "download_size": 2418777187, "dataset_size": 2467169489.125}} | 2023-01-19T22:47:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pexel_images_lots_with_generated_captions"
More Information needed | [
"# Dataset Card for \"pexel_images_lots_with_generated_captions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pexel_images_lots_with_generated_captions\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pexel_images_lots_with_generated_captions\"\n\nMore Information needed"
]
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|
81c757b6f16d2a61af0412c1fef3732c270d1a89 | # Dataset Card for "textual-explanations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | james-burton/textual-explanations | [
"region:us"
]
| 2023-01-19T16:05:45+00:00 | {"dataset_info": {"features": [{"name": "model_name", "dtype": "string"}, {"name": "predicted_class", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "narration", "dtype": "string"}, {"name": "values", "sequence": "string"}, {"name": "sign", "sequence": "string"}, {"name": "narrative_id", "dtype": "int32"}, {"name": "unique_id", "dtype": "int32"}, {"name": "classes_dict", "dtype": "string"}, {"name": "narrative_questions", "sequence": "string"}, {"name": "feature_nums", "sequence": "string"}, {"name": "ft_num2name", "dtype": "string"}, {"name": "old2new_ft_nums", "dtype": "string"}, {"name": "old2new_classes", "dtype": "string"}, {"name": "predicted_class_label", "dtype": "string"}, {"name": "class2name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 994784, "num_examples": 375}, {"name": "validation", "num_bytes": 121591, "num_examples": 47}, {"name": "test", "num_bytes": 122830, "num_examples": 47}], "download_size": 0, "dataset_size": 1239205}} | 2023-01-19T16:09:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "textual-explanations"
More Information needed | [
"# Dataset Card for \"textual-explanations\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"textual-explanations\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"textual-explanations\"\n\nMore Information needed"
]
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|
a23b1d69b4f93c97ddee8f7bad0dc0812976e254 | # Dataset Card for "text-exp-qa-hard"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | james-burton/text-exp-qa-hard | [
"region:us"
]
| 2023-01-19T16:12:10+00:00 | {"dataset_info": {"features": [{"name": "predicted_class", "dtype": "string"}, {"name": "classes_dict", "dtype": "string"}, {"name": "feature_nums", "sequence": "string"}, {"name": "sign", "sequence": "string"}, {"name": "values", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "id", "dtype": "int32"}, {"name": "question_id", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 13000973, "num_examples": 27000}, {"name": "validation", "num_bytes": 1445534, "num_examples": 3000}, {"name": "test", "num_bytes": 297588, "num_examples": 469}], "download_size": 1800431, "dataset_size": 14744095}} | 2023-01-30T17:54:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "text-exp-qa-hard"
More Information needed | [
"# Dataset Card for \"text-exp-qa-hard\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"text-exp-qa-hard\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"text-exp-qa-hard\"\n\nMore Information needed"
]
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|
31539d6c16533829a8ec7d3ded9a5d8e2fc53dcb | # nameToStdName for Minecraft plugins from SpigotMC and Bukkit
From Spigot/Bukkit plugin titles and description, extract plugin names.
Main repository: https://github.com/pluget/services
## License (SPDX)
GPL-3.0 for code
ODbL-1.0 for data/models
## Creators
Maciej Błędkowski - Founder, Lead Developer | mble/nameToStdName | [
"size_categories:n<1K",
"language:en",
"license:gpl-3.0",
"code",
"ner",
"named entity recognition",
"minecraft",
"minecraft plugins",
"product name",
"region:us"
]
| 2023-01-19T18:46:56+00:00 | {"language": ["en"], "license": "gpl-3.0", "size_categories": ["n<1K"], "tags": ["code", "ner", "named entity recognition", "minecraft", "minecraft plugins", "product name"]} | 2023-01-23T18:30:55+00:00 | []
| [
"en"
]
| TAGS
#size_categories-n<1K #language-English #license-gpl-3.0 #code #ner #named entity recognition #minecraft #minecraft plugins #product name #region-us
| # nameToStdName for Minecraft plugins from SpigotMC and Bukkit
From Spigot/Bukkit plugin titles and description, extract plugin names.
Main repository: URL
## License (SPDX)
GPL-3.0 for code
ODbL-1.0 for data/models
## Creators
Maciej Błędkowski - Founder, Lead Developer | [
"# nameToStdName for Minecraft plugins from SpigotMC and Bukkit\nFrom Spigot/Bukkit plugin titles and description, extract plugin names.\nMain repository: URL",
"## License (SPDX)\nGPL-3.0 for code\nODbL-1.0 for data/models",
"## Creators\nMaciej Błędkowski - Founder, Lead Developer"
]
| [
"TAGS\n#size_categories-n<1K #language-English #license-gpl-3.0 #code #ner #named entity recognition #minecraft #minecraft plugins #product name #region-us \n",
"# nameToStdName for Minecraft plugins from SpigotMC and Bukkit\nFrom Spigot/Bukkit plugin titles and description, extract plugin names.\nMain repository: URL",
"## License (SPDX)\nGPL-3.0 for code\nODbL-1.0 for data/models",
"## Creators\nMaciej Błędkowski - Founder, Lead Developer"
]
| [
49,
41,
23,
15
]
| [
"passage: TAGS\n#size_categories-n<1K #language-English #license-gpl-3.0 #code #ner #named entity recognition #minecraft #minecraft plugins #product name #region-us \n# nameToStdName for Minecraft plugins from SpigotMC and Bukkit\nFrom Spigot/Bukkit plugin titles and description, extract plugin names.\nMain repository: URL## License (SPDX)\nGPL-3.0 for code\nODbL-1.0 for data/models## Creators\nMaciej Błędkowski - Founder, Lead Developer"
]
| [
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|
15815164efd7fa649f59cd49e3c9d9e9f810e3f6 | # Dataset Card for "rico_sca_refexp_synthetic_saved"
This is a saved snapshot of the dynamically generated [Rico SCA RefExp dataset](https://huggingface.co/datasets/ivelin/rico_sca_refexp_synthetic) | ivelin/rico_sca_refexp_synthetic_saved | [
"region:us"
]
| 2023-01-19T20:00:26+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_id", "dtype": "string"}, {"name": "labels", "list": [{"name": "prompt", "dtype": "string"}, {"name": "target_bounding_box", "struct": [{"name": "xmin", "dtype": "float32"}, {"name": "ymin", "dtype": "float32"}, {"name": "xmax", "dtype": "float32"}, {"name": "ymax", "dtype": "float32"}]}]}], "splits": [{"name": "train", "num_bytes": 2604982403.694, "num_examples": 24063}, {"name": "validation", "num_bytes": 21192787.0, "num_examples": 160}, {"name": "test", "num_bytes": 22057836.0, "num_examples": 185}], "download_size": 2096931333, "dataset_size": 2648233026.694}} | 2023-01-19T20:10:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "rico_sca_refexp_synthetic_saved"
This is a saved snapshot of the dynamically generated Rico SCA RefExp dataset | [
"# Dataset Card for \"rico_sca_refexp_synthetic_saved\"\n\nThis is a saved snapshot of the dynamically generated Rico SCA RefExp dataset"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"rico_sca_refexp_synthetic_saved\"\n\nThis is a saved snapshot of the dynamically generated Rico SCA RefExp dataset"
]
| [
6,
41
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"rico_sca_refexp_synthetic_saved\"\n\nThis is a saved snapshot of the dynamically generated Rico SCA RefExp dataset"
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|
11890d5a8ef9ee69887456021e8c80c437767fd5 |
Unzip data in the scorer
to get the architecture
data/grants... | Poupou/Gitcoin-Grant-DataBuilder | [
"license:mit",
"region:us"
]
| 2023-01-19T20:05:25+00:00 | {"license": "mit"} | 2023-01-26T21:10:11+00:00 | []
| []
| TAGS
#license-mit #region-us
|
Unzip data in the scorer
to get the architecture
data/grants... | []
| [
"TAGS\n#license-mit #region-us \n"
]
| [
11
]
| [
"passage: TAGS\n#license-mit #region-us \n"
]
| [
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0a87570753165f427cfa530fc5e2aeb5737b7e73 | # Dataset Card for "2048_has_code_filtered_base_code_review_python"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | reshinthadith/2048_has_code_filtered_base_code_review_python | [
"region:us"
]
| 2023-01-19T20:10:34+00:00 | {"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "comments", "list": [{"name": "ContentLicense", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "Score", "dtype": "string"}, {"name": "body", "dtype": "string"}]}, {"name": "answers", "list": [{"name": "body", "dtype": "string"}, {"name": "comments", "list": [{"name": "ContentLicense", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "Score", "dtype": "string"}, {"name": "body", "dtype": "string"}]}, {"name": "meta_data", "struct": [{"name": "CommentCount", "dtype": "string"}, {"name": "ContentLicense", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "ParentId", "dtype": "string"}, {"name": "Score", "dtype": "string"}]}]}, {"name": "meta_data", "struct": [{"name": "AcceptedAnswerId", "dtype": "string"}, {"name": "CommentCount", "dtype": "string"}, {"name": "ContentLicense", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "Score", "dtype": "string"}, {"name": "Tags", "sequence": "string"}, {"name": "Title", "dtype": "string"}]}, {"name": "question_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34984009.92705029, "num_examples": 6398}], "download_size": 18050163, "dataset_size": 34984009.92705029}} | 2023-01-19T20:11:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "2048_has_code_filtered_base_code_review_python"
More Information needed | [
"# Dataset Card for \"2048_has_code_filtered_base_code_review_python\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"2048_has_code_filtered_base_code_review_python\"\n\nMore Information needed"
]
| [
6,
28
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|
a0f40bf036af3ed5a037137c356f60317be71a17 | # fake_railroad_company
This is toy data I created about an imaginary railroad company.
# V1
This is the first version of the data that I generated.
# V2
I tweaked some of the weights I used to calculate the satisfaction score.
# V3
Some customers are now power users who ride more often than other users.
# V4
Customers with children are more likely to be members
| davidwisdom/fake_railroad_company | [
"task_categories:time-series-forecasting",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"trains",
"railroads",
"train",
"railroad",
"toy",
"region:us"
]
| 2023-01-19T21:15:43+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["time-series-forecasting"], "task_ids": [], "pretty_name": "Fake Railroad Company", "tags": ["trains", "railroads", "train", "railroad", "toy"]} | 2023-06-21T03:23:33+00:00 | []
| [
"en"
]
| TAGS
#task_categories-time-series-forecasting #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #trains #railroads #train #railroad #toy #region-us
| # fake_railroad_company
This is toy data I created about an imaginary railroad company.
# V1
This is the first version of the data that I generated.
# V2
I tweaked some of the weights I used to calculate the satisfaction score.
# V3
Some customers are now power users who ride more often than other users.
# V4
Customers with children are more likely to be members
| [
"# fake_railroad_company\nThis is toy data I created about an imaginary railroad company.",
"# V1\nThis is the first version of the data that I generated.",
"# V2\nI tweaked some of the weights I used to calculate the satisfaction score.",
"# V3\nSome customers are now power users who ride more often than other users.",
"# V4\nCustomers with children are more likely to be members"
]
| [
"TAGS\n#task_categories-time-series-forecasting #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #trains #railroads #train #railroad #toy #region-us \n",
"# fake_railroad_company\nThis is toy data I created about an imaginary railroad company.",
"# V1\nThis is the first version of the data that I generated.",
"# V2\nI tweaked some of the weights I used to calculate the satisfaction score.",
"# V3\nSome customers are now power users who ride more often than other users.",
"# V4\nCustomers with children are more likely to be members"
]
| [
102,
22,
16,
22,
17,
13
]
| [
"passage: TAGS\n#task_categories-time-series-forecasting #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #trains #railroads #train #railroad #toy #region-us \n# fake_railroad_company\nThis is toy data I created about an imaginary railroad company.# V1\nThis is the first version of the data that I generated.# V2\nI tweaked some of the weights I used to calculate the satisfaction score.# V3\nSome customers are now power users who ride more often than other users.# V4\nCustomers with children are more likely to be members"
]
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ccbdde04393f9c9004cf2da3cb323c085c87a729 | # Dataset Card for "2048_has_code_filtered_base_code_review_python_based_on_property"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | reshinthadith/2048_has_code_filtered_base_code_review_python_based_on_property | [
"region:us"
]
| 2023-01-19T21:26:07+00:00 | {"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "comments", "list": [{"name": "ContentLicense", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "Score", "dtype": "string"}, {"name": "body", "dtype": "string"}]}, {"name": "meta_data", "struct": [{"name": "AcceptedAnswerId", "dtype": "string"}, {"name": "CommentCount", "dtype": "string"}, {"name": "ContentLicense", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "Score", "dtype": "string"}, {"name": "Tags", "sequence": "string"}, {"name": "Title", "dtype": "string"}]}, {"name": "question_id", "dtype": "string"}, {"name": "yield", "dtype": "string"}, {"name": "answers", "list": [{"name": "body", "dtype": "string"}, {"name": "comments", "list": [{"name": "ContentLicense", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "Score", "dtype": "string"}, {"name": "body", "dtype": "string"}]}, {"name": "meta_data", "struct": [{"name": "CommentCount", "dtype": "string"}, {"name": "ContentLicense", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "ParentId", "dtype": "string"}, {"name": "Score", "dtype": "string"}]}]}], "splits": [{"name": "train", "num_bytes": 28462610, "num_examples": 6398}], "download_size": 0, "dataset_size": 28462610}} | 2023-01-19T21:36:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "2048_has_code_filtered_base_code_review_python_based_on_property"
More Information needed | [
"# Dataset Card for \"2048_has_code_filtered_base_code_review_python_based_on_property\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"2048_has_code_filtered_base_code_review_python_based_on_property\"\n\nMore Information needed"
]
| [
6,
36
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"2048_has_code_filtered_base_code_review_python_based_on_property\"\n\nMore Information needed"
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|
46a779304d9d7955c4dda1c00ae9e9b86709fe2e | # Dataset Card for MiningLegalArguments
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [GitHub](https://github.com/eliasjacob/paper_brcad5/)
- **Repository:** [Kaggle](https://www.kaggle.com/datasets/eliasjacob/brcad5)
- **Paper:** [PLOS ONE](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0272287)
- **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 [@JoelNiklaus](https://github.com/JoelNiklaus) for adding this dataset.
| joelniklaus/BrCAD-5 | [
"license:cc-by-nc-sa-4.0",
"region:us"
]
| 2023-01-20T00:45:55+00:00 | {"license": "cc-by-nc-sa-4.0"} | 2023-01-20T00:47:27+00:00 | []
| []
| TAGS
#license-cc-by-nc-sa-4.0 #region-us
| # Dataset Card for MiningLegalArguments
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage: GitHub
- Repository: Kaggle
- Paper: PLOS ONE
- Leaderboard:
- Point of Contact:
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## 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
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @JoelNiklaus for adding this dataset.
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|
6a12f025da4ce45858844b4cb68a47654aa24120 | # Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | camenduru/test | [
"region:us"
]
| 2023-01-20T02:20:07+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1147232.0, "num_examples": 8}], "download_size": 1148603, "dataset_size": 1147232.0}} | 2023-01-20T02:20:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test"
More Information needed | [
"# Dataset Card for \"test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test\"\n\nMore Information needed"
]
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|
c67864622cb78a554118958f806cf97d50992001 | # Introduction
There are four folders:
**./pink original** 30 images from videoes and dynamics, and they are all cut into squares
**./pink_cleaned**
**./pink_cleaned_processed** clean the background and fill it with white colour, I recommend use this folder
**./style_new** cute small girl style, I do not use them but just collect for storage
| ecccho/lumi-pink-2022 | [
"size_categories:n<1K",
"region:us"
]
| 2023-01-20T05:36:33+00:00 | {"size_categories": ["n<1K"]} | 2023-01-20T05:50:22+00:00 | []
| []
| TAGS
#size_categories-n<1K #region-us
| # Introduction
There are four folders:
./pink original 30 images from videoes and dynamics, and they are all cut into squares
./pink_cleaned
./pink_cleaned_processed clean the background and fill it with white colour, I recommend use this folder
./style_new cute small girl style, I do not use them but just collect for storage
| [
"# Introduction\nThere are four folders: \n./pink original 30 images from videoes and dynamics, and they are all cut into squares \n./pink_cleaned \n./pink_cleaned_processed clean the background and fill it with white colour, I recommend use this folder \n./style_new cute small girl style, I do not use them but just collect for storage"
]
| [
"TAGS\n#size_categories-n<1K #region-us \n",
"# Introduction\nThere are four folders: \n./pink original 30 images from videoes and dynamics, and they are all cut into squares \n./pink_cleaned \n./pink_cleaned_processed clean the background and fill it with white colour, I recommend use this folder \n./style_new cute small girl style, I do not use them but just collect for storage"
]
| [
16,
90
]
| [
"passage: TAGS\n#size_categories-n<1K #region-us \n# Introduction\nThere are four folders: \n./pink original 30 images from videoes and dynamics, and they are all cut into squares \n./pink_cleaned \n./pink_cleaned_processed clean the background and fill it with white colour, I recommend use this folder \n./style_new cute small girl style, I do not use them but just collect for storage"
]
| [
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11fac81362d7bcc017710e09bd3465a22ca887f7 |
407 images and captions taken from danbooru, picked and cropped by hand, 768x768 size. | cosc/cutesexyrobutts | [
"license:creativeml-openrail-m",
"region:us"
]
| 2023-01-20T06:34:48+00:00 | {"license": "creativeml-openrail-m"} | 2023-02-16T08:28:43+00:00 | []
| []
| TAGS
#license-creativeml-openrail-m #region-us
|
407 images and captions taken from danbooru, picked and cropped by hand, 768x768 size. | []
| [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
]
| [
18
]
| [
"passage: TAGS\n#license-creativeml-openrail-m #region-us \n"
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|
55bf906bdab5cd2849c339f17ef4404e8ddb0822 | # defamation_japanese_twitter
# Twitter日本語誹謗中傷検出データセット
<!-- ## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** -->
## Dataset Summary
SNSにおける誹謗中傷検出のためのデータセットです．
5,000件の日本語のツイートに，それぞれ以下で定義している誹謗中傷の対象者と内容をアノテーションしています．アノテーションは，3人のクラウドワーカーにより行われています．2022年2月15日から2022年6月30日までのツイートです．
元のツイートは含まれていないため，Twitter APIを用いてデータセットを収集してください．
中傷対象(target)と中傷内容(label)の2項目がアノテーションされています．
- target ：テキストが話題にしている対象者の分類
- label ： targetで選択された対象者に対する誹謗中傷の種類の分類
文として成立しておらず意味の取れないものはラベルC(0)としています．
| target | 対象 | 例|
| ---- | ---- | ---- |
| A1(1) | (人種・性別・職業・思想などを共通とする)グループ | (人種・性別・職業・思想などを共通とする)グループ
| A2(2) | 個人（著名人や知人など） | 〇〇大統領，芸能人の〇〇さん，おまえ
| A3(3) | 対象がはっきりしないもの |
| C(0) | 文として成立しておらず意味が取れない |
| label | 誹謗中傷の種類 | 侵害されるもの | 例
| ---- | ---- | ---- | ---- |
| B1(1) | 生命を脅かす，精神的・身体的な危害を加える | 私生活の平穏 | • 殺害予告などの脅迫発言<br>• ◯◯なんていなくなればいいのにな
| B2(2) | 容姿，人格などをけなしている | 名誉感情| • 太っているくせにカッコいいと勘違いしている<br>• 田舎育ちだからファッション感覚がない
| B3(3) | 社会から客観的に受ける価値を低下させる | 名誉権| • ◯◯さんは過去に事件を起こして逮捕されたことがある<br>• ◯◯さんは会社の同僚と不倫をしている
| B4(4) | B1-B3のどれにも当てはまらず中傷性がない | |
| C(0) | 文として成立しておらず意味が取れない |
## Data Fields
- `id` Twitter ID
- `target`: 3名のアノテータのカテゴリAの回答 values: C(0), A1(1), A2(2), A3(3)
- `label`: 3名のアノテータのカテゴリBの回答 values: C(0), B1(1), B2(2), B3(3), B4(4)
- `user_id_list`: 匿名化された回答者のID
## Example Using Twitter API
[](https://colab.research.google.com/github/kubotaissei/defamation_japanese_twitter/blob/master/notebooks/get_dataset_example.ipynb)
```python
# sample code from https://github.com/twitterdev/Twitter-API-v2-sample-code/blob/main/Tweet-Lookup/get_tweets_with_bearer_token.py
import requests
import os
import json
from datasets import load_dataset
# To set your enviornment variables in your terminal run the following line:
# export 'BEARER_TOKEN'='<your_bearer_token>'
bearer_token = os.environ.get("BEARER_TOKEN")
def create_url(ids: list):
tweet_fields = "tweet.fields=created_at"
ids = f"ids={','.join(ids)}"
url = "https://api.twitter.com/2/tweets?{}&{}".format(ids, tweet_fields)
return url
def bearer_oauth(r):
"""
Method required by bearer token authentication.
"""
r.headers["Authorization"] = f"Bearer {bearer_token}"
r.headers["User-Agent"] = "v2TweetLookupPython"
return r
def connect_to_endpoint(url):
response = requests.request("GET", url, auth=bearer_oauth)
if response.status_code != 200:
raise Exception(
"Request returned an error: {} {}".format(
response.status_code, response.text
)
)
return response.json()
def get_text_data(examples):
url = create_url(examples["id"])
json_response = connect_to_endpoint(url)
# print(json_response["data"])
text_dict = {data["id"]: data["text"] for data in json_response["data"]}
time_dict = {data["id"]: data["created_at"] for data in json_response["data"]}
return {
"text": [text_dict.get(id) for id in examples["id"]],
"created_at": [time_dict.get(id) for id in examples["id"]],
}
dataset = load_dataset("kubota/defamation-japanese-twitter")
dataset = dataset.map(get_text_data, batched=True, batch_size=100)
dataset["train"].to_pandas().head()
```
<!-- ## 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 [@kubotaissei](https://github.com/kubotaissei) for adding this dataset. | kubota/defamation-japanese-twitter | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ja",
"license:cc-by-4.0",
"region:us"
]
| 2023-01-20T06:50:46+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ja"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "defamation_japanese_twitter", "tags": [], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "target", "sequence": "string"}, {"name": "label", "sequence": "string"}, {"name": "user_id_list", "sequence": "int32"}]}} | 2023-02-06T18:26:10+00:00 | []
| [
"ja"
]
| TAGS
#task_categories-text-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Japanese #license-cc-by-4.0 #region-us
| defamation\_japanese\_twitter
=============================
Twitter日本語誹謗中傷検出データセット
======================
Dataset Summary
---------------
SNSにおける誹謗中傷検出のためのデータセットです．
5,000件の日本語のツイートに，それぞれ以下で定義している誹謗中傷の対象者と内容をアノテーションしています．アノテーションは，3人のクラウドワーカーにより行われています．2022年2月15日から2022年6月30日までのツイートです．
元のツイートは含まれていないため，Twitter APIを用いてデータセットを収集してください．
中傷対象(target)と中傷内容(label)の2項目がアノテーションされています．
* target ：テキストが話題にしている対象者の分類
* label ： targetで選択された対象者に対する誹謗中傷の種類の分類
文として成立しておらず意味の取れないものはラベルC(0)としています．
target: A1(1), 対象: (人種・性別・職業・思想などを共通とする)グループ, 例: (人種・性別・職業・思想などを共通とする)グループ
target: A2(2), 対象: 個人（著名人や知人など）, 例: 〇〇大統領，芸能人の〇〇さん，おまえ
target: A3(3), 対象: 対象がはっきりしないもの, 例:
target: C(0), 対象: 文として成立しておらず意味が取れない, 例:
Data Fields
-----------
* 'id' Twitter ID
* 'target': 3名のアノテータのカテゴリAの回答 values: C(0), A1(1), A2(2), A3(3)
* 'label': 3名のアノテータのカテゴリBの回答 values: C(0), B1(1), B2(2), B3(3), B4(4)
* 'user\_id\_list': 匿名化された回答者のID
Example Using Twitter API
-------------------------
![Open In Colab](URL
Contributions
-------------
Thanks to @kubotaissei for adding this dataset.
| []
| [
"TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Japanese #license-cc-by-4.0 #region-us \n"
]
| [
84
]
| [
"passage: TAGS\n#task_categories-text-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Japanese #license-cc-by-4.0 #region-us \n"
]
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|
d020c8636ea3634b614786543747bba081e865ec | # Paraphrase Dataset (Urdu)
This dataset contains paraphrases in Urdu. It is provided in the Parquet format and is split into a training set with 393,000 rows.
## Dataset Details
- Columns:
- `sentence1`: The first sentence in a pair of paraphrases (string).
- `sentence2`: The second sentence in a pair of paraphrases (string).
## Usage
You can use this dataset for various natural language processing tasks such as text similarity, paraphrase identification, and language generation.
| mwz/ur_para | [
"task_categories:text2text-generation",
"task_categories:summarization",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:ur",
"license:mit",
"region:us"
]
| 2023-01-20T07:11:27+00:00 | {"language": ["ur"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["text2text-generation", "summarization", "text-generation"], "pretty_name": "ur_para"} | 2023-06-24T12:06:04+00:00 | []
| [
"ur"
]
| TAGS
#task_categories-text2text-generation #task_categories-summarization #task_categories-text-generation #size_categories-100K<n<1M #language-Urdu #license-mit #region-us
| # Paraphrase Dataset (Urdu)
This dataset contains paraphrases in Urdu. It is provided in the Parquet format and is split into a training set with 393,000 rows.
## Dataset Details
- Columns:
- 'sentence1': The first sentence in a pair of paraphrases (string).
- 'sentence2': The second sentence in a pair of paraphrases (string).
## Usage
You can use this dataset for various natural language processing tasks such as text similarity, paraphrase identification, and language generation.
| [
"# Paraphrase Dataset (Urdu)\n\nThis dataset contains paraphrases in Urdu. It is provided in the Parquet format and is split into a training set with 393,000 rows.",
"## Dataset Details\n\n- Columns:\n - 'sentence1': The first sentence in a pair of paraphrases (string).\n - 'sentence2': The second sentence in a pair of paraphrases (string).",
"## Usage\n\nYou can use this dataset for various natural language processing tasks such as text similarity, paraphrase identification, and language generation."
]
| [
"TAGS\n#task_categories-text2text-generation #task_categories-summarization #task_categories-text-generation #size_categories-100K<n<1M #language-Urdu #license-mit #region-us \n",
"# Paraphrase Dataset (Urdu)\n\nThis dataset contains paraphrases in Urdu. It is provided in the Parquet format and is split into a training set with 393,000 rows.",
"## Dataset Details\n\n- Columns:\n - 'sentence1': The first sentence in a pair of paraphrases (string).\n - 'sentence2': The second sentence in a pair of paraphrases (string).",
"## Usage\n\nYou can use this dataset for various natural language processing tasks such as text similarity, paraphrase identification, and language generation."
]
| [
62,
43,
48,
33
]
| [
"passage: TAGS\n#task_categories-text2text-generation #task_categories-summarization #task_categories-text-generation #size_categories-100K<n<1M #language-Urdu #license-mit #region-us \n# Paraphrase Dataset (Urdu)\n\nThis dataset contains paraphrases in Urdu. It is provided in the Parquet format and is split into a training set with 393,000 rows.## Dataset Details\n\n- Columns:\n - 'sentence1': The first sentence in a pair of paraphrases (string).\n - 'sentence2': The second sentence in a pair of paraphrases (string).## Usage\n\nYou can use this dataset for various natural language processing tasks such as text similarity, paraphrase identification, and language generation."
]
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|
b1849b573c20e9d2ea9b4ba7e79ceb4f6c2b559f |
## Required installation
```bash
pip3 install pypdf2 pdf2image
sudo apt-get install poppler-utils
``` | jordyvl/rvl_cdip_multi | [
"license:cc-by-nc-4.0",
"region:us"
]
| 2023-01-20T08:23:10+00:00 | {"license": "cc-by-nc-4.0"} | 2023-01-23T20:09:46+00:00 | []
| []
| TAGS
#license-cc-by-nc-4.0 #region-us
|
## Required installation
| [
"## Required installation"
]
| [
"TAGS\n#license-cc-by-nc-4.0 #region-us \n",
"## Required installation"
]
| [
17,
5
]
| [
"passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n## Required installation"
]
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|
9d86e4924bc2d935bf6eb0081b1b95eb817bae38 | # Dataset Card for re-medical-annotations
## Dataset Description
### Dataset Summary
HuggingFace Dataset from the Inception Medical Annotations project.
This dataset can be used locally with any archive downloaded from Inception that contains relation annotations.
Loading this dataset requires `dkpro-cassis>=0.7.2`.
**Example**: load the dataset from the "RE Temporality POC"
```
import datasets
ds = datasets.load_dataset(
"bio-datasets/re-medical-annotations",
data_dir=<Inception Archive path>,
labels = ["bound"],
)
```
## Dataset Structure
### Data Fields
- `text (str)`: text of the sentence
- `subj_start (int)`: start char of the relation subject mention
- `subj_end (int)`: end char of the relation subject mention, exclusive
- `subj_type (str)`: NER label of the relation subject
- `obj_start (int)`: start char of the relation object mention
- `obj_end (int)`: end char of the relation object mention, exclusive
- `obj_type (str)`: NER label of the relation object
- `relation (str)`: the relation label of this instance
| bio-datasets/re-medical-annotations | [
"region:us"
]
| 2023-01-20T10:50:56+00:00 | {} | 2023-01-20T11:59:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for re-medical-annotations
## Dataset Description
### Dataset Summary
HuggingFace Dataset from the Inception Medical Annotations project.
This dataset can be used locally with any archive downloaded from Inception that contains relation annotations.
Loading this dataset requires 'dkpro-cassis>=0.7.2'.
Example: load the dataset from the "RE Temporality POC"
## Dataset Structure
### Data Fields
- 'text (str)': text of the sentence
- 'subj_start (int)': start char of the relation subject mention
- 'subj_end (int)': end char of the relation subject mention, exclusive
- 'subj_type (str)': NER label of the relation subject
- 'obj_start (int)': start char of the relation object mention
- 'obj_end (int)': end char of the relation object mention, exclusive
- 'obj_type (str)': NER label of the relation object
- 'relation (str)': the relation label of this instance
| [
"# Dataset Card for re-medical-annotations",
"## Dataset Description",
"### Dataset Summary\n\nHuggingFace Dataset from the Inception Medical Annotations project.\n\nThis dataset can be used locally with any archive downloaded from Inception that contains relation annotations.\n\nLoading this dataset requires 'dkpro-cassis>=0.7.2'.\n\nExample: load the dataset from the \"RE Temporality POC\"",
"## Dataset Structure",
"### Data Fields\n\n- 'text (str)': text of the sentence\n- 'subj_start (int)': start char of the relation subject mention\n- 'subj_end (int)': end char of the relation subject mention, exclusive\n- 'subj_type (str)': NER label of the relation subject\n- 'obj_start (int)': start char of the relation object mention\n- 'obj_end (int)': end char of the relation object mention, exclusive\n- 'obj_type (str)': NER label of the relation object\n- 'relation (str)': the relation label of this instance"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for re-medical-annotations",
"## Dataset Description",
"### Dataset Summary\n\nHuggingFace Dataset from the Inception Medical Annotations project.\n\nThis dataset can be used locally with any archive downloaded from Inception that contains relation annotations.\n\nLoading this dataset requires 'dkpro-cassis>=0.7.2'.\n\nExample: load the dataset from the \"RE Temporality POC\"",
"## Dataset Structure",
"### Data Fields\n\n- 'text (str)': text of the sentence\n- 'subj_start (int)': start char of the relation subject mention\n- 'subj_end (int)': end char of the relation subject mention, exclusive\n- 'subj_type (str)': NER label of the relation subject\n- 'obj_start (int)': start char of the relation object mention\n- 'obj_end (int)': end char of the relation object mention, exclusive\n- 'obj_type (str)': NER label of the relation object\n- 'relation (str)': the relation label of this instance"
]
| [
6,
13,
4,
81,
6,
144
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for re-medical-annotations## Dataset Description### Dataset Summary\n\nHuggingFace Dataset from the Inception Medical Annotations project.\n\nThis dataset can be used locally with any archive downloaded from Inception that contains relation annotations.\n\nLoading this dataset requires 'dkpro-cassis>=0.7.2'.\n\nExample: load the dataset from the \"RE Temporality POC\"## Dataset Structure### Data Fields\n\n- 'text (str)': text of the sentence\n- 'subj_start (int)': start char of the relation subject mention\n- 'subj_end (int)': end char of the relation subject mention, exclusive\n- 'subj_type (str)': NER label of the relation subject\n- 'obj_start (int)': start char of the relation object mention\n- 'obj_end (int)': end char of the relation object mention, exclusive\n- 'obj_type (str)': NER label of the relation object\n- 'relation (str)': the relation label of this instance"
]
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]
|
5bc4a5c387ebf97cfe2fbb44f2db611bca5c5d1b | # Dataset Card for "patched_1000_test_p_100_m2_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_1000_test_p_100_m2_predictions | [
"region:us"
]
| 2023-01-20T12:17:21+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "features", "sequence": "float64"}, {"name": "m2_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 5874386840, "num_examples": 659861}], "download_size": 5594068699, "dataset_size": 5874386840}} | 2023-01-20T12:21:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_1000_test_p_100_m2_predictions"
More Information needed | [
"# Dataset Card for \"patched_1000_test_p_100_m2_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_1000_test_p_100_m2_predictions\"\n\nMore Information needed"
]
| [
6,
27
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_1000_test_p_100_m2_predictions\"\n\nMore Information needed"
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|
190471b4ec3387b61fe020f58834a3bf450e3121 | # Dataset Card for "input-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | huggingface-projects/auto-retrain-input-dataset | [
"region:us"
]
| 2023-01-20T13:35:38+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ADONIS", "1": "AFRICAN GIANT SWALLOWTAIL", "2": "AMERICAN SNOOT"}}}}], "splits": [{"name": "train", "num_bytes": 8825732.0, "num_examples": 338}], "download_size": 8823395, "dataset_size": 8825732.0}} | 2023-01-23T11:02:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "input-dataset"
More Information needed | [
"# Dataset Card for \"input-dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"input-dataset\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"input-dataset\"\n\nMore Information needed"
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|
d4621b82f778e6d98dfc63819db248a5778adcfc | # Dataset Card for "rico_sca_refexp_synthetic_flat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ivelin/rico_sca_refexp_synthetic_flat | [
"region:us"
]
| 2023-01-20T14:33:37+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "target_bounding_box", "struct": [{"name": "xmax", "dtype": "float64"}, {"name": "xmin", "dtype": "float64"}, {"name": "ymax", "dtype": "float64"}, {"name": "ymin", "dtype": "float64"}]}], "splits": [{"name": "train", "num_bytes": 40217006718.2, "num_examples": 374460}, {"name": "validation", "num_bytes": 348658434.4, "num_examples": 2720}, {"name": "test", "num_bytes": 387295818.89, "num_examples": 3347}], "download_size": 25615165078, "dataset_size": 40952960971.49}} | 2023-01-20T14:46:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "rico_sca_refexp_synthetic_flat"
More Information needed | [
"# Dataset Card for \"rico_sca_refexp_synthetic_flat\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"rico_sca_refexp_synthetic_flat\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"rico_sca_refexp_synthetic_flat\"\n\nMore Information needed"
]
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|
e7aa449a0cee5851d28b450830737ae5e5d53345 | # AutoTrain Dataset for project: rottentomato
## Dataset Description
This dataset has been automatically processed by AutoTrain for project rottentomato.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "too much of storytelling moves away from solondz's social critique , casting its audience as that of intellectual lector in contemplation of the auteur's professional injuries .",
"target": 1
},
{
"text": "what the audience feels is exhaustion , from watching a movie that is dark ( dark green , to be exact ) , sour , bloody and mean .",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['neg', 'pos'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 852 |
| valid | 214 |
| tolgadev/autotrain-data-rottentomato | [
"task_categories:text-classification",
"region:us"
]
| 2023-01-20T14:42:00+00:00 | {"task_categories": ["text-classification"]} | 2023-01-20T14:43:13+00:00 | []
| []
| TAGS
#task_categories-text-classification #region-us
| AutoTrain Dataset for project: rottentomato
===========================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project rottentomato.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-text-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-text-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
32e8eb6edf8b9d506ec328ae9aabae7846c7c3d0 |
# Dataset Card for "emotion"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3.95 MB
- **Size of the generated dataset:** 4.16 MB
- **Total amount of disk used:** 8.11 MB
### Dataset Summary
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
An example looks as follows.
```
{
"text": "im feeling quite sad and sorry for myself but ill snap out of it soon",
"label": 0
}
```
### Data Fields
The data fields are:
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5).
### Data Splits
The dataset has 2 configurations:
- split: with a total of 20_000 examples split into train, validation and split
- unsplit: with a total of 416_809 examples in a single train split
| name | train | validation | test |
|---------|-------:|-----------:|-----:|
| split | 16000 | 2000 | 2000 |
| unsplit | 416809 | n/a | n/a |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset should be used for educational and research purposes only.
### Citation Information
If you use this dataset, please cite:
```
@inproceedings{saravia-etal-2018-carer,
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
author = "Saravia, Elvis and
Liu, Hsien-Chi Toby and
Huang, Yen-Hao and
Wu, Junlin and
Chen, Yi-Shin",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D18-1404",
doi = "10.18653/v1/D18-1404",
pages = "3687--3697",
abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
}
```
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.
| philschmid/emotion | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"emotion-classification",
"region:us"
]
| 2023-01-20T14:56:20+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "paperswithcode_id": "emotion", "pretty_name": "Emotion", "tags": ["emotion-classification"], "dataset_info": [{"config_name": "split", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "sadness", "1": "joy", "2": "love", "3": "anger", "4": "fear", "5": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 1741597, "num_examples": 16000}, {"name": "validation", "num_bytes": 214703, "num_examples": 2000}, {"name": "test", "num_bytes": 217181, "num_examples": 2000}], "download_size": 740883, "dataset_size": 2173481}, {"config_name": "unsplit", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "sadness", "1": "joy", "2": "love", "3": "anger", "4": "fear", "5": "surprise"}}}}], "splits": [{"name": "train", "num_bytes": 45445685, "num_examples": 416809}], "download_size": 15388281, "dataset_size": 45445685}], "duplicated_from": "emotion", "train-eval-index": [{"config": "default", "task": "text-classification", "task_id": "multi_class_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"text": "text", "label": "target"}, "metrics": [{"type": "accuracy", "name": "Accuracy"}, {"type": "f1", "name": "F1 macro", "args": {"average": "macro"}}, {"type": "f1", "name": "F1 micro", "args": {"average": "micro"}}, {"type": "f1", "name": "F1 weighted", "args": {"average": "weighted"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}]} | 2023-01-20T14:56:20+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-other #emotion-classification #region-us
| Dataset Card for "emotion"
==========================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository:
* Paper:
* Point of Contact:
* Size of downloaded dataset files: 3.95 MB
* Size of the generated dataset: 4.16 MB
* Total amount of disk used: 8.11 MB
### Dataset Summary
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
### Supported Tasks and Leaderboards
### Languages
Dataset Structure
-----------------
### Data Instances
An example looks as follows.
### Data Fields
The data fields are:
* 'text': a 'string' feature.
* 'label': a classification label, with possible values including 'sadness' (0), 'joy' (1), 'love' (2), 'anger' (3), 'fear' (4), 'surprise' (5).
### Data Splits
The dataset has 2 configurations:
* split: with a total of 20\_000 examples split into train, validation and split
* unsplit: with a total of 416\_809 examples in a single train split
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
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
The dataset should be used for educational and research purposes only.
If you use this dataset, please cite:
### Contributions
Thanks to @lhoestq, @thomwolf, @lewtun for adding this dataset.
| [
"### Dataset Summary\n\n\nEmotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.",
"### Supported Tasks and Leaderboards",
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"#### Who are the source language producers?",
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"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nThe dataset should be used for educational and research purposes only.\n\n\nIf you use this dataset, please cite:",
"### Contributions\n\n\nThanks to @lhoestq, @thomwolf, @lewtun for adding this dataset."
]
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"### Dataset Summary\n\n\nEmotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example looks as follows.",
"### Data Fields\n\n\nThe data fields are:\n\n\n* 'text': a 'string' feature.\n* 'label': a classification label, with possible values including 'sadness' (0), 'joy' (1), 'love' (2), 'anger' (3), 'fear' (4), 'surprise' (5).",
"### Data Splits\n\n\nThe dataset has 2 configurations:\n\n\n* split: with a total of 20\\_000 examples split into train, validation and split\n* unsplit: with a total of 416\\_809 examples in a single train split\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
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"#### Who are the source language producers?",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nThe dataset should be used for educational and research purposes only.\n\n\nIf you use this dataset, please cite:",
"### Contributions\n\n\nThanks to @lhoestq, @thomwolf, @lewtun for adding this dataset."
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"passage: TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-other #emotion-classification #region-us \n### Dataset Summary\n\n\nEmotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.### Supported Tasks and Leaderboards### Languages\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example looks as follows.### Data Fields\n\n\nThe data fields are:\n\n\n* 'text': a 'string' feature.\n* 'label': a classification label, with possible values including 'sadness' (0), 'joy' (1), 'love' (2), 'anger' (3), 'fear' (4), 'surprise' (5).### Data Splits\n\n\nThe dataset has 2 configurations:\n\n\n* split: with a total of 20\\_000 examples split into train, validation and split\n* unsplit: with a total of 416\\_809 examples in a single train split\n\n\n\nDataset Creation\n----------------### 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\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nThe dataset should be used for educational and research purposes only.\n\n\nIf you use this dataset, please cite:### Contributions\n\n\nThanks to @lhoestq, @thomwolf, @lewtun for adding this dataset."
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|
7d915d6049944f6bf0b906d2b58e4725c49d822e | # Dataset Card for "NWPU-RESISC45"
## Dataset Description
- **Paper** [Remote sensing image scene classification: Benchmark and state of the art](https://ieeexplore.ieee.org/iel7/5/8045830/07891544.pdf)
### Licensing Information
[CC-BY-SA]
## Citation Information
[Remote sensing image scene classification: Benchmark and state of the art](https://ieeexplore.ieee.org/iel7/5/8045830/07891544.pdf)
```
@article{cheng2017remote,
title = {Remote sensing image scene classification: Benchmark and state of the art},
author = {Cheng, Gong and Han, Junwei and Lu, Xiaoqiang},
year = 2017,
journal = {Proceedings of the IEEE},
publisher = {IEEE},
volume = 105,
number = 10,
pages = {1865--1883}
}
``` | jonathan-roberts1/NWPU-RESISC45 | [
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
]
| 2023-01-20T15:46:31+00:00 | {"license": "other", "task_categories": ["image-classification", "zero-shot-image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "airplane", "1": "airport", "2": "baseball diamond", "3": "basketball court", "4": "beach", "5": "bridge", "6": "chaparral", "7": "church", "8": "circular farmland", "9": "cloud", "10": "commercial area", "11": "dense residential", "12": "desert", "13": "forest", "14": "freeway", "15": "golf course", "16": "ground track field", "17": "harbor", "18": "industrial area", "19": "intersection", "20": "island", "21": "lake", "22": "meadow", "23": "medium residential", "24": "mobile home park", "25": "mountain", "26": "overpass", "27": "palace", "28": "parking lot", "29": "railway", "30": "railway station", "31": "rectangular farmland", "32": "river", "33": "roundabout", "34": "runway", "35": "sea ice", "36": "ship", "37": "snowberg", "38": "sparse residential", "39": "stadium", "40": "storage tank", "41": "tennis court", "42": "terrace", "43": "thermal power station", "44": "wetland"}}}}], "splits": [{"name": "train", "num_bytes": 381151705, "num_examples": 31500}], "download_size": 424827902, "dataset_size": 381151705}} | 2023-03-31T15:57:43+00:00 | []
| []
| TAGS
#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us
| # Dataset Card for "NWPU-RESISC45"
## Dataset Description
- Paper Remote sensing image scene classification: Benchmark and state of the art
### Licensing Information
[CC-BY-SA]
Remote sensing image scene classification: Benchmark and state of the art
| [
"# Dataset Card for \"NWPU-RESISC45\"",
"## Dataset Description\n\n- Paper Remote sensing image scene classification: Benchmark and state of the art",
"### Licensing Information\n\n[CC-BY-SA]\n\n\n\nRemote sensing image scene classification: Benchmark and state of the art"
]
| [
"TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n",
"# Dataset Card for \"NWPU-RESISC45\"",
"## Dataset Description\n\n- Paper Remote sensing image scene classification: Benchmark and state of the art",
"### Licensing Information\n\n[CC-BY-SA]\n\n\n\nRemote sensing image scene classification: Benchmark and state of the art"
]
| [
37,
14,
22,
29
]
| [
"passage: TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n# Dataset Card for \"NWPU-RESISC45\"## Dataset Description\n\n- Paper Remote sensing image scene classification: Benchmark and state of the art### Licensing Information\n\n[CC-BY-SA]\n\n\n\nRemote sensing image scene classification: Benchmark and state of the art"
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|
79fbb09aa4dc9e4221379dffe151efd8759b59c3 | # Dataset Card for "SIRI-WHU"
## Dataset Description
- **Paper** [Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/36/4358825/07329997.pdf)
- **Paper** [The Fisher kernel coding framework for high spatial resolution scene classification](https://www.mdpi.com/2072-4292/8/2/157/pdf)
- **Paper** [Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/8859/7473942/07466064.pdf)
### Licensing Information
CC BY-NC-ND
## Citation Information
[Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/36/4358825/07329997.pdf)
[The Fisher kernel coding framework for high spatial resolution scene classification](https://www.mdpi.com/2072-4292/8/2/157/pdf)
[Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/8859/7473942/07466064.pdf)
```
@article{zhao2015dirichlet,
title={Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery},
author={Zhao, Bei and Zhong, Yanfei and Xia, Gui-Song and Zhang, Liangpei},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={54},
number={4},
pages={2108--2123},
year={2015},
publisher={IEEE}
}
@article{zhao2016fisher,
title={The Fisher kernel coding framework for high spatial resolution scene classification},
author={Zhao, Bei and Zhong, Yanfei and Zhang, Liangpei and Huang, Bo},
journal={Remote Sensing},
volume={8},
number={2},
pages={157},
year={2016},
publisher={MDPI}
}
@article{zhu2016bag,
title={Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery},
author={Zhu, Qiqi and Zhong, Yanfei and Zhao, Bei and Xia, Gui-Song and Zhang, Liangpei},
journal={IEEE Geoscience and Remote Sensing Letters},
volume={13},
number={6},
pages={747--751},
year={2016},
publisher={IEEE}
}
``` | jonathan-roberts1/SIRI-WHU | [
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
]
| 2023-01-20T15:46:58+00:00 | {"license": "other", "task_categories": ["image-classification", "zero-shot-image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "agriculture", "1": "commercial", "2": "harbor", "3": "idle_land", "4": "industrial", "5": "meadow", "6": "overpass", "7": "park", "8": "pond", "9": "residential", "10": "river", "11": "water"}}}}], "splits": [{"name": "train", "num_bytes": 158215614.4, "num_examples": 2400}], "download_size": 147702566, "dataset_size": 158215614.4}} | 2023-03-31T16:18:08+00:00 | []
| []
| TAGS
#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us
| # Dataset Card for "SIRI-WHU"
## Dataset Description
- Paper Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery
- Paper The Fisher kernel coding framework for high spatial resolution scene classification
- Paper Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery
### Licensing Information
CC BY-NC-ND
Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery
The Fisher kernel coding framework for high spatial resolution scene classification
Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery
| [
"# Dataset Card for \"SIRI-WHU\"",
"## Dataset Description\n\n- Paper Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery\n- Paper The Fisher kernel coding framework for high spatial resolution scene classification\n- Paper Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery",
"### Licensing Information\n\nCC BY-NC-ND\n\n\n\nDirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery\n\nThe Fisher kernel coding framework for high spatial resolution scene classification\n\nBag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery"
]
| [
"TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n",
"# Dataset Card for \"SIRI-WHU\"",
"## Dataset Description\n\n- Paper Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery\n- Paper The Fisher kernel coding framework for high spatial resolution scene classification\n- Paper Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery",
"### Licensing Information\n\nCC BY-NC-ND\n\n\n\nDirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery\n\nThe Fisher kernel coding framework for high spatial resolution scene classification\n\nBag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery"
]
| [
37,
12,
78,
80
]
| [
"passage: TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n# Dataset Card for \"SIRI-WHU\"## Dataset Description\n\n- Paper Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery\n- Paper The Fisher kernel coding framework for high spatial resolution scene classification\n- Paper Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery### Licensing Information\n\nCC BY-NC-ND\n\n\n\nDirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery\n\nThe Fisher kernel coding framework for high spatial resolution scene classification\n\nBag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery"
]
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|
89a10374941b0e80d75ae00ad6ca81d7b67e33ac | # Dataset Card for "rico_refexp_combined"
This dataset combines the crowdsourced RICO RefExp prompts from the [UIBert dataset](https://huggingface.co/datasets/ivelin/rico_sca_refexp_synthetic) and the synthetically generated prompts from the [seq2act dataset](https://huggingface.co/datasets/ivelin/rico_sca_refexp_synthetic). | ivelin/rico_refexp_combined | [
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:en",
"license:cc",
"ui refexp",
"region:us"
]
| 2023-01-20T16:29:52+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering"], "pretty_name": "UI RefExp Combined", "tags": ["ui refexp"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "target_bounding_box", "struct": [{"name": "xmax", "dtype": "float64"}, {"name": "xmin", "dtype": "float64"}, {"name": "ymax", "dtype": "float64"}, {"name": "ymin", "dtype": "float64"}]}], "splits": [{"name": "train", "num_bytes": 42127199077.08, "num_examples": 390084}, {"name": "validation", "num_bytes": 409042403.17, "num_examples": 3191}, {"name": "test", "num_bytes": 456349755.528, "num_examples": 3912}], "download_size": 27184189035, "dataset_size": 42992591235.778}} | 2023-01-20T16:46:06+00:00 | []
| [
"en"
]
| TAGS
#task_categories-question-answering #size_categories-100K<n<1M #language-English #license-cc #ui refexp #region-us
| # Dataset Card for "rico_refexp_combined"
This dataset combines the crowdsourced RICO RefExp prompts from the UIBert dataset and the synthetically generated prompts from the seq2act dataset. | [
"# Dataset Card for \"rico_refexp_combined\"\n\nThis dataset combines the crowdsourced RICO RefExp prompts from the UIBert dataset and the synthetically generated prompts from the seq2act dataset."
]
| [
"TAGS\n#task_categories-question-answering #size_categories-100K<n<1M #language-English #license-cc #ui refexp #region-us \n",
"# Dataset Card for \"rico_refexp_combined\"\n\nThis dataset combines the crowdsourced RICO RefExp prompts from the UIBert dataset and the synthetically generated prompts from the seq2act dataset."
]
| [
43,
56
]
| [
"passage: TAGS\n#task_categories-question-answering #size_categories-100K<n<1M #language-English #license-cc #ui refexp #region-us \n# Dataset Card for \"rico_refexp_combined\"\n\nThis dataset combines the crowdsourced RICO RefExp prompts from the UIBert dataset and the synthetically generated prompts from the seq2act dataset."
]
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|
f11d63929abb91d630dbf6afc91f56b200979c2a | # Dataset Card for "jojo-stone-ocean-blip-captions-512"
## JoJo's Bizarre Adventure: Stone Ocean with Blip captions.
## Dataset contains 512x512 cropped images whose source is [jojowiki](https://jojowiki.com/Stone_Ocean_(Anime)) | Norod78/jojo-stone-ocean-blip-captions-512 | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-sa-4.0",
"text-to-image",
"region:us"
]
| 2023-01-20T17:00:05+00:00 | {"language": "en", "license": "cc-by-nc-sa-4.0", "size_categories": ["1K<n<10K"], "pretty_name": "JoJo's Bizarre Adventure: Stone Ocean - Blip captions", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 94744425.832, "num_examples": 1376}], "download_size": 94450521, "dataset_size": 94744425.832}, "tags": ["text-to-image"]} | 2023-07-13T10:27:31+00:00 | []
| [
"en"
]
| TAGS
#size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-4.0 #text-to-image #region-us
| # Dataset Card for "jojo-stone-ocean-blip-captions-512"
## JoJo's Bizarre Adventure: Stone Ocean with Blip captions.
## Dataset contains 512x512 cropped images whose source is jojowiki) | [
"# Dataset Card for \"jojo-stone-ocean-blip-captions-512\"",
"## JoJo's Bizarre Adventure: Stone Ocean with Blip captions.",
"## Dataset contains 512x512 cropped images whose source is jojowiki)"
]
| [
"TAGS\n#size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-4.0 #text-to-image #region-us \n",
"# Dataset Card for \"jojo-stone-ocean-blip-captions-512\"",
"## JoJo's Bizarre Adventure: Stone Ocean with Blip captions.",
"## Dataset contains 512x512 cropped images whose source is jojowiki)"
]
| [
41,
22,
17,
19
]
| [
"passage: TAGS\n#size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-4.0 #text-to-image #region-us \n# Dataset Card for \"jojo-stone-ocean-blip-captions-512\"## JoJo's Bizarre Adventure: Stone Ocean with Blip captions.## Dataset contains 512x512 cropped images whose source is jojowiki)"
]
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|
d1a7ad247dbe0383048311bdeb452c028517c155 | # Beyond web-scraping
Original paper: [Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset
](https://arxiv.org/abs/2301.02560)
Homepage: https://geodiverse-data-collection.cs.princeton.edu/
Test split obtained from the paper authors.
Bibtex:
```
@inproceedings{ramaswamy2022geode,
author = {Vikram V. Ramaswamy and Sing Yu Lin and Dora Zhao and Aaron B. Adcock and Laurens van der Maaten and Deepti Ghadiyaram and
Olga Russakovsky},
title = {Beyond web-scraping: {C}rowd-sourcing a geodiverse dataset},
booktitle = {arXiv preprint},
year = {2023}
}
``` | nlphuji/beyond_web_scraping | [
"arxiv:2301.02560",
"region:us"
]
| 2023-01-20T17:00:09+00:00 | {} | 2023-01-20T17:12:36+00:00 | [
"2301.02560"
]
| []
| TAGS
#arxiv-2301.02560 #region-us
| # Beyond web-scraping
Original paper: Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset
Homepage: URL
Test split obtained from the paper authors.
Bibtex:
| [
"# Beyond web-scraping\n\nOriginal paper: Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset\n\n\nHomepage: URL\nTest split obtained from the paper authors. \n\nBibtex:"
]
| [
"TAGS\n#arxiv-2301.02560 #region-us \n",
"# Beyond web-scraping\n\nOriginal paper: Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset\n\n\nHomepage: URL\nTest split obtained from the paper authors. \n\nBibtex:"
]
| [
15,
46
]
| [
"passage: TAGS\n#arxiv-2301.02560 #region-us \n# Beyond web-scraping\n\nOriginal paper: Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset\n\n\nHomepage: URL\nTest split obtained from the paper authors. \n\nBibtex:"
]
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|
a93005f73bc5d36f115098ca792cf6169a344740 | # Dataset Card for "wallbed_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dadosdq/wallbed_dataset | [
"region:us"
]
| 2023-01-20T17:42:18+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 16160402.0, "num_examples": 39}], "download_size": 16162324, "dataset_size": 16160402.0}} | 2023-01-20T17:42:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wallbed_dataset"
More Information needed | [
"# Dataset Card for \"wallbed_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wallbed_dataset\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wallbed_dataset\"\n\nMore Information needed"
]
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|
668b2cabee807d5350a72bedc0e33d7433a6be33 | # Dataset Card for "pii-pile-chunk3-0-50000-tagged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | j-chim/pii-pile-chunk3-0-50000-tagged | [
"region:us"
]
| 2023-01-20T18:34:35+00:00 | {"dataset_info": {"features": [{"name": "texts", "sequence": "string"}, {"name": "meta", "struct": [{"name": "pile_set_name", "dtype": "string"}]}, {"name": "scores", "sequence": "float64"}, {"name": "avg_score", "dtype": "float64"}, {"name": "num_sents", "dtype": "int64"}, {"name": "tagged_pii_results", "list": [{"name": "analysis_explanation", "dtype": "null"}, {"name": "end", "dtype": "int64"}, {"name": "entity_type", "dtype": "string"}, {"name": "recognition_metadata", "struct": [{"name": "recognizer_identifier", "dtype": "string"}, {"name": "recognizer_name", "dtype": "string"}]}, {"name": "score", "dtype": "float64"}, {"name": "start", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 505187810, "num_examples": 50000}], "download_size": 192707833, "dataset_size": 505187810}} | 2023-01-21T02:03:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pii-pile-chunk3-0-50000-tagged"
More Information needed | [
"# Dataset Card for \"pii-pile-chunk3-0-50000-tagged\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pii-pile-chunk3-0-50000-tagged\"\n\nMore Information needed"
]
| [
6,
25
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pii-pile-chunk3-0-50000-tagged\"\n\nMore Information needed"
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]
|
82540efee3d778168f560b6a94b40f3532c774f4 |
# Architecture Regularization Images
A collection of regularization & class instance datasets of architecture for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | 3ee/regularization-architecture | [
"license:mit",
"stable-diffusion",
"regularization-images",
"text-to-image",
"image-to-image",
"dreambooth",
"class-instance",
"preservation-loss-training",
"architecture",
"region:us"
]
| 2023-01-20T18:44:31+00:00 | {"license": "mit", "tags": ["stable-diffusion", "regularization-images", "text-to-image", "image-to-image", "dreambooth", "class-instance", "preservation-loss-training", "architecture"]} | 2023-01-20T19:12:23+00:00 | []
| []
| TAGS
#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #architecture #region-us
|
# Architecture Regularization Images
A collection of regularization & class instance datasets of architecture for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | [
"# Architecture Regularization Images\n\nA collection of regularization & class instance datasets of architecture for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
"TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #architecture #region-us \n",
"# Architecture Regularization Images\n\nA collection of regularization & class instance datasets of architecture for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
57,
41
]
| [
"passage: TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #architecture #region-us \n# Architecture Regularization Images\n\nA collection of regularization & class instance datasets of architecture for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
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|
d11aaa3e488925af824db86d176a340a9b052e48 |
# Castle Regularization Images
A collection of regularization & class instance datasets of castles for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | 3ee/regularization-castle | [
"license:mit",
"stable-diffusion",
"regularization-images",
"text-to-image",
"image-to-image",
"dreambooth",
"class-instance",
"preservation-loss-training",
"region:us"
]
| 2023-01-20T18:44:53+00:00 | {"license": "mit", "tags": ["stable-diffusion", "regularization-images", "text-to-image", "image-to-image", "dreambooth", "class-instance", "preservation-loss-training"]} | 2023-01-20T19:25:41+00:00 | []
| []
| TAGS
#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us
|
# Castle Regularization Images
A collection of regularization & class instance datasets of castles for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | [
"# Castle Regularization Images\n\nA collection of regularization & class instance datasets of castles for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
"TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n",
"# Castle Regularization Images\n\nA collection of regularization & class instance datasets of castles for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
55,
40
]
| [
"passage: TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n# Castle Regularization Images\n\nA collection of regularization & class instance datasets of castles for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
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|
300b8d8167d7d274ac17f71a5b8073d06681e74f |
# Horse Regularization Images
A collection of regularization & class instance datasets of horses for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | 3ee/regularization-horse | [
"license:mit",
"stable-diffusion",
"regularization-images",
"text-to-image",
"image-to-image",
"dreambooth",
"class-instance",
"preservation-loss-training",
"region:us"
]
| 2023-01-20T18:46:07+00:00 | {"license": "mit", "tags": ["stable-diffusion", "regularization-images", "text-to-image", "image-to-image", "dreambooth", "class-instance", "preservation-loss-training"]} | 2023-01-20T19:21:02+00:00 | []
| []
| TAGS
#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us
|
# Horse Regularization Images
A collection of regularization & class instance datasets of horses for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | [
"# Horse Regularization Images\n\nA collection of regularization & class instance datasets of horses for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
"TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n",
"# Horse Regularization Images\n\nA collection of regularization & class instance datasets of horses for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
55,
40
]
| [
"passage: TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n# Horse Regularization Images\n\nA collection of regularization & class instance datasets of horses for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
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]
|
523f93cc65de4046ec445793607481a5c03e40b5 |
# Creature Regularization Images
A collection of regularization & class instance datasets of creatures for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | 3ee/regularization-creature | [
"license:mit",
"stable-diffusion",
"regularization-images",
"text-to-image",
"image-to-image",
"dreambooth",
"class-instance",
"preservation-loss-training",
"region:us"
]
| 2023-01-20T18:47:30+00:00 | {"license": "mit", "tags": ["stable-diffusion", "regularization-images", "text-to-image", "image-to-image", "dreambooth", "class-instance", "preservation-loss-training"]} | 2023-01-20T19:20:28+00:00 | []
| []
| TAGS
#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us
|
# Creature Regularization Images
A collection of regularization & class instance datasets of creatures for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | [
"# Creature Regularization Images\n\nA collection of regularization & class instance datasets of creatures for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
"TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n",
"# Creature Regularization Images\n\nA collection of regularization & class instance datasets of creatures for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
55,
41
]
| [
"passage: TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n# Creature Regularization Images\n\nA collection of regularization & class instance datasets of creatures for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
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|
26eae2964447518e7671dca7cfac0d1ba9130881 |
# Forest Regularization Images
A collection of regularization & class instance datasets of forests for the Stable Diffusion 1.5 model to use for DreamBooth prior preservation loss training. | 3ee/regularization-forest | [
"license:mit",
"stable-diffusion",
"regularization-images",
"text-to-image",
"image-to-image",
"dreambooth",
"class-instance",
"preservation-loss-training",
"forest",
"region:us"
]
| 2023-01-20T18:47:57+00:00 | {"license": "mit", "tags": ["stable-diffusion", "regularization-images", "text-to-image", "image-to-image", "dreambooth", "class-instance", "preservation-loss-training", "forest"]} | 2023-01-20T19:09:47+00:00 | []
| []
| TAGS
#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #forest #region-us
|
# Forest Regularization Images
A collection of regularization & class instance datasets of forests for the Stable Diffusion 1.5 model to use for DreamBooth prior preservation loss training. | [
"# Forest Regularization Images\n\nA collection of regularization & class instance datasets of forests for the Stable Diffusion 1.5 model to use for DreamBooth prior preservation loss training."
]
| [
"TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #forest #region-us \n",
"# Forest Regularization Images\n\nA collection of regularization & class instance datasets of forests for the Stable Diffusion 1.5 model to use for DreamBooth prior preservation loss training."
]
| [
58,
42
]
| [
"passage: TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #forest #region-us \n# Forest Regularization Images\n\nA collection of regularization & class instance datasets of forests for the Stable Diffusion 1.5 model to use for DreamBooth prior preservation loss training."
]
| [
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|
7829914720649837fa2bbe0a2340661963171e04 |
# Space Regularization Images
A collection of regularization & class instance datasets of space for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | 3ee/regularization-space | [
"license:mit",
"stable-diffusion",
"regularization-images",
"text-to-image",
"image-to-image",
"dreambooth",
"class-instance",
"preservation-loss-training",
"region:us"
]
| 2023-01-20T19:26:19+00:00 | {"license": "mit", "tags": ["stable-diffusion", "regularization-images", "text-to-image", "image-to-image", "dreambooth", "class-instance", "preservation-loss-training"]} | 2023-01-20T19:30:22+00:00 | []
| []
| TAGS
#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us
|
# Space Regularization Images
A collection of regularization & class instance datasets of space for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | [
"# Space Regularization Images\n\nA collection of regularization & class instance datasets of space for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
"TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n",
"# Space Regularization Images\n\nA collection of regularization & class instance datasets of space for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
55,
39
]
| [
"passage: TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n# Space Regularization Images\n\nA collection of regularization & class instance datasets of space for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
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|
086607dc0921967db7972f418722dbbe61466fb1 |
# Tiger Regularization Images
A collection of regularization & class instance datasets of tigers for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | 3ee/regularization-tiger | [
"license:mit",
"stable-diffusion",
"regularization-images",
"text-to-image",
"image-to-image",
"dreambooth",
"class-instance",
"preservation-loss-training",
"region:us"
]
| 2023-01-20T19:33:41+00:00 | {"license": "mit", "tags": ["stable-diffusion", "regularization-images", "text-to-image", "image-to-image", "dreambooth", "class-instance", "preservation-loss-training"]} | 2023-01-20T19:36:58+00:00 | []
| []
| TAGS
#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us
|
# Tiger Regularization Images
A collection of regularization & class instance datasets of tigers for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | [
"# Tiger Regularization Images\n\nA collection of regularization & class instance datasets of tigers for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
"TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n",
"# Tiger Regularization Images\n\nA collection of regularization & class instance datasets of tigers for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
55,
40
]
| [
"passage: TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n# Tiger Regularization Images\n\nA collection of regularization & class instance datasets of tigers for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
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|
c08583489ad46b3ffa6fcbc0c460a1d7ebf5a35a |
# Landscape Regularization Images
A collection of regularization & class instance datasets of landscapes for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | 3ee/regularization-landscape | [
"license:mit",
"stable-diffusion",
"regularization-images",
"text-to-image",
"image-to-image",
"dreambooth",
"class-instance",
"preservation-loss-training",
"region:us"
]
| 2023-01-20T19:40:14+00:00 | {"license": "mit", "tags": ["stable-diffusion", "regularization-images", "text-to-image", "image-to-image", "dreambooth", "class-instance", "preservation-loss-training"]} | 2023-01-20T19:48:58+00:00 | []
| []
| TAGS
#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us
|
# Landscape Regularization Images
A collection of regularization & class instance datasets of landscapes for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | [
"# Landscape Regularization Images\n\nA collection of regularization & class instance datasets of landscapes for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
"TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n",
"# Landscape Regularization Images\n\nA collection of regularization & class instance datasets of landscapes for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
55,
41
]
| [
"passage: TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n# Landscape Regularization Images\n\nA collection of regularization & class instance datasets of landscapes for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
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|
d3c72394c8d5997837c169b567dd4d87d56a19eb |
# Man Regularization Images
A collection of regularization & class instance datasets of men for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | 3ee/regularization-man | [
"license:mit",
"stable-diffusion",
"regularization-images",
"text-to-image",
"image-to-image",
"dreambooth",
"class-instance",
"preservation-loss-training",
"region:us"
]
| 2023-01-20T19:45:22+00:00 | {"license": "mit", "tags": ["stable-diffusion", "regularization-images", "text-to-image", "image-to-image", "dreambooth", "class-instance", "preservation-loss-training"]} | 2023-01-20T19:48:20+00:00 | []
| []
| TAGS
#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us
|
# Man Regularization Images
A collection of regularization & class instance datasets of men for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | [
"# Man Regularization Images\n\nA collection of regularization & class instance datasets of men for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
"TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n",
"# Man Regularization Images\n\nA collection of regularization & class instance datasets of men for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
55,
39
]
| [
"passage: TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n# Man Regularization Images\n\nA collection of regularization & class instance datasets of men for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
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|
7cdefd5bb0e34f067aa578fbd8aa324d36d6cbc2 |
# Woman Regularization Images
A collection of regularization & class instance datasets of women for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | 3ee/regularization-woman | [
"license:mit",
"stable-diffusion",
"regularization-images",
"text-to-image",
"image-to-image",
"dreambooth",
"class-instance",
"preservation-loss-training",
"region:us"
]
| 2023-01-20T19:49:37+00:00 | {"license": "mit", "tags": ["stable-diffusion", "regularization-images", "text-to-image", "image-to-image", "dreambooth", "class-instance", "preservation-loss-training"]} | 2023-01-20T19:52:45+00:00 | []
| []
| TAGS
#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us
|
# Woman Regularization Images
A collection of regularization & class instance datasets of women for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training. | [
"# Woman Regularization Images\n\nA collection of regularization & class instance datasets of women for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
"TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n",
"# Woman Regularization Images\n\nA collection of regularization & class instance datasets of women for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
55,
39
]
| [
"passage: TAGS\n#license-mit #stable-diffusion #regularization-images #text-to-image #image-to-image #dreambooth #class-instance #preservation-loss-training #region-us \n# Woman Regularization Images\n\nA collection of regularization & class instance datasets of women for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training."
]
| [
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-0.049658916890621185,
-0.01648857817053795
]
|
dd87cd421bee6a80552ac154be9b9737ad9ac23f |
# German Municipal Coat of Arms Dataset
This dataset contains 13104 samples for German municipal coat of arms.
Each sample consists of the following features: 'img', 'acceptance', 'municipality', 'description', 'id', historicalJustification', 'municipalityName', 'uri', 'figure', 'cancellation', 'cancellationReason', 'author' | johko/german_municipal_coat_of_arms | [
"size_categories:10K<n<100K",
"language:de",
"license:cc-by-4.0",
"region:us"
]
| 2023-01-20T20:50:31+00:00 | {"language": ["de"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"]} | 2023-01-22T19:41:39+00:00 | []
| [
"de"
]
| TAGS
#size_categories-10K<n<100K #language-German #license-cc-by-4.0 #region-us
|
# German Municipal Coat of Arms Dataset
This dataset contains 13104 samples for German municipal coat of arms.
Each sample consists of the following features: 'img', 'acceptance', 'municipality', 'description', 'id', historicalJustification', 'municipalityName', 'uri', 'figure', 'cancellation', 'cancellationReason', 'author' | [
"# German Municipal Coat of Arms Dataset\n\nThis dataset contains 13104 samples for German municipal coat of arms.\n\nEach sample consists of the following features: 'img', 'acceptance', 'municipality', 'description', 'id', historicalJustification', 'municipalityName', 'uri', 'figure', 'cancellation', 'cancellationReason', 'author'"
]
| [
"TAGS\n#size_categories-10K<n<100K #language-German #license-cc-by-4.0 #region-us \n",
"# German Municipal Coat of Arms Dataset\n\nThis dataset contains 13104 samples for German municipal coat of arms.\n\nEach sample consists of the following features: 'img', 'acceptance', 'municipality', 'description', 'id', historicalJustification', 'municipalityName', 'uri', 'figure', 'cancellation', 'cancellationReason', 'author'"
]
| [
31,
102
]
| [
"passage: TAGS\n#size_categories-10K<n<100K #language-German #license-cc-by-4.0 #region-us \n# German Municipal Coat of Arms Dataset\n\nThis dataset contains 13104 samples for German municipal coat of arms.\n\nEach sample consists of the following features: 'img', 'acceptance', 'municipality', 'description', 'id', historicalJustification', 'municipalityName', 'uri', 'figure', 'cancellation', 'cancellationReason', 'author'"
]
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|
d2337a07734fed19482342cd7b5a911472c4c007 | # Dataset Card for "processed_light_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | andrewnoel/processed_light_dataset | [
"region:us"
]
| 2023-01-20T22:52:19+00:00 | {"dataset_info": {"features": [{"name": "scene", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18625958.401499182, "num_examples": 7684}, {"name": "test", "num_bytes": 2070089.5985008199, "num_examples": 854}], "download_size": 11530553, "dataset_size": 20696048.0}} | 2023-01-23T04:54:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "processed_light_dataset"
More Information needed | [
"# Dataset Card for \"processed_light_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_light_dataset\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_light_dataset\"\n\nMore Information needed"
]
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|
e055e9f2de692a3d35c1495a7c153472b46ca7ca | # Dataset Card for "bookcorpus_compact_256_shard0_of_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_256_shard0_of_10 | [
"region:us"
]
| 2023-01-21T01:39:30+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 784542200, "num_examples": 238935}], "download_size": 393350476, "dataset_size": 784542200}} | 2023-01-21T01:39:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_compact_256_shard0_of_10"
More Information needed | [
"# Dataset Card for \"bookcorpus_compact_256_shard0_of_10\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_compact_256_shard0_of_10\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_compact_256_shard0_of_10\"\n\nMore Information needed"
]
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|
2fd777f220307d63db62610e51820a630107faa2 | # AutoTrain Dataset for project: 230121_t5_lcw99
## Dataset Description
This dataset has been automatically processed by AutoTrain for project 230121_t5_lcw99.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "\u5510\uc774 \uc815\ubc8c\uc744 \ud1b5\ud574 \u908a\u5916\uc5d0 \u7f88?\u5e9c\u5dde\u9ad4\u5236\ub97c \uc2dc\ud589\ud558\uae30 \uc2dc\uc791\ud55c \uac83\uc740 \ud0dc\uc885\ub9d0\uc758 \uc77c\uc774\uc9c0\ub9cc, \uace0\uc885\ub300\uc5d0\ub294 \uc774\ub97c \uc11c\ub3cc\uad90\uacfc \uace0\uad6c\ub824\uae4c\uc9c0 \ud655\ub300\uc2dc\ud0a4\uba74\uc11c \ucd5c\ub300 \ud310\ub3c4\uc758 \uc601\ud1a0\ub97c \ud655\ubcf4\ud558\uc600\ub2e4. \uace0\uc885\ub300 \u5510\u4f7f \ud30c\uacac\uc740 \uace0\uc885\uc989\uc704(650)\ub85c\ubd80\ud130 661\ub144\uae4c\uc9c0\ub294 \uc11c\ub3cc\uad90\uc5d0, 670\ub144\ubd80\ud130 \uace0\uc885\ub9d0\uae4c\uc9c0\ub294 \ud1a0\ubc88\uc5d0 \uc9d1\uc911\ub418\uc5c8\uace0, \ud30c\uacac \ud69f\uc218\ub3c4 \ub144 0.53\ud68c\ub85c \ubb34\ucc99 \uc801\ub2e4. \ubfd0\ub9cc \uc544\ub2c8\ub77c \uace0\uc885\ub300\uc758 \uc870\uacf5 \ud69f\uc218\ub3c4 \ud0dc\uc885\ub300\uc5d0 \ube44\ud574 \ud604\uaca9\ud788 \uc801\ub2e4. \uc774\ub294 \ub2f9\uc758 \uc8fc\ub41c \uc0ac\uc2e0 \uad50\ub958\uad6d(\ub3cc\uad90\uc744 \ube44\ub86f\ud55c \ucca0\ub975, \uc11c\ub3cc\uad90, \uace0\uad6c\ub824, \ubc31\uc81c \ub4f1)\uc774 \uc815\ubc8c\ub418\uc5c8\ub358 \uae4c\ub2ed\uc774\uae30\ub3c4 \ud558\uc9c0\ub9cc, \uc2e0\ub77c\ub098 \ud1a0\ubc88\uacfc\uc758 \uad00\uacc4\ub97c \ud1b5\ud574 \ubcfc \ub54c, \uc815\ubc8c\uacfc \uc9c0\ubc30\ub97c \ubaa9\ud45c\ub85c \ud55c \uace0\uc885\ub300 \ub300\uc678 \uc815\ucc45\uc758 \uc131\uaca9\uc5d0 \uae30\uc778\ud55c \uce21\uba74\uc774 \uac15\ud558\ub2e4\uace0 \ud558\uaca0\ub2e4. \uace0\uc885\ub300 \ub2f9\uc758 \uc0ac\uc2e0 \uc678\uad50\ub294 \uc8fc\ub85c \uc815\ubc8c \uc804 \u62db\u6170\uc640 \uc815\ubc8c \ud6c4 \u518a\u5c01\uc774\ub2e4. \ubc18\uba74\uc5d0 \ub2f9\uc758 \uf96f\u8aed\uac00 \uc720\ud6a8\ud558\uc9c0 \uc54a\uc558\ub358 \ud1a0\ubc88\uc5d0\ub294 \uc8fc\ub85c \uad70\uc0ac\uc801\uc73c\ub85c \ub300\uc751\ud558\uc600\uace0, \uc774\ub9c8\uc800\ub3c4 \uc5ec\uc758\uce58 \uc54a\uc740 \uc0c1\ud669\uc5d0\uc11c \ud1a0\ubc88\uc758 \uc694\uad6c\ub97c \uc218\uc6a9\u00b7\ud654\uce5c\ud558\uc600\ub2e4. \ub610\ud55c \ub2f9\uc758 \uc9c0\ubc30\ub97c \uac70\ubd80\ud55c \uc2e0\ub77c\uc640\uc758 \uc0ac\uc2e0 \uc655\ub798\ub294 \uac70\uc758 \ub2e8\uc808\ub41c \uc0c1\ud669\uc774\uc5c8\uc73c\uba70, \uc77c\ubcf8\uc5d0 \ud30c\uacac\ub41c \ub450 \ucc28\ub840\uc758 \uc0ac\uc2e0 \uc5ed\uc2dc \uc77c\ubcf8\uacfc\uc758 \uad6d\uad50\uac00 \ubaa9\uc801\uc774 \uc544\ub2c8\ub77c \uc2e0\ub77c\ub97c \uacac\uc81c\ud558\ub294 \uce21\uba74\uc774 \uac15\ud558\ub2e4. \uc989 \uace0\uc885\ub300 \ub300\uc678\uc815\ucc45\uc740 \uc8fc\ub85c \uad70\uc0ac \uc815\ubc8c\uacfc \uae30\ubbf8\ubd80\uc8fc\uccb4\uc81c\uc758 \ud655\ub300\uc600\uace0, \uc774 \uacfc\uc815\uc5d0\uc11c \uc0ac\uc2e0\uc678\uad50\uc758 \ube44\uc911\uc740 \uadf8\ub2e4\uc9c0 \ud06c\uc9c0 \uc54a\uc558\ub2e4. \uadf8\ub807\ub2e4\uba74 \uc65c \uace0\uc885\ub300\uc5d0\ub294 \uc0ac\uc2e0 \uc678\uad50\uc758 \ube44\uc911\uc774 \ub0ae\uc558\uace0 \ub610 \ud1a0\ubc88\uacfc \uac19\uc740 \uac15\ub825\ud55c \uad6d\uac00\uc5d0 \ub300\ud574\uc11c\ub294 \ud6a8\uacfc\uac00 \uc5c6\uc5c8\uc744\uae4c. \uc5ec\uae30\uc5d0\uc11c \ud55c\uac00\uc9c0 \uc8fc\ubaa9\ud560 \uac83\uc740 \uace0\uc885\ub300 \ub300\uc678\uc815\ucc45\uc740 \ud0dc\uc885\ub300\uc5d0 \uad6c\ucd95\ub41c \uad6d\uc81c\uc801 \uc9c0\uc704\uc640 \ub300\uc678\uc801 \uc5ed\ub7c9\uc774 \uc0c1\uc2b9\uace1\uc120\uc744 \uadf8\ub9ac\ub294 \uc2dc\uc810\uc5d0 \ucd94\uc9c4\ub418\uc5c8\ub2e4\ub294 \uc810\uc774\ub2e4. \ub2e4\uc2dc \ub9d0\ud574 \ub2f9\uc740 \uad6d\uc81c \uc9c8\uc11c\uc758 \uc911\uc2ec\uc5d0\uc11c \uac01\uad6d\uc758 \uad70\uc8fc\ub97c \ucc45\ubd09\ud558\uba70 \uc804\ud1b5\uc801\uc778 \uc911\ud654\uc774\ub150\uc744 \uc2e4\ud604\uc2dc\ud0a4\uace0, \ub098\uc544\uac00 \uae30\ubbf8\uc9c0\ubc30\uccb4\uc81c\uc758 \uc2e4\uc2dc\ub85c \ucc45\ubd09 \ubcf4\ub2e4 \uac15\ub825\ud55c \ud1b5\uc81c\uac00 \uac00\ub2a5\ud574\uc84c\ub2e4. \uc774\ub807\uac8c \ub2f9\uc758 \uad6d\uc81c\uc801 \uc704\ub825\uc774 \ucee4\uc9c0\uba74\uc11c \ub354\ubd88\uc5b4 \ub300\uc678\uad00\uacc4\uc5d0\uc11c \uc911\ud654\uc801 \uc774\ub150 \uc5ed\uc2dc \ud06c\uac8c \uace0\uc870\ub418\uc5c8\uc74c\uc744 \uc9d0\uc791\ud560 \uc218 \uc788\ub2e4. \ub54c\ubb38\uc5d0 \uace0\uc885\ub300\uc5d0\ub294 \uac15\uc131\ud55c \ud1a0\ubc88\uc758 \uacf5\uc138\uc640 \uc694\uad6c\uc5d0 \uc5ec\uc804\ud788 \ud6a8\uacfc \uc5c6\ub294 \u2018\ucc9c\uc790\uc758 \uf96f\u8aed\u2019\ub97c \ubc18\ubcf5\ud55c \uac83\uc774\ub2e4. \ub354 \ub098\uc544\uac00 \uc774\ubbf8 \uad11\ubc94\uc704\ud55c \uc774\ubbfc\uc871 \uc9c0\uc5ed\uc5d0 \ub2f9\uc758 \uc9c0\ubc30\uccb4\uc81c\ub97c \uc2e4\ud604\ud55c \uace0\uc885\ub300\uc5d0 \ucc45\ubd09-\uc870\uacf5\uc758 \uad00\uacc4\ub97c \uc804\uc81c\ub85c \ud558\ub294 \uc0ac\uc2e0 \uc678\uad50\ub294 \uadf8 \ube44\uc911\uc774 \ub0ae\uc544\uc9c0\uace0 \uacbd\uc9c1\ub418\uc5c8\ub2e4\uace0 \ubcf4\uc778\ub2e4. \uadf8\ub807\uae30\uc5d0 \ud1a0\ubc88\uc5d0 \ub300\ud574 \uc804\uc7c1 \uc774\uc678\uc758 \uc720\ud6a8\ud55c \uc678\uad50 \ub300\ucc45\uc744 \ub9c8\ub828\ud558\uc9c0 \ubabb\ud55c \uac83\uc73c\ub85c \ubcf4\uc778\ub2e4. \uc774\ub7ec\ud55c \uace0\uc885\ub300\uc758 \ub300\uc678 \uc815\ucc45\uc740 \ud1a0\ubc88\uc758 \uacf5\uc138\uc640 \uc11c\ub3cc\uad90\uc758 \u53cd\u5510\uc73c\ub85c \ub3d9\uc694\ud558\uae30 \uc2dc\uc791\ud558\uc600\ub2e4. \uc774\ubbf8 \uc2e0\ub77c\ub294 \ub2f9\uc758 \uc9c0\ubc30\ub97c \uac70\ubd80\ud55c \uc0c1\ud669\uc774\uc5c8\uace0, \uace0\uc885\ub9d0\uc5d0 \ubd81\ubc29 \ub3cc\uad90 \uc5ed\uc2dc \ub2f9\uc758 \uc9c0\ubc30\uccb4\uc81c\uc5d0 \uaca9\ub82c\ud558\uac8c \uc800\ud56d\ud558\uc600\ub2e4. \uc774\ub294 \ub2f9\uc758 \uad70\uc0ac\ub825\uc774 \uad11\ubc94\uc704\ud558\uac8c \ud655\uc7a5\ub41c \uae30\ubbf8\ubd80\uc8fc\ub97c \uac10\ub2f9\ud558\uae30\uc5d0 \uc5ed\ubd80\uc871\uc774\uba70 \uae30\ubbf8\ubd80\uc8fc\uccb4\uc81c\ub97c \ud1b5\ud55c \uc9c0\ubc30\ub77c\ub294 \ub300\uc678 \uc815\ucc45\uc5d0 \ud55c\uacc4\uac00 \ub4dc\ub7ec\ub0ac\uc74c\uc744 \ubcf4\uc5ec\uc900\ub2e4. \uadf8\ub7fc\uc5d0\ub3c4 \ubd88\uad6c\ud558\uace0 \ub2f9\uc740 678\ub144(\u5100\u9cf3 3)\uc5d0 \ud669\uc81c \uace0\uc885\uc758 \uc8fc\ub3c4\ud558\uc5d0 \uc2e0\ub77c\ub97c \ud1a0\ubc8c\ud558\ub824 \ud558\uace0, 679\ub144(\u8abf\u9732\u5143\u5e74) 2\uc6d4\uc5d0 \u8d0a\u666e\uc758 \uc8fd\uc74c\uc744 \uae30\ud68c\ub85c \ud1a0\ubc88\uc744 \ub3c4\ubaa8\ud558\uace0\uc790 \ud558\uc600\ub2e4. \uc774 \ubc16\uc5d0\ub3c4 \ub2f9\uc740 \ub3d9\uc694\ud558\ub294 \ub300\uc678\uc9c8\uc11c\uc5d0 \ubd84\uc8fc\ud558\uac8c \ud1a0\ubc8c\uad70\uc744 \ud30c\uacac\ud558\uc5ec \ub2f9\uc758 \uc9c0\ubc30\uc9c8\uc11c\ub97c \uc9c0\uc18d\uc2dc\ud0a4\uace0\uc790 \ud558\uc600\ub2e4. \uc774\ub85c\uc368 \ubcfc \ub54c, \uace0\uc885\ub300 \ub2f9\uc758 \ub300\uc678\uc815\ucc45\uc740 \ub2e4\uc591\ud558\uace0 \uc735\ud1b5\uc131 \uc788\ub294 \uc0ac\uc2e0\uc678\uad50\uc5d0 \ubb34\ucc99 \uc18c\uadf9\uc801\uc778 \ubc18\uba74, \uc815\ubc8c\uacfc \uc9c0\ubc30\uc758 \ud33d\ucc3d \uc815\ucc45\uc774 \uadf8 \ud55c\uacc4\uc5d0\ub3c4 \ubd88\uad6c\ud558\uace0 \uace0\uc885\ub9d0\uae4c\uc9c0 \uc77c\uad00\ub418\uac8c \ucd94\uc9c4\ub418\uc5c8\uc74c\uc744 \uc54c \uc218 \uc788\ub2e4.",
"target": "\u5510\uc774 \uc815\ubc8c\uc744 \ud1b5\ud574 \u908a\u5916\uc5d0 \u7f88?\u5e9c\u5dde\u9ad4\u5236\ub97c \uc2dc\ud589\ud558\uae30 \uc2dc\uc791\ud55c \uac83\uc740 \ud0dc\uc885\ub9d0\uc758 \uc77c\uc774\uc9c0\ub9cc, \uace0\uc885\ub300\uc5d0\ub294 \uc774\ub97c \uc11c\ub3cc\uad90\uacfc \uace0\uad6c\ub824\uae4c\uc9c0 \ud655\ub300\uc2dc\ud0a4\uba74\uc11c \ucd5c\ub300 \ud310\ub3c4\uc758 \uc601\ud1a0\ub97c \ud655\ubcf4\ud588\ub2e4. \uace0\uc885\ub300 \u5510\u4f7f \ud30c\uacac\uc740 \uace0\uc885\uc989\uc704\ub85c\ubd80\ud130 661\ub144\uae4c\uc9c0\ub294 \uc11c\ub3cc\uad90\uc5d0, 670\ub144\ubd80\ud130 \uace0\uc885\ub9d0\uae4c\uc9c0\ub294 \ud1a0\ubc88\uc5d0 \uc9d1\uc911\ub418\uc5c8\uace0, \ud30c\uacac \ud69f\uc218\ub3c4 \ub144 0.53\ud68c\ub85c \ubb34\ucc99 \uc801\ub2e4.",
"feat_section_original": "630\ub144(\uc815\uad00 4)\uc5d0 \ub2f9 \ud0dc\uc885\uc774 \ub3cc\uad90\uc744 \uc815\ubc8c\ud558\uba74\uc11c \ub2f9\uc758 \uad6d\uc81c\uc801 \uc704\ub9dd\uc740 \uae09\uc18d\ud788 \ub192\uc544\uc84c\ub2e4. \uc774\ud6c4\ub85c \ub2f9\uc740 \ubd84\uc5f4\ub41c \uc11c\ub3cc\uad90\uc5d0 \ucc45\ubd09\uc744 \ud1b5\ud574 \uce5c\ub2f9 \uc815\uad8c\uc744 \uad6c\ucd95\ud558\uace0, \uc774\uac83\uc774 \uc5ec\uc758\uce58 \uc54a\uc740 \ud1a0\uc695\ud63c\uc5d0\ub294 \uad70\uc0ac\ub97c \ub3d9\uc6d0\ud55c \uc704\ubb34\ub85c \uce5c\ub2f9\uc801 \ucc45\ubd09\uc744 \uc2e4\ud604\uc2dc\ucf30\ub2e4. \uadf8\ub7ec\ub098 \ub2f9\uc740 640\ub144(\u8c9e\u89c0 14)\uc5d0 \uace0\ucc3d\uc744 \uc815\ubc8c\ud558\uc5ec \u897f\u5dde\ub85c \ud3b8\uc81c\ud558\uc600\uace0, 646\ub144(\u8c9e\u89c0 20)\uc5d0 \uc124\uc5f0\ud0c0\ub97c \uc815\ubc8c\ud558\uc5ec \ucca0\ub975\uc744 6\ubd807\uc8fc\ub85c \ud3b8\uc81c\ud558\uc600\ub2e4. \uc989 \ub2f9\uc758 \ub300\uc678\uc815\ucc45\uc740 \ubcf4\ub2e4 \uc801\uadf9\uc801\uc774\uace0 \uc9c1\uc811\uc801\uc778\uc9c0\ubc30\ub85c \uc804\ud658\ub418\uace0 \uc788\uc5c8\ub2e4. \ud0dc\uc885\uc744 \uacc4\uc704\ud55c \uace0\uc885(650-683)\ub300\uc5d0, \ub2f9\uc740 \ucc9c\uc0b0\ubd81\ub85c\uc758 \uc11c\ub3cc\uad90\ub85c\ubd80\ud130 \uc694\ub3d9\uc758 \uace0\uad6c\ub824\uae4c\uc9c0 \ucd5c\ub300 \ud310\ub3c4\uc758 \uc601\ud1a0\ub97c \ud655\ubcf4\ud558\uc600\uc73c\uba70, \uc774\ub97c \uae30\ubbf8\ubd80\uc8fc\ub85c \ud3b8\uc81c\ud558\uace0 \ub3c4\ud638\ubd80\ub97c \ub450\uc5b4 \ucd1d\uad04\ud558\uc600\ub2e4. \uc774\ub7ec\ud55c \ub300\uc678 \uc815\ubc8c\uacfc \ud655\uc7a5, \ud3b8\uc81c\uc640 \uc9c0\ubc30\ub97c \ubaa9\ud45c\ub85c \ud55c \ub300\uc678\uc815\ucc45\uc740 \ud0dc\uc885 \ub9d0 \ub300\uc678 \uc815\ucc45\uc758 \uc5f0\uc7a5\uc774\ub77c \ud560 \uc218 \uc788\ub2e4. \ud558\uc9c0\ub9cc \ub300\uc678\uc815\ucc45\uc740 \uad70\uc0ac\ub825\uc744 \uc55e\uc138\uc6b4 \uc804\uc7c1\ub9cc\uc774 \uc544\ub2c8\ub77c \uc0ac\uc2e0\uc678\uad50\ub97c \ud1b5\ud574\uc11c\ub3c4 \uc0b4\ud3b4\ubcfc \uc218 \uc788\ub2e4. \u4f7f\u81e3\uc740 \uc678\uad50\uc758 \uc2e4\uc9c8\uc801 \uc218\ud589\uc790\ub85c, \uc774\ub97c \ub458\ub7ec\uc2fc \ub2e4\uc591\ud55c \uc694\uc18c\ub4e4(\ud30c\uacac \ud69f\uc218, \ud30c\uacac\ub300\uc0c1\uad6d, \ud30c\uacac\uc2dc\uae30, \ud30c\uacac \ubaa9\uc801\uacfc \uc784\ubb34 \ub4f1)\uc740 \uad6d\uc81c\uc801 \uc5ed\ud559\uad00\uacc4 \uc18d\uc5d0\uc11c \uac01 \uc2dc\uae30 \ub2f9\uc758 \ub300\uc678 \uad00\uacc4\uc640 \uc815\ucc45\uc774 \uc5b4\ub5bb\uac8c \ucd94\uc9c4\ub418\uace0 \uc5b4\ub290 \uc815\ub3c4 \uc2e4\ud604\ub418\uc5c8\ub294\uac00\ub97c \ubcf4\uc5ec\uc8fc\ub294 \uc911\uc694\ud55c \ub2e8\uc11c\uc774\ub2e4. \ubfd0\ub9cc \uc544\ub2c8\ub77c \uc774\ub294 \ub2f9\uc758 \uc774\ub150\uc801\uc774\uace0 \ud604\uc2e4\uc801\uc778 \uc785\uc7a5\uacfc \uc694\uad6c\ub97c \ud30c\uc545\ud560 \uc218 \uc788\ub294 \uc694\uc18c\uc774\uae30\ub3c4 \ud558\ub2e4.",
"feat_section_summary": "630\ub144\uc5d0 \ub2f9 \ud0dc\uc885\uc774 \ub3cc\uad90\uc744 \uc815\ubc8c\ud558\uba74\uc11c \ub2f9\uc758 \uad6d\uc81c\uc801 \uc704\ub9dd\uc740 \uae09\uc18d\ud788 \ub192\uc544\uc84c\ub2e4. \uc774\ud6c4\ub85c \ub2f9\uc740 \ubd84\uc5f4\ub41c \uc11c\ub3cc\uad90\uc5d0 \ucc45\ubd09\uc744 \ud1b5\ud574 \uce5c\ub2f9 \uc815\uad8c\uc744 \uad6c\ucd95\ud558\uace0, \uc774\uac83\uc774 \uc5ec\uc758\uce58 \uc54a\uc740 \ud1a0\uc695\ud63c\uc5d0\ub294 \uad70\uc0ac\ub97c \ub3d9\uc6d0\ud55c \uc704\ubb34\ub85c \uce5c\ub2f9\uc801 \ucc45\ubd09\uc744 \uc2e4\ud604\uc2dc\ucf30\ub2e4. \ub2f9\uc758 \ub300\uc678\uc815\ucc45\uc740 \ubcf4\ub2e4 \uc801\uadf9\uc801\uc774\uace0 \uc9c1\uc811\uc801\uc778 \uc9c0\ubc30\ub85c \uc804\ud658\ub418\uace0 \uc788\uc5c8\ub2e4."
},
{
"text": "\u300e\uc2e4\ucc9c\uc774\uc131\ube44\ud310\u300f\uc5d0\uc11c \ub3c4\ub355 \ubc95\uce59\uc5d0 \ub300\ud55c \uc874\uacbd\uc774 \uc720\uc77c\ud55c \ub3c4\ub355\uc801 \ub3d9\uae30\uc774\ub77c\uba74, \u300e\ub17c\uc5b4\u300f\uc5d0\uc11c\ub294 \uc778(\u4ec1)\uc758 \ub3c4\ub355 \ubc95\uce59\uc5d0 \ub300\ud55c \uacbd(\u656c)\uc774 \uadf8\ub7ec\ud558\ub2e4. \ub3c4\ub355\uc801\uc778 \ub3d9\uae30\uc640 \uad00\uc2ec \uadf8\ub9ac\uace0 \uc900\uce59 \uac1c\ub150\uc774 \u300e\uc2e4\ucc9c\uc774\uc131\ube44\ud310\u300f\uc5d0\uc11c \uc120\uc758\uc9c0\uac00 \uc870\ud0c1\ub418\ub294 \uacfc\uc815\uc744 \ubc1d\ud788\uace0 \uc788\ub2e4\uba74, \uc801\ubc95\uc131\uacfc \ub3c4\ub355\uc131 \uac1c\ub150\uc740 \uc120\uc758\uc9c0\uac00 \ud589\uc704\ub97c \ud1b5\ud574 \uc62c\ubc14\ub85c \ud589\ud574\uc84c\ub294\uc9c0\ub97c \uac80\uc99d\ud558\ub294 \uacfc\uc815\uc744 \ubc1d\ud788\uace0 \uc788\ub2e4. \uc774\uac83\uc740 \ud589\uc704\uc5d0 \ub300\ud55c \ub3c4\ub355\uc801 \uac00\uce58\ub97c \uac80\uc99d\ud558\ub294 \uac83\uc774\uae30\ub3c4 \ud558\ub2e4. \uc774 \uac80\uc99d \uacfc\uc815\uc5d0\uc11c \uc120\uc758\uc9c0\uc758 \uc870\ud0c1 \uacfc\uc815\uc5d0\uc11c \uadfc\ubcf8\uc801\uc73c\ub85c \uacb0\uc815\uc801\uc774\uc5c8\ub358 \u201c\ub3c4\ub355 \ubc95\uce59\uc774 \uc758\uc9c0\ub97c \uc9c1\uc811\uc801\uc73c\ub85c \uaddc\uc815\u201d\ud558\ub294 \uac83\uc740 \ub9c8\ucc2c\uac00\uc9c0\ub85c \u201c\ud589\uc704\ub4e4\uc758 \ubaa8\ub4e0 \ub3c4\ub355\uc801 \uac00\uce58\uc758 \ubcf8\uc9c8\u201d\uc744 \uc774\ub8ec\ub2e4. \ubcf8 \ub17c\ubb38\uc758 \uc758\ub3c4\ub294\u300e\uc2e4\ucc9c\uc774\uc131\ube44\ud310\u300f\uc758 \uc801\ubc95\uc131\uacfc \ub3c4\ub355\uc131 \uac1c\ub150 \ubc0f \uad6c\uc870\ub97c\u300e\ub17c\uc5b4\u300f\uc5d0 \ud22c\uc601\ud558\uc5ec\u300e\ub17c\uc5b4\u300f\uc5d0\uc11c \uc790\uccb4\uc758 \ub17c\ub9ac\ub97c \uac00\uc9c0\uace0 \uc791\ub3d9\ud558\uace0 \uc788\ub294 \uc801\ubc95\uc131\uacfc \ub3c4\ub355\uc131\uc758 \uac80\uc99d \uc2dc\uc2a4\ud15c\uc744 \ubc1d\ud600\ub0b4\ub294 \ub370 \uc788\ub2e4.",
"target": "\uc774 \ub17c\ubb38\uc758 \uc758\ub3c4\ub294\u300e\uc2e4\ucc9c\uc774\uc131\ube44\ud310\u300f\uc758 \uc801\ubc95\uc131\uacfc \ub3c4\ub355\uc131 \uac1c\ub150 \ubc0f \uad6c\uc870\ub97c\u300e\ub17c\uc5b4\u300f\uc5d0 \ud22c\uc601\ud558\uc5ec\u300e\ub17c\uc5b4\u300f\uc5d0\uc11c \uc790\uccb4\uc758 \ub17c\ub9ac\ub97c \uac00\uc9c0\uace0 \uc791\ub3d9\ud558\uace0 \uc788\ub294 \uc801\ubc95\uc131\uacfc \ub3c4\ub355\uc131\uc758 \uac80\uc99d \uc2dc\uc2a4\ud15c\uc744 \ubc1d\ud600\ub0b4\ub294 \ub370 \uc788\ub2e4.",
"feat_section_original": "\ubcf8 \uc5f0\uad6c\uc758 \uad81\uadf9\uc801\uc778 \ubaa9\uc801\uc740 \u300e\uc2e4\ucc9c\uc774\uc131\ube44\ud310\u300f\uc758 \uc120\uc758\uc9c0 \uac1c\ub150\uacfc \uad6c\uc870\ub97c \u300e\ub17c\uc5b4\u300f\uc758 \uc778(\u4ec1)\uc758\uc9c0 \uac1c\ub150\uacfc \uad6c\uc870 \ubd84\uc11d\uc5d0 \ud22c\uc601\ud558\uc5ec, \u300e\ub17c\uc5b4\u300f\uc5d0\uc11c \uc778(\u4ec1)\uc758\uc9c0\uac00 \uc870\ud0c1(\u5f6b\u596a)\ub418\ub294 \uacfc\uc815\uc744 \uc5c4\uaca9\ud558\uac8c \ubd84\uc11d\ud568\uc73c\ub85c\uc368 \u300e\ub17c\uc5b4\u300f\uc5d0\uc11c \u201c\uadfc\uc6d0\uc801\uc73c\ub85c \ubc95\uce59 \uc218\ub9bd\uc801\u201d\uc778 \ub3c4\ub355 \ubc95\uce59\uc758 \uc8fc\uccb4\uc758 \uadfc\uac70\ub97c \ub17c\uc99d\uc801\uc73c\ub85c \ub4dc\ub7ec\ub0b4\ub294 \ub370 \uc788\ub2e4. \uc774\ub97c \uc704\ud574\uc11c\ub294 \ubb34\uc5c7\ubcf4\ub2e4\ub3c4 \u300e\uc2e4\ucc9c\uc774\uc131\ube44\ud310\u300f\uc758 \uc120\uc758\uc9c0 \uac1c\ub150\uacfc \uad6c\uc870\ub97c \u300e\ub17c\uc5b4\u300f\uc758 \uc778(\u4ec1)\uc758\uc9c0 \uac1c\ub150\uc744 \ubd84\uc11d\ud558\ub294 \ub370\uc5d0 \uc798 \ud22c\uc601\ud558\ub294 \uc5f0\uad6c \ubc29\ubc95\uc774 \uccab \ubc88\uc9f8\ub85c \uc911\uc694 \ud560 \uac83\uc774\uace0, \u300e\uc2e4\ucc9c\uc774\uc131\ube44\ud310\u300f\uc758 \ub17c\ub9ac\ub85c \u300e\ub17c\uc5b4\u300f\uc758 \ub0b4\uc6a9\uc744 \ub2e8\uc21c\ud788 \ud574\ubd80\ud558\ub294 \uac83 \uc774 \uc544\ub2c8\ub77c, \u300e\ub17c\uc5b4\u300f\uc5d0 \uc790\uccb4\uc801\uc73c\ub85c \uac16\ucdb0\uc9c4 \ucda9\ubd84\ud55c \uadfc\uac70\ub97c \u300e\ub17c\uc5b4\u300f\uc758 \uc790\uccb4\uc801\uc778 \ub17c\ub9ac\uc5d0 \ub9de\uac8c \uc798 \ubc1d\ud600\ub0b4\uc5b4\uc11c \uc5c4\uaca9\ud558\uac8c \ub17c\uc99d\uc801\uc73c\ub85c \uc81c\uc2dc\ud560 \uc218 \uc788\ub294 \uc5f0\uad6c\ubc29\ubc95\uc774 \uccab \ubc88\uc9f8 \ubabb\uc9c0\uc54a\uac8c \ub450 \ubc88\uc9f8\ub85c \uc911\uc694\ud560 \uac83\uc774\ub2e4. \uadf8\ub798\uc11c \u300e\uc2e4\ucc9c\uc774\uc131\ube44\ud310\u300f\uc758 \uccb4\uacc4\uc640 \u300e\ub17c\uc5b4\u300f\uc758 \uccb4\uacc4\ub97c \uc11c\ub85c \uc11e\uc9c0 \uc54a\uc73c\uba74\uc11c\ub3c4 \uc758\ubbf8\uc801\uc73c\ub85c \uadf8\ub9ac\uace0 \uad6c\uc870\uc801\uc73c\ub85c \uc11c\ub85c \uc0c1\uad00\ub41c \uac1c\ub150\ub4e4\uc744 \ud22c\uc601\ud574\ubcfc \uc218 \uc788\ub3c4\ub85d \uadfc\uc811\uc2dc\ucf1c \uc11c\ub85c \uc5ee\uc5b4\uc11c \uc5f0\uad6c\ud558\ub294 \ubc29\ubc95\uc744 \uc2dc\ub3c4\ud558\ub824 \ud55c\ub2e4. \u300e\uc2e4\ucc9c\uc774\uc131\ube44\ud310\u300f\uc5d0\uc11c \uc120(\u5584)\uc758\uc9c0\uc5d0 \ub300\ud574\uc11c\ub294 \u300e\ub17c\uc5b4\u300f\uc5d0\uc11c \uc778(\u4ec1)\uc758\uc9c0\ub97c \uc0c1\uad00 \uac1c\ub150\uc73c\ub85c \ud558\uc600\uace0, \uc790\uae30 \uc0ac\ub791\uc758 \uc6d0\ub9ac\ub97c \uc887\uc74c\uc5d0 \ub300\ud574\uc11c\ub294 \u201c\uff3b\uc790\uc2e0\uc5d0\uac8c\uff3d\ud3b8\uc548\ud568(\u5b89\uff3b\u5df1\uff3d)\u201d\uc744, \u201c\uc720\uc77c\ud55c \uadf8\ub9ac\uace0 \ub3d9\uc2dc\uc5d0 \uc758\uc2ec\ud560 \ubc14 \uc5c6\ub294 \ub3c4\ub355\uc801 \ub3d9\uae30\u201d \ub85c\uc11c \u201c\ub3c4\ub355\ubc95\uce59\uc5d0 \ub300\ud55c \uc874\uacbd\u201d\uc5d0 \ub300\ud574\uc11c\ub294 \uc778 (\u4ec1)\uc758 \ub3c4\ub355 \ubc95\uce59\uc5d0 \ub300\ud55c \u201c\uacbd(\u656c)\u201d\uc744 \uc0c1\uad00 \uac1c\ub150\uc73c\ub85c \ud30c\uc545\ud55c\ub2e4.",
"feat_section_summary": "\ubcf8 \uc5f0\uad6c\uc758 \uad81\uadf9\uc801\uc778 \ubaa9\uc801\uc740 \u300e\uc2e4\ucc9c\uc774\uc131\ube44\ud310\u300f\uc758 \uc120\uc758\uc9c0 \uac1c\ub150\uacfc \uad6c\uc870\ub97c \u300e\ub17c\uc5b4\u300f\uc758 \uc778\uc758\uc9c0 \uac1c\ub150\uacfc \uad6c\uc870 \ubd84\uc11d\uc5d0 \ud22c\uc601\ud558\uc5ec, \u300e\ub17c\uc5b4\u300f\uc5d0\uc11c \uc778\uc758\uc9c0\uac00 \uc870\ud0c1\ub418\ub294 \uacfc\uc815\uc744 \uc5c4\uaca9\ud558\uac8c \ubd84\uc11d\ud568\uc73c\ub85c\uc368 \u300e\ub17c\uc5b4\u300f\uc5d0\uc11c \u201c\uadfc\uc6d0\uc801\uc73c\ub85c \ubc95\uce59 \uc218\ub9bd\uc801\u201d\uc778 \ub3c4\ub355 \ubc95\uce59\uc758 \uc8fc\uccb4\uc758 \uadfc\uac70\ub97c \ub17c\uc99d\uc801\uc73c\ub85c \ub4dc\ub7ec\ub0b4\ub294 \ub370 \uc788\ub2e4."
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)",
"feat_section_original": "Value(dtype='string', id=None)",
"feat_section_summary": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 2399 |
| valid | 600 |
| Maeji/autotrain-data-230121_t5_lcw99 | [
"task_categories:summarization",
"region:us"
]
| 2023-01-21T03:33:54+00:00 | {"task_categories": ["summarization"]} | 2023-01-21T04:19:10+00:00 | []
| []
| TAGS
#task_categories-summarization #region-us
| AutoTrain Dataset for project: 230121\_t5\_lcw99
================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project 230121\_t5\_lcw99.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-summarization #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
16,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-summarization #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
2a9c8c2c560bac31e7d65e81f584d2280ce7593d | # Dataset Card for "patched_1000_test_p_150_m2_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_1000_test_p_150_m2_embeddings | [
"region:us"
]
| 2023-01-21T07:29:33+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "features", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 9275601628, "num_examples": 1035692}], "download_size": 8812286870, "dataset_size": 9275601628}} | 2023-01-21T07:36:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_1000_test_p_150_m2_embeddings"
More Information needed | [
"# Dataset Card for \"patched_1000_test_p_150_m2_embeddings\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_1000_test_p_150_m2_embeddings\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_1000_test_p_150_m2_embeddings\"\n\nMore Information needed"
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1040277c7831711568a88aeb09ab09a198d30929 | # Dataset Card for "pii-pile-chunk3-50000-100000-tagged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | j-chim/pii-pile-chunk3-50000-100000-tagged | [
"region:us"
]
| 2023-01-21T08:09:21+00:00 | {"dataset_info": {"features": [{"name": "texts", "sequence": "string"}, {"name": "meta", "struct": [{"name": "pile_set_name", "dtype": "string"}]}, {"name": "scores", "sequence": "float64"}, {"name": "avg_score", "dtype": "float64"}, {"name": "num_sents", "dtype": "int64"}, {"name": "tagged_pii_results", "list": [{"name": "analysis_explanation", "dtype": "null"}, {"name": "end", "dtype": "int64"}, {"name": "entity_type", "dtype": "string"}, {"name": "recognition_metadata", "struct": [{"name": "recognizer_identifier", "dtype": "string"}, {"name": "recognizer_name", "dtype": "string"}]}, {"name": "score", "dtype": "float64"}, {"name": "start", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 519392635, "num_examples": 50000}], "download_size": 200427885, "dataset_size": 519392635}} | 2023-01-21T08:09:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pii-pile-chunk3-50000-100000-tagged"
More Information needed | [
"# Dataset Card for \"pii-pile-chunk3-50000-100000-tagged\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pii-pile-chunk3-50000-100000-tagged\"\n\nMore Information needed"
]
| [
6,
25
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pii-pile-chunk3-50000-100000-tagged\"\n\nMore Information needed"
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|
7564def708f898ee607c78005fb4e67fd1c198c0 | # Dataset Card for "private_common_voice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MohammedNasri/private_common_voice | [
"region:us"
]
| 2023-01-21T09:14:06+00:00 | {"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "input_length", "dtype": "float64"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 36964365264.0, "num_examples": 38481}, {"name": "test", "num_bytes": 10027864480, "num_examples": 10440}], "download_size": 6680862479, "dataset_size": 46992229744.0}} | 2023-01-21T09:21:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "private_common_voice"
More Information needed | [
"# Dataset Card for \"private_common_voice\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"private_common_voice\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"private_common_voice\"\n\nMore Information needed"
]
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|
db6e5361ae49622190a73e1a5bf32ec48360a2da |
# Dataset Card for "imdb_dutch"
## 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://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
Large Movie Review Dataset translated to Dutch.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets.
We provide a set of 24,992 highly polar movie reviews for training, and 24,992 for testing. There is additional unlabeled data for use as well.
### Translation to Dutch
The dataset was translated with [yhavinga/ul2-large-en-nl](https://huggingface.co/yhavinga/ul2-large-en-nl).
The translation code is available in the src directory.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
This dataset contains Dutch and English data.
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 108 MiB
- **Size of the generated dataset:** 277 MiB
An example of 'train' looks as follows.
```
{
"label": 0,
"text": "Holy shit. Dit was de slechtste film die ik in lange tijd heb gezien."
"text_en": "Holy crap. This was the worst film I have seen in a long time."
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `text`: a `string` feature.
- `text_en`: a `string` feature.
- `label`: a classification label, with possible values including `neg` (0), `pos` (1).
### Data Splits
| name |train|unsupervised|test |
|----------|----:|-----------:|----:|
|plain_text|24992| 49984|24992|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
```
### Contributions
Thanks to [@ghazi-f](https://github.com/ghazi-f), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding
the English `imdb` dataset.
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)
| yhavinga/imdb_dutch | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:nl",
"language:en",
"license:other",
"region:us"
]
| 2023-01-21T09:37:16+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["nl", "en"], "license": ["other"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "paperswithcode_id": "imdb-movie-reviews", "pretty_name": "IMDB", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "text_en", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 69589646, "num_examples": 24992}, {"name": "test", "num_bytes": 67958995, "num_examples": 24992}, {"name": "unsupervised", "num_bytes": 139649169, "num_examples": 49984}], "download_size": 108170940, "dataset_size": 277197810}, "train-eval-index": [{"config": "plain_text", "task": "text-classification", "task_id": "binary_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"text": "text", "label": "target"}, "metrics": [{"type": "accuracy"}, {"name": "Accuracy"}, {"type": "f1", "name": "F1 macro", "args": {"average": "macro"}}, {"type": "f1", "name": "F1 micro", "args": {"average": "micro"}}, {"type": "f1", "name": "F1 weighted", "args": {"average": "weighted"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}]} | 2023-01-21T10:57:39+00:00 | []
| [
"nl",
"en"
]
| TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Dutch #language-English #license-other #region-us
| Dataset Card for "imdb\_dutch"
==============================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository:
* Paper:
* Point of Contact:
### Dataset Summary
Large Movie Review Dataset translated to Dutch.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets.
We provide a set of 24,992 highly polar movie reviews for training, and 24,992 for testing. There is additional unlabeled data for use as well.
### Translation to Dutch
The dataset was translated with yhavinga/ul2-large-en-nl.
The translation code is available in the src directory.
### Supported Tasks and Leaderboards
### Languages
This dataset contains Dutch and English data.
Dataset Structure
-----------------
### Data Instances
#### plain\_text
* Size of downloaded dataset files: 108 MiB
* Size of the generated dataset: 277 MiB
An example of 'train' looks as follows.
### Data Fields
The data fields are the same among all splits.
#### plain\_text
* 'text': a 'string' feature.
* 'text\_en': a 'string' feature.
* 'label': a classification label, with possible values including 'neg' (0), 'pos' (1).
### Data Splits
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
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @ghazi-f, @patrickvonplaten, @lhoestq, @thomwolf for adding
the English 'imdb' dataset.
This project would not have been possible without compute generously provided by Google through the
TPU Research Cloud.
Created by Yeb Havinga
| [
"### Dataset Summary\n\n\nLarge Movie Review Dataset translated to Dutch.\n\n\nThis is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets.\nWe provide a set of 24,992 highly polar movie reviews for training, and 24,992 for testing. There is additional unlabeled data for use as well.",
"### Translation to Dutch\n\n\nThe dataset was translated with yhavinga/ul2-large-en-nl.\nThe translation code is available in the src directory.",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nThis dataset contains Dutch and English data.\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### plain\\_text\n\n\n* Size of downloaded dataset files: 108 MiB\n* Size of the generated dataset: 277 MiB\n\n\nAn example of 'train' looks as follows.",
"### Data Fields\n\n\nThe data fields are the same among all splits.",
"#### plain\\_text\n\n\n* 'text': a 'string' feature.\n* 'text\\_en': a 'string' feature.\n* 'label': a classification label, with possible values including 'neg' (0), 'pos' (1).",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### 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\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @ghazi-f, @patrickvonplaten, @lhoestq, @thomwolf for adding\nthe English 'imdb' dataset.\nThis project would not have been possible without compute generously provided by Google through the\nTPU Research Cloud.\n\n\nCreated by Yeb Havinga"
]
| [
"TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Dutch #language-English #license-other #region-us \n",
"### Dataset Summary\n\n\nLarge Movie Review Dataset translated to Dutch.\n\n\nThis is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets.\nWe provide a set of 24,992 highly polar movie reviews for training, and 24,992 for testing. There is additional unlabeled data for use as well.",
"### Translation to Dutch\n\n\nThe dataset was translated with yhavinga/ul2-large-en-nl.\nThe translation code is available in the src directory.",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nThis dataset contains Dutch and English data.\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### plain\\_text\n\n\n* Size of downloaded dataset files: 108 MiB\n* Size of the generated dataset: 277 MiB\n\n\nAn example of 'train' looks as follows.",
"### Data Fields\n\n\nThe data fields are the same among all splits.",
"#### plain\\_text\n\n\n* 'text': a 'string' feature.\n* 'text\\_en': a 'string' feature.\n* 'label': a classification label, with possible values including 'neg' (0), 'pos' (1).",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### 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\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @ghazi-f, @patrickvonplaten, @lhoestq, @thomwolf for adding\nthe English 'imdb' dataset.\nThis project would not have been possible without compute generously provided by Google through the\nTPU Research Cloud.\n\n\nCreated by Yeb Havinga"
]
| [
95,
78,
38,
10,
21,
6,
43,
17,
56,
11,
7,
4,
10,
10,
5,
5,
9,
18,
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| [
"passage: TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-Dutch #language-English #license-other #region-us \n### Dataset Summary\n\n\nLarge Movie Review Dataset translated to Dutch.\n\n\nThis is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets.\nWe provide a set of 24,992 highly polar movie reviews for training, and 24,992 for testing. There is additional unlabeled data for use as well.### Translation to Dutch\n\n\nThe dataset was translated with yhavinga/ul2-large-en-nl.\nThe translation code is available in the src directory.### Supported Tasks and Leaderboards### Languages\n\n\nThis dataset contains Dutch and English data.\n\n\nDataset Structure\n-----------------### Data Instances#### plain\\_text\n\n\n* Size of downloaded dataset files: 108 MiB\n* Size of the generated dataset: 277 MiB\n\n\nAn example of 'train' looks as follows.### Data Fields\n\n\nThe data fields are the same among all splits.#### plain\\_text\n\n\n* 'text': a 'string' feature.\n* 'text\\_en': a 'string' feature.\n* 'label': a classification label, with possible values including 'neg' (0), 'pos' (1).### Data Splits\n\n\n\nDataset Creation\n----------------### 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\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information"
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|
9153a394ed5a5b4d3a9b8ec4e1a4322b8e47ddd4 | # Dataset Card for "bookcorpus_compact_1024_test"
6160 samples randomly sampled from the shard9 of Bookcorpus_compact_1024
```python
from datasets import load_dataset
from datasets import Dataset
corpus_name="xxx"
ds = load_dataset(corpus_name, split="train")
shuffled_ds = ds.shuffle(seed=42)
test_ds = Dataset.from_dict{shuffled_ds[:6160]} # len(ds)//10
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_1024_test | [
"region:us"
]
| 2023-01-21T10:51:30+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 75334225, "num_examples": 6160}], "download_size": 38920916, "dataset_size": 75334225}} | 2023-01-22T23:37:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_compact_1024_test"
6160 samples randomly sampled from the shard9 of Bookcorpus_compact_1024
More Information needed | [
"# Dataset Card for \"bookcorpus_compact_1024_test\"\n\n\n6160 samples randomly sampled from the shard9 of Bookcorpus_compact_1024\n\n\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_compact_1024_test\"\n\n\n6160 samples randomly sampled from the shard9 of Bookcorpus_compact_1024\n\n\n\nMore Information needed"
]
| [
6,
44
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_compact_1024_test\"\n\n\n6160 samples randomly sampled from the shard9 of Bookcorpus_compact_1024\n\n\n\nMore Information needed"
]
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|
887026827822dfa3fe7258c414e6b04c5618fd09 | # Dataset Card for CSS10 Hungarian: Single Speaker Speech Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Hungarian Single Speaker Speech Dataset](https://www.kaggle.com/datasets/bryanpark/hungarian-single-speaker-speech-dataset)
- **Repository:** [CSS10](https://github.com/kyubyong/css10)
- **Paper:** [CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages](https://arxiv.org/abs/1903.11269)
### Dataset Summary
The corpus consists of a single speaker, with 4515 segments extracted
from a single LibriVox audiobook.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The audio is in Hungarian.
## Dataset Structure
[Needs More Information]
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
CSS10 is a collection of single speaker speech datasets for 10 languages. Each of them consists of audio files recorded by a single volunteer and their aligned text sourced from LibriVox.
### Source Data
#### Initial Data Collection and Normalization
[Egri csillagok](https://librivox.org/egri-csillagok-by-geza-gardonyi/),
read by Diana Majlinger.
#### 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
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Kyubyong Park & Tommy Mulc
### Licensing Information
[CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
```
@article{park2019css10,
title={CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages},
author={Park, Kyubyong and Mulc, Thomas},
journal={Interspeech},
year={2019}
}
```
### Contributions
[Needs More Information] | KTH/hungarian-single-speaker-tts | [
"task_categories:text-to-speech",
"task_categories:other",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:hu",
"license:cc0-1.0",
"arxiv:1903.11269",
"region:us"
]
| 2023-01-21T12:03:09+00:00 | {"annotations_creators": ["expert-generated"], "language": ["hu"], "license": "cc0-1.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-to-speech", "other"], "task_ids": [], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 22050}}}, {"name": "original_text", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "duration", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 3173032948.2, "num_examples": 4515}], "download_size": 0, "dataset_size": 3173032948.2}} | 2023-01-22T13:11:38+00:00 | [
"1903.11269"
]
| [
"hu"
]
| TAGS
#task_categories-text-to-speech #task_categories-other #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Hungarian #license-cc0-1.0 #arxiv-1903.11269 #region-us
| # Dataset Card for CSS10 Hungarian: Single Speaker Speech Dataset
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage: Hungarian Single Speaker Speech Dataset
- Repository: CSS10
- Paper: CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages
### Dataset Summary
The corpus consists of a single speaker, with 4515 segments extracted
from a single LibriVox audiobook.
### Supported Tasks and Leaderboards
### Languages
The audio is in Hungarian.
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
CSS10 is a collection of single speaker speech datasets for 10 languages. Each of them consists of audio files recorded by a single volunteer and their aligned text sourced from LibriVox.
### Source Data
#### Initial Data Collection and Normalization
Egri csillagok,
read by Diana Majlinger.
#### 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
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
Kyubyong Park & Tommy Mulc
### Licensing Information
CC0: Public Domain
### Contributions
| [
"# Dataset Card for CSS10 Hungarian: Single Speaker Speech Dataset",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: Hungarian Single Speaker Speech Dataset\n- Repository: CSS10\n- Paper: CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages",
"### Dataset Summary\n\nThe corpus consists of a single speaker, with 4515 segments extracted\nfrom a single LibriVox audiobook.",
"### Supported Tasks and Leaderboards",
"### Languages\n\nThe audio is in Hungarian.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale\n\nCSS10 is a collection of single speaker speech datasets for 10 languages. Each of them consists of audio files recorded by a single volunteer and their aligned text sourced from LibriVox.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nEgri csillagok,\nread by Diana Majlinger.",
"#### 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",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators\n\nKyubyong Park & Tommy Mulc",
"### Licensing Information\n\nCC0: Public Domain",
"### Contributions"
]
| [
"TAGS\n#task_categories-text-to-speech #task_categories-other #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Hungarian #license-cc0-1.0 #arxiv-1903.11269 #region-us \n",
"# Dataset Card for CSS10 Hungarian: Single Speaker Speech Dataset",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: Hungarian Single Speaker Speech Dataset\n- Repository: CSS10\n- Paper: CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages",
"### Dataset Summary\n\nThe corpus consists of a single speaker, with 4515 segments extracted\nfrom a single LibriVox audiobook.",
"### Supported Tasks and Leaderboards",
"### Languages\n\nThe audio is in Hungarian.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale\n\nCSS10 is a collection of single speaker speech datasets for 10 languages. Each of them consists of audio files recorded by a single volunteer and their aligned text sourced from LibriVox.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nEgri csillagok,\nread by Diana Majlinger.",
"#### 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",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators\n\nKyubyong Park & Tommy Mulc",
"### Licensing Information\n\nCC0: Public Domain",
"### Contributions"
]
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"passage: TAGS\n#task_categories-text-to-speech #task_categories-other #annotations_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Hungarian #license-cc0-1.0 #arxiv-1903.11269 #region-us \n# Dataset Card for CSS10 Hungarian: Single Speaker Speech Dataset## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: Hungarian Single Speaker Speech Dataset\n- Repository: CSS10\n- Paper: CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages### Dataset Summary\n\nThe corpus consists of a single speaker, with 4515 segments extracted\nfrom a single LibriVox audiobook.### Supported Tasks and Leaderboards### Languages\n\nThe audio is in Hungarian.## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale\n\nCSS10 is a collection of single speaker speech datasets for 10 languages. Each of them consists of audio files recorded by a single volunteer and their aligned text sourced from LibriVox.### Source Data#### Initial Data Collection and Normalization\n\nEgri csillagok,\nread by Diana Majlinger.#### 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### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators\n\nKyubyong Park & Tommy Mulc"
]
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|
fd28fdcc46ca1bdad8e16325cca653d3fb586906 | # Dataset Card for "patched_1000_test_p_150_m2_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_1000_test_p_150_m2_predictions | [
"region:us"
]
| 2023-01-21T12:13:40+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "features", "sequence": "float64"}, {"name": "m2_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 9279744396, "num_examples": 1035692}], "download_size": 8814051480, "dataset_size": 9279744396}} | 2023-01-21T12:20:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_1000_test_p_150_m2_predictions"
More Information needed | [
"# Dataset Card for \"patched_1000_test_p_150_m2_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_1000_test_p_150_m2_predictions\"\n\nMore Information needed"
]
| [
6,
27
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_1000_test_p_150_m2_predictions\"\n\nMore Information needed"
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|
ba1ece2c445f6ba6106eaad61108272e4e93b3ce |
Regularization images of several types.
CFG Scale: 7
Sampler: DDIM
Steps: 50
Size: see file name
Prompt: see file name
Neg. Prompt: none
#images: see file name
VAE: none
Lora: none
Model for 512x512: SD V1.5
Model for 768x768: SD V2.1
| dragonink/StableDiffusion-Regularization-Images | [
"license:mit",
"region:us"
]
| 2023-01-21T13:29:54+00:00 | {"license": "mit"} | 2023-01-22T13:43:56+00:00 | []
| []
| TAGS
#license-mit #region-us
|
Regularization images of several types.
CFG Scale: 7
Sampler: DDIM
Steps: 50
Size: see file name
Prompt: see file name
Neg. Prompt: none
#images: see file name
VAE: none
Lora: none
Model for 512x512: SD V1.5
Model for 768x768: SD V2.1
| []
| [
"TAGS\n#license-mit #region-us \n"
]
| [
11
]
| [
"passage: TAGS\n#license-mit #region-us \n"
]
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|
98946f840588c333e5d3a1f028c4d7eb4da74beb | ## Dataset audio info
- 16000 Hz 16 bit
- wav
- mono
- Russian speech
## Dataset instance structure
{'audio': {'path': '/path/to/wav.wav',
'array': array([wav numpy array]), dtype=float32),
'sampling_rate': 16000},
'transcription': 'транскрипция'}
## Citation
@Misc{Voxforge.org,
author = {Voxforge.org},
title = {Free Speech... Recognition (Linux, Windows and Mac) - voxforge.org},
howpublished = {\url{[http://www.voxforge.org/]}},
note = {accessed 01/21/2023}
}
## Source
http://www.voxforge.org/ru/downloads | dangrebenkin/voxforge-ru-dataset | [
"license:apache-2.0",
"region:us"
]
| 2023-01-21T14:34:23+00:00 | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "transcription", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 1947609729.4653895, "num_examples": 6169}, {"name": "test", "num_bytes": 864278563.4406104, "num_examples": 2645}], "download_size": 2705520657, "dataset_size": 2811888292.906}} | 2023-02-06T19:23:29+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| ## Dataset audio info
- 16000 Hz 16 bit
- wav
- mono
- Russian speech
## Dataset instance structure
{'audio': {'path': '/path/to/URL',
'array': array([wav numpy array]), dtype=float32),
'sampling_rate': 16000},
'transcription': 'транскрипция'}
@Misc{URL,
author = {URL},
title = {Free Speech... Recognition (Linux, Windows and Mac) - URL},
howpublished = {\url{[URL
note = {accessed 01/21/2023}
}
## Source
URL | [
"## Dataset audio info\n- 16000 Hz 16 bit\n- wav\n- mono\n- Russian speech",
"## Dataset instance structure\n{'audio': {'path': '/path/to/URL',\n 'array': array([wav numpy array]), dtype=float32),\n 'sampling_rate': 16000},\n 'transcription': 'транскрипция'}\n@Misc{URL,\n author = {URL},\n title = {Free Speech... Recognition (Linux, Windows and Mac) - URL},\n howpublished = {\\url{[URL\n note = {accessed 01/21/2023}\n}",
"## Source\nURL"
]
| [
"TAGS\n#license-apache-2.0 #region-us \n",
"## Dataset audio info\n- 16000 Hz 16 bit\n- wav\n- mono\n- Russian speech",
"## Dataset instance structure\n{'audio': {'path': '/path/to/URL',\n 'array': array([wav numpy array]), dtype=float32),\n 'sampling_rate': 16000},\n 'transcription': 'транскрипция'}\n@Misc{URL,\n author = {URL},\n title = {Free Speech... Recognition (Linux, Windows and Mac) - URL},\n howpublished = {\\url{[URL\n note = {accessed 01/21/2023}\n}",
"## Source\nURL"
]
| [
14,
19,
127,
3
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n## Dataset audio info\n- 16000 Hz 16 bit\n- wav\n- mono\n- Russian speech## Dataset instance structure\n{'audio': {'path': '/path/to/URL',\n 'array': array([wav numpy array]), dtype=float32),\n 'sampling_rate': 16000},\n 'transcription': 'транскрипция'}\n@Misc{URL,\n author = {URL},\n title = {Free Speech... Recognition (Linux, Windows and Mac) - URL},\n howpublished = {\\url{[URL\n note = {accessed 01/21/2023}\n}## Source\nURL"
]
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|
90243c00259e684300f4f03ef057848a8ee68f17 | Датасет ассоциаций к русским существительным | solkogan/russian_nouns_associations | [
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:ru",
"region:us"
]
| 2023-01-21T14:59:46+00:00 | {"language": ["ru"], "size_categories": ["100K<n<1M"], "task_categories": ["conversational"]} | 2023-01-21T15:12:42+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-conversational #size_categories-100K<n<1M #language-Russian #region-us
| Датасет ассоциаций к русским существительным | []
| [
"TAGS\n#task_categories-conversational #size_categories-100K<n<1M #language-Russian #region-us \n"
]
| [
33
]
| [
"passage: TAGS\n#task_categories-conversational #size_categories-100K<n<1M #language-Russian #region-us \n"
]
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|
18d9f1c1395ae061a34be544253d763b5fa39b71 | Датасет для классификации существительных русского языка | solkogan/nouns_classes | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:ru",
"region:us"
]
| 2023-01-21T15:17:48+00:00 | {"language": ["ru"], "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"]} | 2023-01-21T15:23:11+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-text-classification #size_categories-100K<n<1M #language-Russian #region-us
| Датасет для классификации существительных русского языка | []
| [
"TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-Russian #region-us \n"
]
| [
34
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-Russian #region-us \n"
]
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|
5275a96cea765dc3bbfd41e6034485791704ed69 | https://github.com/google-deepmind/logical-entailment-dataset
```
@inproceedings{
evans2018can,
title={Can Neural Networks Understand Logical Entailment?},
author={Richard Evans and David Saxton and David Amos and Pushmeet Kohli and Edward Grefenstette},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=SkZxCk-0Z},
}
```
| tasksource/logical-entailment | [
"license:apache-2.0",
"region:us"
]
| 2023-01-21T15:27:45+00:00 | {"license": "apache-2.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "E", "dtype": "int64"}, {"name": "H1", "dtype": "int64"}, {"name": "H2", "dtype": "int64"}, {"name": "H3", "dtype": "int64"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9803153, "num_examples": 99876}, {"name": "test", "num_bytes": 550241, "num_examples": 5000}, {"name": "validation", "num_bytes": 548346, "num_examples": 5000}], "download_size": 2505053, "dataset_size": 10901740}} | 2024-01-19T09:37:11+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| URL
| []
| [
"TAGS\n#license-apache-2.0 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n"
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]
|
254ac58b7ce1716a32f244b80e390359e201aadc |
# PVC figure products dataset
This dataset contains product information of figure images scraped from multiple Web sites.
## Dataset information
|Subset|Source|Size|
|-|-|-|
|`goodsmile-figma`|https://www.goodsmile.info/ja/products/category/figma/announced/2023|947|
|`goodsmile-nendoroid`|https://www.goodsmile.info/ja/products/category/nendoroid_series/announced/2023|3378|
|`goodsmile-scale`|https://www.goodsmile.info/ja/products/category/scale/announced/2023|2203|
|`kotobukiya`|https://www.kotobukiya.co.jp/en/product/figures/|864|
|`myethos`|http://www.myethos.cn/Collection|95|
|`spiritale`|https://spiritale.jp/shop/c/csallitem/|21|
|`tokyofigures`|https://tokyofigure.jp/products/list.php|394| | p1atdev/pvc | [
"size_categories:1K<n<10K",
"language:en",
"language:ja",
"license:cc0-1.0",
"region:us"
]
| 2023-01-21T16:12:04+00:00 | {"language": ["en", "ja"], "license": "cc0-1.0", "size_categories": ["1K<n<10K"], "dataset_info": [{"config_name": "goodsmile-figma", "features": [{"name": "id", "dtype": "string"}, {"name": "image_urls", "sequence": "string"}, {"name": "details", "struct": [{"name": "", "dtype": "string"}, {"name": "Bag Design Assistance", "dtype": "string"}, {"name": "Booklet Design", "dtype": "string"}, {"name": "CG Coloring", "dtype": "string"}, {"name": "Category", "dtype": "string"}, {"name": "Character Design/Illustration", "dtype": "string"}, {"name": "Cooperation", "dtype": "string"}, {"name": "Dengekiya Exclusive Product", "dtype": "string"}, {"name": "Design Cooperation", "dtype": "string"}, {"name": "Distributed by", "dtype": "string"}, {"name": "Distributor", "dtype": "string"}, {"name": "First Orders Release Date", "dtype": "string"}, {"name": "First Release Extra", "dtype": "string"}, {"name": "GOODSMILE RACING Personal Sponsor Bonus", "dtype": "string"}, {"name": "GOODSMILE Racing Personal Sponsor Bonus", "dtype": "string"}, {"name": "Good Smile Kuji Hatsune Miku 2014 Spring Ver. - B Prize", "dtype": "string"}, {"name": "Good Smile Racing 2017 Personal Sponsor Bonus", "dtype": "string"}, {"name": "Good Smile Racing Personal Sponsor Bonus", "dtype": "string"}, {"name": "Illustrated by", "dtype": "string"}, {"name": "Included with the 'Limited Edition Contract BOX'", "dtype": "string"}, {"name": "Included with the Fate/Extra CCC TYPE-MOON Virgin White Box", "dtype": "string"}, {"name": "Included with the Japanese 'GRAVITY DAZE Collector's Edition'.", "dtype": "string"}, {"name": "Included with the limited edition 37th volume of Berserk.", "dtype": "string"}, {"name": "LTD", "dtype": "string"}, {"name": "Limited Edition Extra", "dtype": "string"}, {"name": "Manufacturer", "dtype": "string"}, {"name": "Manufacturing Cooperation", "dtype": "string"}, {"name": "Model Data", "dtype": "string"}, {"name": "Originally released in March 2017 with a rerelease in June 2021.", "dtype": "string"}, {"name": "Originally released in May 2021 with a rerelease in July 2024.", "dtype": "string"}, {"name": "Outfit Design/Production", "dtype": "string"}, {"name": "Outfit/Pattern Design", "dtype": "string"}, {"name": "Painted ABS&PVC non-scale articulated figure with stand included. Approximately 165mm in height", "dtype": "string"}, {"name": "Painted ABS&PVC posable figure - not to scale - approximately 150mm in height", "dtype": "string"}, {"name": "Paintowork", "dtype": "string"}, {"name": "Paintwork", "dtype": "string"}, {"name": "Photography", "dtype": "string"}, {"name": "Photography Assistance", "dtype": "string"}, {"name": "Planning", "dtype": "string"}, {"name": "Planning Assistance", "dtype": "string"}, {"name": "Planning/Cooperation", "dtype": "string"}, {"name": "Planning/Production", "dtype": "string"}, {"name": "Planning/Production Assistance", "dtype": "string"}, {"name": "Planning/Production Assitance", "dtype": "string"}, {"name": "Price", "dtype": "string"}, {"name": "Product Name", "dtype": "string"}, {"name": "Production Cooperation", "dtype": "string"}, {"name": "Production/Distributed by", "dtype": "string"}, {"name": "Production/Production", "dtype": "string"}, {"name": "Production/Sculpting", "dtype": "string"}, {"name": "Purchase Info", "dtype": "string"}, {"name": "Redesign by IZMOJUKI / Design Cooperation", "dtype": "string"}, {"name": "Release Date", "dtype": "string"}, {"name": "Release Info", "dtype": "string"}, {"name": "Release/Manufacturing/Distribution", "dtype": "string"}, {"name": "Released by", "dtype": "string"}, {"name": "Released by/Production Cooperation", "dtype": "string"}, {"name": "Released in April 2012 with a rerelease in October 2012.", "dtype": "string"}, {"name": "Released/Distributed by", "dtype": "string"}, {"name": "Rerelease Info", "dtype": "string"}, {"name": "Resale", "dtype": "string"}, {"name": "Resale Info", "dtype": "string"}, {"name": "Sales", "dtype": "string"}, {"name": "Sales Info", "dtype": "string"}, {"name": "Sales/Manufacturing/Distribution", "dtype": "string"}, {"name": "Sculpting / Manufacturing", "dtype": "string"}, {"name": "Sculpting Cooperation", "dtype": "string"}, {"name": "Sculpting/Paintwork", "dtype": "string"}, {"name": "Sculpting/Production/Released by", "dtype": "string"}, {"name": "Sculpting/Released by", "dtype": "string"}, {"name": "Sculpting/Sold By", "dtype": "string"}, {"name": "Sculptor", "dtype": "string"}, {"name": "Sculptor/Paintwork", "dtype": "string"}, {"name": "Second Orders Release Date", "dtype": "string"}, {"name": "Series", "dtype": "string"}, {"name": "Set Contents", "dtype": "string"}, {"name": "Sold By", "dtype": "string"}, {"name": "Sold by", "dtype": "string"}, {"name": "Sold/Distributed by", "dtype": "string"}, {"name": "Sold/Released by", "dtype": "string"}, {"name": "Specifications", "dtype": "string"}, {"name": "Speicifications", "dtype": "string"}, {"name": "Summer Wonder Festival 2017 Product", "dtype": "string"}, {"name": "Summer Wonder Festival 2018 Product", "dtype": "string"}, {"name": "WONDERFUL HOBBY LIFE FOR YOU!!32 Product", "dtype": "string"}, {"name": "Winter Wonder Festival 2018 Product", "dtype": "string"}, {"name": "Wonder Festival 2011 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2011 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2012 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2012 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2013 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2013 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2014 (Summer) Web Sales Product", "dtype": "string"}, {"name": "Wonder Festival 2014 (Winter) Limited Edition Product", "dtype": "string"}, {"name": "Wonder Festival 2015 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2015 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2016 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2016 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2019 Summer Product", "dtype": "string"}, {"name": "Wonder Festival 2019 Winter Product", "dtype": "string"}, {"name": "Wonder Festival 2020 Winter Product", "dtype": "string"}, {"name": "Wonder Festival Summer 2009 Product", "dtype": "string"}, {"name": "ebten Product", "dtype": "string"}, {"name": "figma Production", "dtype": "string"}, {"name": "figma Specifications", "dtype": "string"}, {"name": "\u30ef\u30f3\u30c0\u30fc\u30d5\u30a7\u30b9\u30c6\u30a3\u30d0\u30eb 2012\uff3b\u590f\uff3d\u8ca9\u58f2\u5546\u54c1", "dtype": "string"}, {"name": "\u4f01\u5283\u88fd\u4f5c", "dtype": "string"}, {"name": "\u4f01\u753b\u30fb\u5236\u4f5c\u5354\u529b", "dtype": "string"}, {"name": "\u4fa1\u683c", "dtype": "string"}, {"name": "\u518d\u8ca9", "dtype": "string"}, {"name": "\u518d\u8ca9\u4fa1\u683c", "dtype": "string"}, {"name": "\u518d\u8ca9\uff1a\u518d\u51fa\u8377", "dtype": "string"}, {"name": "\u539f\u578b\u5236\u4f5c\u30fb\u767a\u58f2\u5143", "dtype": "string"}, {"name": "\u767a\u58f2\u30fb\u88fd\u9020\u30fb\u8ca9\u58f2\u5143", "dtype": "string"}, {"name": "\u8ca9\u58f2\u65b9\u6cd5", "dtype": "string"}]}, {"name": "title", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1447190, "num_examples": 947}], "download_size": 409156, "dataset_size": 1447190}, {"config_name": "goodsmile-nendoroid", "features": [{"name": "image_urls", "sequence": "string"}, {"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "details", "struct": [{"name": "", "dtype": "string"}, {"name": "*Event/GOODSMILE ONLINE SHOP Exclusive.", "dtype": "string"}, {"name": "2012 Release Price", "dtype": "string"}, {"name": "Accessories", "dtype": "string"}, {"name": "Ages", "dtype": "string"}, {"name": "Available From", "dtype": "string"}, {"name": "Batteries", "dtype": "string"}, {"name": "Bonus Parts", "dtype": "string"}, {"name": "Category", "dtype": "string"}, {"name": "Characters", "dtype": "string"}, {"name": "Chest - 47cm/M - Length - 65cm", "dtype": "string"}, {"name": "Chest - 49cm/L - Length - 69cm", "dtype": "string"}, {"name": "Chest - 52cm/XL - Length - 73cm", "dtype": "string"}, {"name": "Chest - 55cm", "dtype": "string"}, {"name": "Colouring Design", "dtype": "string"}, {"name": "Cooperation", "dtype": "string"}, {"name": "Costume/Pattern Planning", "dtype": "string"}, {"name": "Costume/Pattern Production", "dtype": "string"}, {"name": "Delivery will be in late October 2011.", "dtype": "string"}, {"name": "Design", "dtype": "string"}, {"name": "Design/Illust", "dtype": "string"}, {"name": "Disitributed by", "dtype": "string"}, {"name": "Distributed by", "dtype": "string"}, {"name": "Distributed/Released by", "dtype": "string"}, {"name": "Distributer", "dtype": "string"}, {"name": "Distribution", "dtype": "string"}, {"name": "Distributor", "dtype": "string"}, {"name": "Editing", "dtype": "string"}, {"name": "Event Exclusive Product", "dtype": "string"}, {"name": "Event Price", "dtype": "string"}, {"name": "Event Product / GSC Online Shop Product", "dtype": "string"}, {"name": "Event Sales Product", "dtype": "string"}, {"name": "Event/GSC Online Shop Product. (More details below)", "dtype": "string"}, {"name": "Exclusive to the Good Smile x Karaoke no Tetsujin Caf\u00e9 and GOOD SMILE ONLINE SHOP.", "dtype": "string"}, {"name": "Extras", "dtype": "string"}, {"name": "Figure", "dtype": "string"}, {"name": "Figure Specifications", "dtype": "string"}, {"name": "GOOD SMILE ONLINE SHOP Exclusive Product", "dtype": "string"}, {"name": "GOOD SMILE ONLINE SHOP Product", "dtype": "string"}, {"name": "GOODSMILE Racing Personal Sponsor Bonus", "dtype": "string"}, {"name": "GSC Lottery - Hatsune Miku 2012 Winter Ver. - A Prize", "dtype": "string"}, {"name": "GSC Lottery Hatsune Miku 2012 Winter Ver. - B Prize", "dtype": "string"}, {"name": "GSC Lottery Hatsune Miku 2012 Winter Ver. - C Prize", "dtype": "string"}, {"name": "GSC Lottery Hatsune Miku 2012 Winter Ver. - Last Draw Prize", "dtype": "string"}, {"name": "GSC Online Rerelease", "dtype": "string"}, {"name": "GSC Online Shop Rerelease", "dtype": "string"}, {"name": "Good Smile Kuji Hatsune Miku 2014 Spring Ver. - A Prize", "dtype": "string"}, {"name": "Good Smile Kuji Hatsune Miku 2014 Spring Ver. - LAST Prize", "dtype": "string"}, {"name": "Good Smile Racing 2017 Personal Sponsor Bonus", "dtype": "string"}, {"name": "Happy Kuji", "dtype": "string"}, {"name": "Happy Lots Miku Hatsune", "dtype": "string"}, {"name": "Included in the Bakemonogatari Premium Item BOX due for release on the 21st November 2013", "dtype": "string"}, {"name": "Included in the Limited Box of the PlayStation\u00ae4/PlayStation\u00ae3 Game 'BLAZBLUE CENTRALFICTION'", "dtype": "string"}, {"name": "Included with 'Space Brothers' Volume 27 on sale from the 20th November 2015.", "dtype": "string"}, {"name": "Included with the 'Saki Achiga-hen episode of side - A Blu-ray Limited First Edition Special BOX.", "dtype": "string"}, {"name": "Included with the Limited Edition 18th Volume 'Attack on Titan' Manga (Japanese Version)", "dtype": "string"}, {"name": "Included with the Limited Edition Yuru Yuri San Hai! 6th Volume Blu-ray", "dtype": "string"}, {"name": "Included with the Limited Edition of the Milky Holmes 2 PSP Game", "dtype": "string"}, {"name": "Included with the Limited First Edition of the 'PARTY TIME' Album.", "dtype": "string"}, {"name": "Included with the Monster Hunter Frontier G Five Million Hunters Memorial Goods", "dtype": "string"}, {"name": "Included with the Nisemonogatari Premium Item BOX", "dtype": "string"}, {"name": "Manufacturer", "dtype": "string"}, {"name": "Manufacturing", "dtype": "string"}, {"name": "Manufacturing Assistance", "dtype": "string"}, {"name": "Mini 4WD Specs", "dtype": "string"}, {"name": "Minimum Requirements", "dtype": "string"}, {"name": "Nendoroid Petite Specs", "dtype": "string"}, {"name": "Only 1000 Nendoroids will be available for winners of the 'Torarete! Hobby Channel' lot raws.", "dtype": "string"}, {"name": "Original Price", "dtype": "string"}, {"name": "Original release", "dtype": "string"}, {"name": "Originally released in April 2020 with a rerelease in November 2023.", "dtype": "string"}, {"name": "Originally released in February 2023 with a rerelease in May 2024.", "dtype": "string"}, {"name": "Originally released in May 2019 with a rerelease in July 2021.", "dtype": "string"}, {"name": "Outfit Design", "dtype": "string"}, {"name": "Outfit/Pattern Design", "dtype": "string"}, {"name": "Outfit/Pattern Planning", "dtype": "string"}, {"name": "Paintwork", "dtype": "string"}, {"name": "Paintwork Assistance", "dtype": "string"}, {"name": "Paintwork Cooperation", "dtype": "string"}, {"name": "Part of the Monster Hunter Frontier G 2014 Anniversary Premium Goods", "dtype": "string"}, {"name": "Photography", "dtype": "string"}, {"name": "Planning", "dtype": "string"}, {"name": "Planning Assistance", "dtype": "string"}, {"name": "Planning Cooperation", "dtype": "string"}, {"name": "Planning/Manufacturing", "dtype": "string"}, {"name": "Planning/Prodcution/Manufacturing Assistance", "dtype": "string"}, {"name": "Planning/Production", "dtype": "string"}, {"name": "Planning/Productions", "dtype": "string"}, {"name": "Planning/Prouction", "dtype": "string"}, {"name": "Planning/Sculpt", "dtype": "string"}, {"name": "Planning/Sculpting", "dtype": "string"}, {"name": "Platform", "dtype": "string"}, {"name": "Please Note", "dtype": "string"}, {"name": "Pose Concepts", "dtype": "string"}, {"name": "Price", "dtype": "string"}, {"name": "Produced and Released by", "dtype": "string"}, {"name": "Produced by", "dtype": "string"}, {"name": "Produced/Released by", "dtype": "string"}, {"name": "Product Name", "dtype": "string"}, {"name": "Production", "dtype": "string"}, {"name": "Production Assistance", "dtype": "string"}, {"name": "Production Assitance", "dtype": "string"}, {"name": "Production Cooperation", "dtype": "string"}, {"name": "Production/Released By", "dtype": "string"}, {"name": "Production/Released by", "dtype": "string"}, {"name": "Production/Sold By", "dtype": "string"}, {"name": "Prosuction Assistance", "dtype": "string"}, {"name": "Re-release Date", "dtype": "string"}, {"name": "Release Date", "dtype": "string"}, {"name": "Release Dates", "dtype": "string"}, {"name": "Release Details", "dtype": "string"}, {"name": "Release Info", "dtype": "string"}, {"name": "Release info", "dtype": "string"}, {"name": "Released by", "dtype": "string"}, {"name": "Released by/Sculpted by", "dtype": "string"}, {"name": "Released/Distributed by", "dtype": "string"}, {"name": "Released/Sold by", "dtype": "string"}, {"name": "Rerelease Price", "dtype": "string"}, {"name": "Resale", "dtype": "string"}, {"name": "Resale Info", "dtype": "string"}, {"name": "Resale info", "dtype": "string"}, {"name": "Retailers", "dtype": "string"}, {"name": "SNOW MIKU for SAPPORO2011 and Wonder Festival 2011 (Winter) Product", "dtype": "string"}, {"name": "Sales", "dtype": "string"}, {"name": "Sales Agency", "dtype": "string"}, {"name": "Sales Agent", "dtype": "string"}, {"name": "Sales Info", "dtype": "string"}, {"name": "Sculpor", "dtype": "string"}, {"name": "Sculpted/Released by", "dtype": "string"}, {"name": "Sculpting", "dtype": "string"}, {"name": "Sculpting Assistance", "dtype": "string"}, {"name": "Sculpting/Cooperation", "dtype": "string"}, {"name": "Sculpting/Paintwork", "dtype": "string"}, {"name": "Sculpting/Production", "dtype": "string"}, {"name": "Sculpting/Production/Sold By", "dtype": "string"}, {"name": "Sculpting/Released by", "dtype": "string"}, {"name": "Sculpting/Released by FREEing", "dtype": "string"}, {"name": "Sculptor", "dtype": "string"}, {"name": "Sculptor/Cooperation", "dtype": "string"}, {"name": "Sculptor/Paintwork", "dtype": "string"}, {"name": "Sculptor/Production/Sold By", "dtype": "string"}, {"name": "Scultping/Released by", "dtype": "string"}, {"name": "Second Rerelease Price", "dtype": "string"}, {"name": "Series", "dtype": "string"}, {"name": "Shinnichi Premium Store Limited Edition Product", "dtype": "string"}, {"name": "Size", "dtype": "string"}, {"name": "Sizes", "dtype": "string"}, {"name": "Snow Miku 2013 Outfit Design", "dtype": "string"}, {"name": "Sold At", "dtype": "string"}, {"name": "Sold and Released by", "dtype": "string"}, {"name": "Sold at", "dtype": "string"}, {"name": "Sold by", "dtype": "string"}, {"name": "Sold by/Distributor", "dtype": "string"}, {"name": "Sold/Released by", "dtype": "string"}, {"name": "Specification", "dtype": "string"}, {"name": "Specifications", "dtype": "string"}, {"name": "Stores", "dtype": "string"}, {"name": "Summer Wonder Festival 2017 Product", "dtype": "string"}, {"name": "Summer Wonder Festival 2018 Product", "dtype": "string"}, {"name": "Supervision", "dtype": "string"}, {"name": "TYPE-MOON Fes\u8ca9\u58f2\u5546\u54c1", "dtype": "string"}, {"name": "Target age", "dtype": "string"}, {"name": "WONDERFUL HOBBY LIFE FOR YOU!!32 Product", "dtype": "string"}, {"name": "Winter Wonder Festival 2018 Product", "dtype": "string"}, {"name": "Wonder Festival 2006 Winter Product", "dtype": "string"}, {"name": "Wonder Festival 2011 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2011 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2011 Product", "dtype": "string"}, {"name": "Wonder Festival 2012 (Summer) Product.", "dtype": "string"}, {"name": "Wonder Festival 2012 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2013 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2014 (Winter) Limited Edition Product", "dtype": "string"}, {"name": "Wonder Festival 2014 Presale Product", "dtype": "string"}, {"name": "Wonder Festival 2015 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2015 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2016 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2016 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2016 (Winter/Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2019 Summer Product", "dtype": "string"}, {"name": "Wonder Festival 2019 Winter Product", "dtype": "string"}, {"name": "Wonder Festival 2020 Winter Product", "dtype": "string"}, {"name": "not to scale - approximately 100mm in height", "dtype": "string"}, {"name": "painted ABS&PVC poseable figure - not to scale - stand included - approximately 100mm in height", "dtype": "string"}, {"name": "pecifications", "dtype": "string"}, {"name": "plus one secret figure", "dtype": "string"}, {"name": "\u203bD.C.II To You \uff5e\u30c0\u30fb\u30ab\u30fc\u30ddII\uff5e\u30c8\u30a5\u30fc\u30e6\u30fc \u30b5\u30fc\u30ab\u30b9\u901a\u8ca9\u9650\u5b9a\u7248\u3000\u540c\u68b1", "dtype": "string"}, {"name": "\u203bWonder Festival 2006 Summer Product", "dtype": "string"}, {"name": 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"string"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 663316, "num_examples": 394}], "download_size": 205977, "dataset_size": 663316}], "configs": [{"config_name": "goodsmile-figma", "data_files": [{"split": "train", "path": "goodsmile-figma/train-*"}]}, {"config_name": "goodsmile-nendoroid", "data_files": [{"split": "train", "path": "goodsmile-nendoroid/train-*"}]}, {"config_name": "goodsmile-scale", "data_files": [{"split": "train", "path": "goodsmile-scale/train-*"}]}, {"config_name": "kotobukiya", "data_files": [{"split": "train", "path": "kotobukiya/train-*"}]}, {"config_name": "myethos", "data_files": [{"split": "train", "path": "myethos/train-*"}]}, {"config_name": "spiritale", "data_files": [{"split": "train", "path": "spiritale/train-*"}]}, {"config_name": "tokyofigure", "data_files": [{"split": "train", "path": "tokyofigure/train-*"}]}]} | 2023-11-29T12:01:52+00:00 | []
| [
"en",
"ja"
]
| TAGS
#size_categories-1K<n<10K #language-English #language-Japanese #license-cc0-1.0 #region-us
| PVC figure products dataset
===========================
This dataset contains product information of figure images scraped from multiple Web sites.
Dataset information
-------------------
Subset: 'goodsmile-figma', Source: URL, Size:
Subset: 'goodsmile-nendoroid', Source: URL, Size:
Subset: 'goodsmile-scale', Source: URL, Size:
Subset: 'kotobukiya', Source: URL, Size:
Subset: 'myethos', Source: URL, Size:
Subset: 'spiritale', Source: URL, Size:
Subset: 'tokyofigures', Source: URL, Size:
| []
| [
"TAGS\n#size_categories-1K<n<10K #language-English #language-Japanese #license-cc0-1.0 #region-us \n"
]
| [
36
]
| [
"passage: TAGS\n#size_categories-1K<n<10K #language-English #language-Japanese #license-cc0-1.0 #region-us \n"
]
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36844e1a894d33a3dd2c68c40aa491431317ca60 | # Dataset Card for "dreambooth-hackathon-nala"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ben-yu/dreambooth-hackathon-nala | [
"region:us"
]
| 2023-01-21T16:13:36+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 87657557.0, "num_examples": 20}], "download_size": 87645130, "dataset_size": 87657557.0}} | 2023-01-21T16:13:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dreambooth-hackathon-nala"
More Information needed | [
"# Dataset Card for \"dreambooth-hackathon-nala\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dreambooth-hackathon-nala\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dreambooth-hackathon-nala\"\n\nMore Information needed"
]
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|
7765a2e7e8255766f36169d5265cfcb5993b14ac |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:https://gitlabdev.bridgeconn.com/software/research/datasets/snow-mountain**
- **Paper:https://arxiv.org/abs/2206.01205**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Snow Mountain dataset contains the audio recordings (in .mp3 format) and the corresponding text of The Bible in 11 Indian languages. The recordings were done in a studio setting by native speakers. Each language has a single speaker in the dataset. Most of these languages are geographically concentrated in the Northern part of India around the state of Himachal Pradesh. Being related to Hindi they all use the Devanagari script for transcription.
We have used this dataset for experiments in ASR tasks. But these could be used for other applications in speech domain, like speaker recognition, language identification or even as unlabelled corpus for pre-training.
### Supported Tasks and Leaderboards
Atomatic speech recognition, Speaker recognition, Language identification
### Languages
Hindi, Haryanvi, Bilaspuri, Dogri, Bhadrawahi, Gaddi, Kangri, Kulvi, Mandeali, Kulvi Outer Seraji, Pahari Mahasui
## 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
The Bible recordings were done in a studio setting by native speakers.
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)
### Citation Information
@inproceedings{Raju2022SnowMD,
title={Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages},
author={Kavitha Raju and V. Anjaly and R. Allen Lish and Joel Mathew},
year={2022}
}
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
| anjalyjayakrishnan/test | [
"task_categories:automatic-speech-recognition",
"multilinguality:multilingual",
"source_datasets:Snow Mountain",
"language:hi",
"language:bgc",
"language:kfs",
"language:dgo",
"language:bhd",
"language:gbk",
"language:xnr",
"language:kfx",
"language:mjl",
"language:kfo",
"language:bfz",
"arxiv:2206.01205",
"region:us"
]
| 2023-01-21T17:15:34+00:00 | {"annotations_creators": [{}], "language_creators": [{}], "language": ["hi", "bgc", "kfs", "dgo", "bhd", "gbk", "xnr", "kfx", "mjl", "kfo", "bfz"], "license": [], "multilinguality": ["multilingual"], "size_categories": [], "source_datasets": ["Snow Mountain"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "pretty_name": "Snow Mountain", "tags": [], "configs": ["hi", "bgc"], "dataset_info": [{"config_name": "hi", "features": [{"name": "Unnamed", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}, {"name": "path", "dtype": "string"}], "splits": [{"name": "train_500", "num_examples": 400}, {"name": "val_500", "num_examples": 100}, {"name": "train_1000", "num_examples": 800}, {"name": "val_1000", "num_examples": 200}, {"name": "test_common", "num_examples": 500}], "dataset_size": "71.41 hrs"}, {"config_name": "bgc", "features": [{"name": "Unnamed", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}, {"name": "path", "dtype": "string"}], "splits": [{"name": "train_500", "num_examples": 400}, {"name": "val_500", "num_examples": 100}, {"name": "train_1000", "num_examples": 800}, {"name": "val_1000", "num_examples": 200}, {"name": "test_common", "num_examples": 500}], "dataset_size": "27.41 hrs"}]} | 2023-02-03T14:08:32+00:00 | [
"2206.01205"
]
| [
"hi",
"bgc",
"kfs",
"dgo",
"bhd",
"gbk",
"xnr",
"kfx",
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]
| TAGS
#task_categories-automatic-speech-recognition #multilinguality-multilingual #source_datasets-Snow Mountain #language-Hindi #language-Haryanvi #language-Bilaspuri #language-Dogri (individual language) #language-Bhadrawahi #language-Gaddi #language-Kangri #language-Kullu Pahari #language-Mandeali #language-Koro (Côte d'Ivoire) #language-Mahasu Pahari #arxiv-2206.01205 #region-us
|
# Dataset Card for [Dataset Name]
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage:
- Repository:URL
- Paper:URL
- Leaderboard:
- Point of Contact:
### Dataset Summary
The Snow Mountain dataset contains the audio recordings (in .mp3 format) and the corresponding text of The Bible in 11 Indian languages. The recordings were done in a studio setting by native speakers. Each language has a single speaker in the dataset. Most of these languages are geographically concentrated in the Northern part of India around the state of Himachal Pradesh. Being related to Hindi they all use the Devanagari script for transcription.
We have used this dataset for experiments in ASR tasks. But these could be used for other applications in speech domain, like speaker recognition, language identification or even as unlabelled corpus for pre-training.
### Supported Tasks and Leaderboards
Atomatic speech recognition, Speaker recognition, Language identification
### Languages
Hindi, Haryanvi, Bilaspuri, Dogri, Bhadrawahi, Gaddi, Kangri, Kulvi, Mandeali, Kulvi Outer Seraji, Pahari Mahasui
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
The Bible recordings were done in a studio setting by native speakers.
#### 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
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
The data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)
@inproceedings{Raju2022SnowMD,
title={Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages},
author={Kavitha Raju and V. Anjaly and R. Allen Lish and Joel Mathew},
year={2022}
}
### Contributions
Thanks to @github-username for adding this dataset.
| [
"# Dataset Card for [Dataset Name]",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage:\n- Repository:URL\n- Paper:URL\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary\n\nThe Snow Mountain dataset contains the audio recordings (in .mp3 format) and the corresponding text of The Bible in 11 Indian languages. The recordings were done in a studio setting by native speakers. Each language has a single speaker in the dataset. Most of these languages are geographically concentrated in the Northern part of India around the state of Himachal Pradesh. Being related to Hindi they all use the Devanagari script for transcription. \n\nWe have used this dataset for experiments in ASR tasks. But these could be used for other applications in speech domain, like speaker recognition, language identification or even as unlabelled corpus for pre-training.",
"### Supported Tasks and Leaderboards\n\nAtomatic speech recognition, Speaker recognition, Language identification",
"### Languages\n\nHindi, Haryanvi, Bilaspuri, Dogri, Bhadrawahi, Gaddi, Kangri, Kulvi, Mandeali, Kulvi Outer Seraji, Pahari Mahasui",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data\n\nThe Bible recordings were done in a studio setting by native speakers.",
"#### 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",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\nThe data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)\n\n\n\n\n@inproceedings{Raju2022SnowMD,\n title={Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages},\n author={Kavitha Raju and V. Anjaly and R. Allen Lish and Joel Mathew},\n year={2022}\n}",
"### Contributions\n\nThanks to @github-username for adding this dataset."
]
| [
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"# Dataset Card for [Dataset Name]",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage:\n- Repository:URL\n- Paper:URL\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary\n\nThe Snow Mountain dataset contains the audio recordings (in .mp3 format) and the corresponding text of The Bible in 11 Indian languages. The recordings were done in a studio setting by native speakers. Each language has a single speaker in the dataset. Most of these languages are geographically concentrated in the Northern part of India around the state of Himachal Pradesh. Being related to Hindi they all use the Devanagari script for transcription. \n\nWe have used this dataset for experiments in ASR tasks. But these could be used for other applications in speech domain, like speaker recognition, language identification or even as unlabelled corpus for pre-training.",
"### Supported Tasks and Leaderboards\n\nAtomatic speech recognition, Speaker recognition, Language identification",
"### Languages\n\nHindi, Haryanvi, Bilaspuri, Dogri, Bhadrawahi, Gaddi, Kangri, Kulvi, Mandeali, Kulvi Outer Seraji, Pahari Mahasui",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data\n\nThe Bible recordings were done in a studio setting by native speakers.",
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"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\nThe data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)\n\n\n\n\n@inproceedings{Raju2022SnowMD,\n title={Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages},\n author={Kavitha Raju and V. Anjaly and R. Allen Lish and Joel Mathew},\n year={2022}\n}",
"### Contributions\n\nThanks to @github-username for adding this dataset."
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|
2af40e78838da0dd2c35631edc30dc09ec7551c3 | # Dataset Card for "plant_species"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jbarat/plant_species | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:unknown",
"region:us"
]
| 2023-01-21T17:50:33+00:00 | {"language": ["en"], "license": "unknown", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification"], "pretty_name": "Plant Species", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "aechmea_fasciata", "1": "agave_americana", "2": "agave_attenuata", "3": "agave_tequilana", "4": "aglaonema_commutatum", "5": "albuca_spiralis", "6": "allium_cepa", "7": "allium_sativum"}}}}], "splits": [{"name": "train", "num_bytes": 82083349.0, "num_examples": 800}], "download_size": 82004194, "dataset_size": 82083349.0}} | 2023-01-22T14:03:45+00:00 | []
| [
"en"
]
| TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-unknown #region-us
| # Dataset Card for "plant_species"
More Information needed | [
"# Dataset Card for \"plant_species\"\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-unknown #region-us \n",
"# Dataset Card for \"plant_species\"\n\nMore Information needed"
]
| [
40,
14
]
| [
"passage: TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-unknown #region-us \n# Dataset Card for \"plant_species\"\n\nMore Information needed"
]
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|
b192525fc7e0b440cef7a930f829475491678168 |
# GovReport Summarization - 8192 tokens
- `ccdv/govreport-summarization` with the changes of:
- data cleaned with the [clean-text python package](https://pypi.org/project/clean-text/)
- total tokens for each column computed and added in new columns according to the `long-t5` tokenizer (_done **after** cleaning_)
---
## train info
```python
RangeIndex: 8200 entries, 0 to 8199
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 report 8200 non-null string
1 summary 8200 non-null string
2 input_token_len 8200 non-null Int64
3 summary_token_len 8200 non-null Int64
dtypes: Int64(2), string(2)
memory usage: 272.4 KB
```
## token length distribution (long-t5)
--- | pszemraj/govreport-summarization-8192 | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"source_datasets:ccdv/govreport-summarization",
"language:en",
"license:apache-2.0",
"govreport",
"long document",
"region:us"
]
| 2023-01-21T19:04:28+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "source_datasets": "ccdv/govreport-summarization", "task_categories": ["summarization"], "pretty_name": "GovReport Summarization - 8192 tokens", "tags": ["govreport", "long document"]} | 2023-04-21T21:17:46+00:00 | []
| [
"en"
]
| TAGS
#task_categories-summarization #size_categories-1K<n<10K #source_datasets-ccdv/govreport-summarization #language-English #license-apache-2.0 #govreport #long document #region-us
|
# GovReport Summarization - 8192 tokens
- 'ccdv/govreport-summarization' with the changes of:
- data cleaned with the clean-text python package
- total tokens for each column computed and added in new columns according to the 'long-t5' tokenizer (_done after cleaning_)
---
## train info
## token length distribution (long-t5)
!tokens
--- | [
"# GovReport Summarization - 8192 tokens\n\n- 'ccdv/govreport-summarization' with the changes of:\n - data cleaned with the clean-text python package\n - total tokens for each column computed and added in new columns according to the 'long-t5' tokenizer (_done after cleaning_)\n---",
"## train info",
"## token length distribution (long-t5)\n\n!tokens\n\n---"
]
| [
"TAGS\n#task_categories-summarization #size_categories-1K<n<10K #source_datasets-ccdv/govreport-summarization #language-English #license-apache-2.0 #govreport #long document #region-us \n",
"# GovReport Summarization - 8192 tokens\n\n- 'ccdv/govreport-summarization' with the changes of:\n - data cleaned with the clean-text python package\n - total tokens for each column computed and added in new columns according to the 'long-t5' tokenizer (_done after cleaning_)\n---",
"## train info",
"## token length distribution (long-t5)\n\n!tokens\n\n---"
]
| [
62,
80,
3,
14
]
| [
"passage: TAGS\n#task_categories-summarization #size_categories-1K<n<10K #source_datasets-ccdv/govreport-summarization #language-English #license-apache-2.0 #govreport #long document #region-us \n# GovReport Summarization - 8192 tokens\n\n- 'ccdv/govreport-summarization' with the changes of:\n - data cleaned with the clean-text python package\n - total tokens for each column computed and added in new columns according to the 'long-t5' tokenizer (_done after cleaning_)\n---## train info## token length distribution (long-t5)\n\n!tokens\n\n---"
]
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|
456efd85d3cf03ee13e7b28cd9e34657c8b5c15b |
# Dataset Card for spaeti_store
## Dataset Description
The dataset consists of 12 pictures of different spätis (German convenience stores) from different angles.
The data is unlabeled.
The dataset was created to fine-tune a text-to-image Stable Diffusion model as part of the DreamBooth Hackathon. Visit the [organization's page](https://huggingface.co/dreambooth-hackathon) for more info. | malysheva42/spaeti_stores | [
"task_categories:image-to-text",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-classification",
"size_categories:n<1K",
"license:openrail",
"region:us"
]
| 2023-01-21T19:41:50+00:00 | {"license": "openrail", "size_categories": ["n<1K"], "task_categories": ["image-to-text", "image-segmentation", "image-to-image", "image-classification"]} | 2023-02-07T18:09:43+00:00 | []
| []
| TAGS
#task_categories-image-to-text #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-classification #size_categories-n<1K #license-openrail #region-us
|
# Dataset Card for spaeti_store
## Dataset Description
The dataset consists of 12 pictures of different spätis (German convenience stores) from different angles.
The data is unlabeled.
The dataset was created to fine-tune a text-to-image Stable Diffusion model as part of the DreamBooth Hackathon. Visit the organization's page for more info. | [
"# Dataset Card for spaeti_store",
"## Dataset Description\nThe dataset consists of 12 pictures of different spätis (German convenience stores) from different angles. \nThe data is unlabeled. \nThe dataset was created to fine-tune a text-to-image Stable Diffusion model as part of the DreamBooth Hackathon. Visit the organization's page for more info."
]
| [
"TAGS\n#task_categories-image-to-text #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-classification #size_categories-n<1K #license-openrail #region-us \n",
"# Dataset Card for spaeti_store",
"## Dataset Description\nThe dataset consists of 12 pictures of different spätis (German convenience stores) from different angles. \nThe data is unlabeled. \nThe dataset was created to fine-tune a text-to-image Stable Diffusion model as part of the DreamBooth Hackathon. Visit the organization's page for more info."
]
| [
69,
9,
77
]
| [
"passage: TAGS\n#task_categories-image-to-text #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-classification #size_categories-n<1K #license-openrail #region-us \n# Dataset Card for spaeti_store## Dataset Description\nThe dataset consists of 12 pictures of different spätis (German convenience stores) from different angles. \nThe data is unlabeled. \nThe dataset was created to fine-tune a text-to-image Stable Diffusion model as part of the DreamBooth Hackathon. Visit the organization's page for more info."
]
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|
a2a0b33eb96a95e0c260667a45f8a8e807d569c6 |
**‼️ This is not a real dataset!‼️** This dataset is used to demo using Hub [webooks](https://huggingface.co/docs/hub/webhooks) to automate metadata quality review. | davanstrien/test_webhook | [
"license:openrail",
"region:us"
]
| 2023-01-21T20:04:00+00:00 | {"license": "openrail"} | 2023-01-31T15:27:27+00:00 | []
| []
| TAGS
#license-openrail #region-us
|
‼️ This is not a real dataset!‼️ This dataset is used to demo using Hub webooks to automate metadata quality review. | []
| [
"TAGS\n#license-openrail #region-us \n"
]
| [
12
]
| [
"passage: TAGS\n#license-openrail #region-us \n"
]
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|
d04051a88744820ac8f257ce48ad8e89ffa19b96 | # Dataset Card for "yolochess_deepblue"
Source: https://github.com/niklasf/python-chess/tree/master/data/pgn
Features:
- fen = Chess board position in [FEN](https://en.wikipedia.org/wiki/Forsyth%E2%80%93Edwards_Notation) format
- move = Move played by a strong human player in this position
- result = Final result of the match
- eco = Opening [ECO](https://en.wikipedia.org/wiki/Encyclopaedia_of_Chess_Openings)-code
Deduplicated on (fen, move) pairs.
Samples: 511 | jrahn/yolochess_deepblue | [
"task_categories:text-classification",
"task_categories:reinforcement-learning",
"size_categories:n<1K",
"license:gpl-3.0",
"chess",
"region:us"
]
| 2023-01-21T20:26:09+00:00 | {"license": "gpl-3.0", "size_categories": ["n<1K"], "task_categories": ["text-classification", "reinforcement-learning"], "dataset_info": {"features": [{"name": "fen", "dtype": "string"}, {"name": "move", "dtype": "string"}, {"name": "result", "dtype": "string"}, {"name": "eco", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 45608.0, "num_examples": 511}], "download_size": 18295, "dataset_size": 45608.0}, "tags": ["chess"]} | 2023-02-03T21:29:20+00:00 | []
| []
| TAGS
#task_categories-text-classification #task_categories-reinforcement-learning #size_categories-n<1K #license-gpl-3.0 #chess #region-us
| # Dataset Card for "yolochess_deepblue"
Source: URL
Features:
- fen = Chess board position in FEN format
- move = Move played by a strong human player in this position
- result = Final result of the match
- eco = Opening ECO-code
Deduplicated on (fen, move) pairs.
Samples: 511 | [
"# Dataset Card for \"yolochess_deepblue\"\n\nSource: URL \n\nFeatures:\n- fen = Chess board position in FEN format\n- move = Move played by a strong human player in this position\n- result = Final result of the match\n- eco = Opening ECO-code\n \nDeduplicated on (fen, move) pairs. \n \nSamples: 511"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-reinforcement-learning #size_categories-n<1K #license-gpl-3.0 #chess #region-us \n",
"# Dataset Card for \"yolochess_deepblue\"\n\nSource: URL \n\nFeatures:\n- fen = Chess board position in FEN format\n- move = Move played by a strong human player in this position\n- result = Final result of the match\n- eco = Opening ECO-code\n \nDeduplicated on (fen, move) pairs. \n \nSamples: 511"
]
| [
50,
79
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-reinforcement-learning #size_categories-n<1K #license-gpl-3.0 #chess #region-us \n# Dataset Card for \"yolochess_deepblue\"\n\nSource: URL \n\nFeatures:\n- fen = Chess board position in FEN format\n- move = Move played by a strong human player in this position\n- result = Final result of the match\n- eco = Opening ECO-code\n \nDeduplicated on (fen, move) pairs. \n \nSamples: 511"
]
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|
5ee18cb324f084c03c03f649d77432fec0bf146a | # Dataset Card for "patched_1000_test_p_40_m2_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_1000_test_p_40_m2_predictions | [
"region:us"
]
| 2023-01-21T21:16:38+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "features", "sequence": "float64"}, {"name": "m2_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 8380474294, "num_examples": 942535}], "download_size": 7949577002, "dataset_size": 8380474294}} | 2023-01-21T21:23:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_1000_test_p_40_m2_predictions"
More Information needed | [
"# Dataset Card for \"patched_1000_test_p_40_m2_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_1000_test_p_40_m2_predictions\"\n\nMore Information needed"
]
| [
6,
27
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_1000_test_p_40_m2_predictions\"\n\nMore Information needed"
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|
b8ed08c39133ff3c59fe8cd3854a4f7c0876fcb0 | ## My Notes 📓
This repository contains my lecture notes from graduate school on following topics 👇🏼
- Data Science: 8 cheatsheets
- Machine Learning (follows [Tom Mitchell's book](http://www.cs.cmu.edu/~tom/mlbook.html)): 25 pages of notes
- Statistics: 9 cheatsheets
- Deep Learning: 12 cheatsheets, will upload more
- Image Processing (follows [digital image processing book](https://www.amazon.fr/Digital-Image-Processing-Rafael-Gonzalez/dp/013168728X)): 21 cheatsheets
- Data Structures and Algorithms (follows [this book by Goodrich](https://www.wiley.com/en-us/Data+Structures+and+Algorithms+in+Python-p-9781118549582)): 26 cheatsheets
✨ *Some notes* ✨
- Most of these notes aren't intended to teach a topic from scratch but are rather notes that I took and compiled during my midterm & finals, might help you remember things, study for exams, and prepare for job interviews.
- There might be very small Turkish notes in few of the pages, you can ignore them.
- I will upload more notes as I find or create them. Will soon compile my Hugging Face cheatsheets so stay tuned!
- It's appreciated if you could improve the quality of PDF handwritten scans or convert them to JPEG, you can open a PR to this repository.
*Updates* 🎉
- I uploaded hierarchical clustering and improved version of K-means.
- I compiled every lecture in separate PDFs, and also compiled those into single PDF, found under `Compiled PDF`s.
- I uploaded Hugging Face cheatsheets. | merve/my_notes | [
"license:apache-2.0",
"region:us"
]
| 2023-01-21T21:35:32+00:00 | {"license": "apache-2.0"} | 2023-01-22T14:54:19+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| ## My Notes
This repository contains my lecture notes from graduate school on following topics
- Data Science: 8 cheatsheets
- Machine Learning (follows Tom Mitchell's book): 25 pages of notes
- Statistics: 9 cheatsheets
- Deep Learning: 12 cheatsheets, will upload more
- Image Processing (follows digital image processing book): 21 cheatsheets
- Data Structures and Algorithms (follows this book by Goodrich): 26 cheatsheets
*Some notes*
- Most of these notes aren't intended to teach a topic from scratch but are rather notes that I took and compiled during my midterm & finals, might help you remember things, study for exams, and prepare for job interviews.
- There might be very small Turkish notes in few of the pages, you can ignore them.
- I will upload more notes as I find or create them. Will soon compile my Hugging Face cheatsheets so stay tuned!
- It's appreciated if you could improve the quality of PDF handwritten scans or convert them to JPEG, you can open a PR to this repository.
*Updates*
- I uploaded hierarchical clustering and improved version of K-means.
- I compiled every lecture in separate PDFs, and also compiled those into single PDF, found under 'Compiled PDF's.
- I uploaded Hugging Face cheatsheets. | []
| [
"TAGS\n#license-apache-2.0 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n"
]
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|
8e1bf642ae77cc209c00d13dc808abbea0d2b1b7 | # Dataset Card for "OxfordPets_test_facebook_opt_350m_Visclues_ns_20"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordPets_test_facebook_opt_350m_Visclues_ns_20 | [
"region:us"
]
| 2023-01-21T22:06:18+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_5_bs_3", "num_bytes": 292097.0, "num_examples": 20}], "download_size": 0, "dataset_size": 292097.0}} | 2023-01-22T04:38:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordPets_test_facebook_opt_350m_Visclues_ns_20"
More Information needed | [
"# Dataset Card for \"OxfordPets_test_facebook_opt_350m_Visclues_ns_20\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordPets_test_facebook_opt_350m_Visclues_ns_20\"\n\nMore Information needed"
]
| [
6,
31
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_facebook_opt_350m_Visclues_ns_20\"\n\nMore Information needed"
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|
dc19a6a3f100050376f84a52a2bf5a2de322d64f | # Dataset Card for "third_experiment_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arefm/third_experiment_data | [
"region:us"
]
| 2023-01-21T22:54:44+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "id", "dtype": "string"}, {"name": "texts", "dtype": "string"}, {"name": "noisy_audio_0", "dtype": "audio"}, {"name": "noisy_audio_10", "dtype": "audio"}, {"name": "noisy_audio_20", "dtype": "audio"}, {"name": "noisy_audio_30", "dtype": "audio"}, {"name": "noisy_audio_40", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 275715715.0, "num_examples": 200}], "download_size": 267505861, "dataset_size": 275715715.0}} | 2023-01-21T22:57:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "third_experiment_data"
More Information needed | [
"# Dataset Card for \"third_experiment_data\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"third_experiment_data\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"third_experiment_data\"\n\nMore Information needed"
]
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|
e157acbed57741fdaa82e56ef8a6a6525a0dfdd0 | # Dataset Card for "pii-pile-chunk3-150000-200000-tagged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | j-chim/pii-pile-chunk3-150000-200000-tagged | [
"region:us"
]
| 2023-01-21T23:05:44+00:00 | {"dataset_info": {"features": [{"name": "texts", "sequence": "string"}, {"name": "meta", "struct": [{"name": "pile_set_name", "dtype": "string"}]}, {"name": "scores", "sequence": "float64"}, {"name": "avg_score", "dtype": "float64"}, {"name": "num_sents", "dtype": "int64"}, {"name": "tagged_pii_results", "list": [{"name": "analysis_explanation", "dtype": "null"}, {"name": "end", "dtype": "int64"}, {"name": "entity_type", "dtype": "string"}, {"name": "recognition_metadata", "struct": [{"name": "recognizer_identifier", "dtype": "string"}, {"name": "recognizer_name", "dtype": "string"}]}, {"name": "score", "dtype": "float64"}, {"name": "start", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 512476526, "num_examples": 49998}], "download_size": 196006381, "dataset_size": 512476526}} | 2023-01-21T23:06:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pii-pile-chunk3-150000-200000-tagged"
More Information needed | [
"# Dataset Card for \"pii-pile-chunk3-150000-200000-tagged\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pii-pile-chunk3-150000-200000-tagged\"\n\nMore Information needed"
]
| [
6,
26
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pii-pile-chunk3-150000-200000-tagged\"\n\nMore Information needed"
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|
da5407f6fa62895b25315b4a1ed4bca3704c8039 |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | samp3209/logo-dataset | [
"region:us"
]
| 2023-01-21T23:50:18+00:00 | {} | 2023-01-22T00:30:36+00:00 | []
| []
| TAGS
#region-us
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# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## 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
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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|
a5de7c0293a6bfb83c5104b290b0902094f45976 |
# ParaShoot
[ParaShoot](https://github.com/omrikeren/ParaShoot): A Hebrew question and answering dataset in the style of [SQuAD](https://arxiv.org/abs/1606.05250), based on articles scraped from Wikipedia. The dataset contains a few thousand crowdsource-annotated pairs of questions and answers, in a setting suitable for few-shot learning.
For more details and quality analysis, see the [paper](https://arxiv.org/abs/2109.11314).
## Dataset Statistics
| **#Items** | **#Articles** | **#Paragraphs** | |
| ---------- | ------------- | --------------- | ------- |
| Train | 1792 | 295 | 565 |
| Dev | 221 | 33 | 63 |
| Test | 1025 | 165 | 319 |
| **Total** | **3038** | **493** | **947** |
## Citing
If you use ParaShoot in your research, please cite the ParaShoot paper:
```bibtex
@inproceedings{keren2021parashoot,
title={ParaShoot: A Hebrew Question Answering Dataset},
author={Keren, Omri and Levy, Omer},
booktitle={Proceedings of the 3rd Workshop on Machine Reading for Question Answering},
pages={106--112},
year={2021}
}
``` | imvladikon/parashoot | [
"task_categories:question-answering",
"language:he",
"arxiv:1606.05250",
"arxiv:2109.11314",
"region:us"
]
| 2023-01-22T00:05:53+00:00 | {"language": ["he"], "task_categories": ["question-answering"]} | 2023-01-22T00:32:13+00:00 | [
"1606.05250",
"2109.11314"
]
| [
"he"
]
| TAGS
#task_categories-question-answering #language-Hebrew #arxiv-1606.05250 #arxiv-2109.11314 #region-us
| ParaShoot
=========
ParaShoot: A Hebrew question and answering dataset in the style of SQuAD, based on articles scraped from Wikipedia. The dataset contains a few thousand crowdsource-annotated pairs of questions and answers, in a setting suitable for few-shot learning.
For more details and quality analysis, see the paper.
Dataset Statistics
------------------
Citing
------
If you use ParaShoot in your research, please cite the ParaShoot paper:
| []
| [
"TAGS\n#task_categories-question-answering #language-Hebrew #arxiv-1606.05250 #arxiv-2109.11314 #region-us \n"
]
| [
39
]
| [
"passage: TAGS\n#task_categories-question-answering #language-Hebrew #arxiv-1606.05250 #arxiv-2109.11314 #region-us \n"
]
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|
d5ea85a28342947bfa0ad94075840f21373a256d | # Dataset Card for "hack"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | merkalo-ziri/hack | [
"region:us"
]
| 2023-01-22T00:34:59+00:00 | {"dataset_info": {"features": [{"name": "labels", "sequence": "int64"}, {"name": "pixel_values", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 10719767724, "num_examples": 17679}, {"name": "test", "num_bytes": 2680093520, "num_examples": 4420}], "download_size": 2998628450, "dataset_size": 13399861244}} | 2023-01-30T18:51:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "hack"
More Information needed | [
"# Dataset Card for \"hack\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"hack\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"hack\"\n\nMore Information needed"
]
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|
6ffe8fc804065cbfecc26cec94f00b702a9dce30 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
* Config: default
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Shepel](https://huggingface.co/Shepel) for evaluating this model. | autoevaluate/autoeval-eval-acronym_identification-default-d2d5a9-3002686420 | [
"autotrain",
"evaluation",
"region:us"
]
| 2023-01-22T01:09:57+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["acronym_identification"], "eval_info": {"task": "entity_extraction", "model": "lewtun/autotrain-acronym-identification-7324788", "metrics": [], "dataset_name": "acronym_identification", "dataset_config": "default", "dataset_split": "validation", "col_mapping": {"tokens": "tokens", "tags": "labels"}}} | 2023-01-22T01:10:47+00:00 | []
| []
| TAGS
#autotrain #evaluation #region-us
| # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
* Config: default
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @Shepel for evaluating this model. | [
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: lewtun/autotrain-acronym-identification-7324788\n* Dataset: acronym_identification\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Shepel for evaluating this model."
]
| [
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: lewtun/autotrain-acronym-identification-7324788\n* Dataset: acronym_identification\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @Shepel for evaluating this model."
]
| [
13,
97,
15
]
| [
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: lewtun/autotrain-acronym-identification-7324788\n* Dataset: acronym_identification\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Shepel for evaluating this model."
]
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|
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