id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
LeoCordoba/CC-NEWS-ES-titles | 2023-02-23T21:53:46.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:cc-news",
"language:es",
"license:mit",
"conditional-text-generation",
"region:us"
] | LeoCordoba | null | null | 2 | 7 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- es
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- cc-news
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-generation
---
# Dataset Card for CC-NEWS-ES-titles
## 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:** [CC-NEWS-ES-titles dataset repository](https://huggingface.co/datasets/LeoCordoba/CC-NEWS-ES-titles)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Leonardo Ignacio Córdoba](https://www.linkedin.com/in/leonardo-ignacio-c%C3%B3rdoba/)
### Dataset Summary
CC-NEWS-ES-titles is a Spanish-language dataset for news titles generation. The text and titles comes from 2019 and 2020 CC-NEWS data (which is part of Common Crawl).
It contains 402.310 pairs of news title and body, splitted in :
- Train: 370.125
- Eval: 16.092
- Test: 16.092
### Supported Tasks and Leaderboards
- `text-classification`, `sentiment-classification`: The dataset can be used to train a model for news title generation which can be considered a subset of abstractive summarization.
### Languages
The text is in Spanish. The BCP-47 code for Spanish is es.
## Dataset Structure
### Data Instances
Each data instance contains the following features: _text_ and _output_text_.
- _text_ is the body of the news.
- _output_text_ is the title of the news.
An example from the CC-NEWS-ES-titles train set looks like the following:
```
{'text': 'Hoy en el Boletín Oficial también se publicó la disposición para universidades, institutos universitarios y de educación superior de todas las jurisdicciones, a las que recomienda que "adecúen las condiciones en que se desarrolla la actividad académica presencial en el marco de la emergencia conforme con las recomendaciones del Ministerio de Salud", según lo publicado por la agencia ',
'output_text': 'Coronavirus: "Seguimos educando", la plataforma online para que los chicos estudien en cuarentena'}
```
### Data Fields
- 'text': a string containing the body of the news.
- 'output_text': a string containing the title of the news.
### Data Splits
The CC-NEWS-ES-titles dataset has 3 splits: _train_, _validation_, and _test_. The splits contain disjoint sets of news.
| Dataset Split | Number of Instances in Split |
| ------------- | ---------------------------- |
| Train | 370.125 |
| Eval | 16.092 |
| Test | 16.092 |
## Dataset Creation
### Curation Rationale
[N/A]
### Source Data
#### Initial Data Collection and Normalization
TODO
#### Who are the source language producers?
Common Crawl: https://commoncrawl.org/
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
Abstractive summarization is a complex task and Spanish is a underrepresented language in the NLP domain. As a consequence, adding a Spanish resource may help others to improve their research and educational activities.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
This dataset is maintained by [Leonardo Ignacio Córdoba](https://www.linkedin.com/in/leonardo-ignacio-c%C3%B3rdoba/) and was built with the help of [María Gaska](https://www.linkedin.com/in/mfgaska/).
### Licensing Information
[N/A]
### Citation Information
TODO
### Contributions
[N/A] | |
NYTK/HuCOLA | 2022-10-21T16:08:35.000Z | [
"task_ids:text-simplification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:hu",
"license:cc-by-sa-4.0",
"region:us"
] | NYTK | null | null | null | 0 | 7 | ---
YAML tags:
annotations_creators:
- expert-generated
language_creators:
- found
language:
- hu
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: HuCOLA
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conditional-text-generation
task_ids:
- machine-translation
- summarization
- text-simplification
---
# Dataset Card for HuCOLA
## 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:**
[HuCOLA dataset](https://github.com/nytud/HuCOLA)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
[lnnoemi](mailto:ligeti-nagy.noemi@nytud.hu)
### Dataset Summary
This is the dataset card for the Hungarian Corpus of Linguistic Acceptability (HuCOLA), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit [HuLU](hulu.nlp.nytud.hu).
### Supported Tasks and Leaderboards
### Languages
The BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.
## Dataset Structure
### Data Instances
For each instance, there is aN id, a sentence and a label.
An example:
```
{"Sent_id": "dev_0",
"Sent": "A földek eláradtak.",
"Label": "0"}
```
### Data Fields
- Sent_id: unique id of the instances, an integer between 1 and 1000;
- Sent: a Hungarian sentence;
- label: '0' for wrong, '1' for good sentences.
### Data Splits
HuCOLA has 3 splits: *train*, *validation* and *test*.
| Dataset split | Number of sentences in the split | Proportion of the split
|---------------|----------------------------------| ---------|
| train | 7276 | 80%|
| validation | 900 |10%|
| test | 900 |10%|
The test data is distributed without the labels. To evaluate your model, please [contact us](mailto:ligeti-nagy.noemi@nytud.hu), or check [HuLU's website](hulu.nlp.nytud.hu) for an automatic evaluation (this feature is under construction at the moment). The evaluation metric is Matthew's correlation coefficient.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The data was collected by two human annotators from 3 main linguistic books on Hungarian language:
- Kiefer Ferenc (ed.) (1992), Strukturális magyar nyelvtan 1. Mondattan. Budapest, Akadémiai Kiadó.
- Alberti, Gábor and Laczkó, Tibor (eds) (2018), Syntax of Hungarian Nouns and Noun Phrases. I., II. Comprehensive grammar resources. Amsterdam University Press, Amsterdam.
- Katalin É. Kiss and Veronika Hegedűs (eds) (2021), Postpositions and Postpositional Phrases. Amsterdam: Amsterdam University Press.
The process of collecting sentences partly followed the one described in Warstadt et. al (2018). The guideline of our process is available in the repository of [HuCOLA](https://github.com/nytud/HuCOLA).
### Annotations
#### Annotation process
Each instance was annotated by 4 human annotators for its acceptability (see the annotation guidelines in the repository of [HuCOLA](https://github.com/nytud/HuCOLA)).
#### Who are the annotators?
The annotators were native Hungarian speakers (of various ages, from 20 to 67) without any linguistic backround.
## Additional Information
### Licensing Information
HuCOLA is released under the CC-BY-SA 4.0 licence.
### Citation Information
If you use this resource or any part of its documentation, please refer to:
Ligeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis
kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. (in press)
```
@inproceedings{ligetinagy2022hulu,
title={HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából},
author={Ligeti-Nagy, N. and Ferenczi, G. and Héja, E. and Jelencsik-Mátyus, K. and Laki, L. J. and Vadász, N. and Yang, Z. Gy. and Váradi, T.},
booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
year={2022}
}
```
### Contributions
Thanks to [lnnoemi](https://github.com/lnnoemi) for adding this dataset. |
antoinegk/HealthChallenge_dataset | 2022-01-19T18:21:42.000Z | [
"region:us"
] | antoinegk | null | null | null | 0 | 7 | Entry not found |
holylovenia/recam | 2021-10-18T03:28:53.000Z | [
"region:us"
] | holylovenia | null | null | null | 0 | 7 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# ReCAM: Reading Comprehension of Abstract Meaning
## 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 is from SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning. [Original repository for the dataset and baseline code can be accessed here.](https://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning)
- **Paper:** [SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning in ACL](https://aclanthology.org/2021.semeval-1.4.pdf)
- **Leaderboard:** [CodaLab](https://competitions.codalab.org/competitions/26153#learn_the_details)
### Dataset Summary
Refer to [this page](https://competitions.codalab.org/competitions/26153#learn_the_details).
## Dataset Structure
Refer to [the GitHub](https://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning).
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{zheng-etal-2021-semeval,
title = "{S}em{E}val-2021 Task 4: Reading Comprehension of Abstract Meaning",
author = "Zheng, Boyuan and
Yang, Xiaoyu and
Ruan, Yu-Ping and
Ling, Zhenhua and
Liu, Quan and
Wei, Si and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.4",
doi = "10.18653/v1/2021.semeval-1.4",
pages = "37--50",
}
```
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
indonesian-nlp/id_newspapers_2018 | 2022-10-25T13:47:43.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:id",
"license:cc-by-4.0",
"region:us"
] | indonesian-nlp | null | null | null | 8 | 7 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: Indonesian Newspapers 2018
---
# Dataset of Indonesian Online Newspaper
This is a copy of dataset created by **Feryandi Nurdiantoro** (<https://github.com/feryandi/Dataset-Artikel>). The original dataset in json format is stored uncompressed in Google Drive in more than 500K files, one file per article. Unfortunately, due to its size, it is impossible to download the whole dataset as one big compressed file (it takes forever to compress it online). Therefore I provide here a copy and its cleaned version as compressed files.
The dataset contains around 500K articles (136M of words) from 7 Indonesian newspapers: Detik, Kompas, Tempo, CNN Indonesia, Sindo, Republika and Poskota. The articles are dated between 1st January 2018 and 20th August 2018 (with few exceptions dated earlier). The size of uncompressed 500K json files (newspapers-json.tgz) is around 2.2GB, and the cleaned uncompressed in a big text file (newspapers.txt.gz) is about 1GB. The original source in Google Drive contains also a dataset in html format which include raw data (pictures, css, javascript, ...) from the online news website. I don't copy it here since it is about 60GB and mostly we only need the text content for NLP research.
Following is the compressed files:
* newspaper-json.gz: the compressed original 500K json files.
* newspaper.txt.gz: a dump of all json files in one big cleaned text file which is normally the only one needed for language model training.
The license has been copied from the source:
## License
Proyek ini dilisensikan dibawah lisensi **Creative Commons Attribution-ShareAlike 4.0 International License**\*. Kumpulan data yang dibagikan bertujuan untuk ilmu pengetahuan, pembelajaran, dan penelitian Bahasa Indonesia (komputasi maupun lingusitik), dan hanya dapat digunakan untuk hal tersebut. Kepemilikan data untuk setiap artikel dimiliki oleh media yang bersangkutan dimana data tersebut diambil; dan pemilik repository ini tidak melakukan klaim kepemilikan atas konten tersebut. Jika Anda mendapati bahwa data ini telah melanggar suatu hak cipta; mohon kontak pengelola repository ini.
This work is licensed under a **Creative Commons Attribution-ShareAlike 4.0 International License**. The dataset is shared for the sole purpose of aiding open scientific research in Bahasa Indonesia (computing or linguistics), and can only be used for that purpose. The ownership of each article within the dataset belongs to the respective newspaper from which it was extracted; and the maintainer of the repository does not claim ownership of any of the content within it. If you think, by any means, that this dataset breaches any established copyrights; please contact the repository maintainer.
|
midas/citeulike180 | 2022-01-23T06:52:23.000Z | [
"region:us"
] | midas | \ | @inproceedings{medelyan-etal-2009-human,
title = "Human-competitive tagging using automatic keyphrase extraction",
author = "Medelyan, Olena and
Frank, Eibe and
Witten, Ian H.",
booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
month = aug,
year = "2009",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D09-1137",
pages = "1318--1327",
} | null | 0 | 7 | ## Dataset Summary
A dataset for benchmarking keyphrase extraction and generation techniques from long document english scientific articles. For more details about the dataset please refer the original paper - [https://aclanthology.org/D09-1137/](https://aclanthology.org/D09-1137/)
Original source of the data - []()
## Dataset Structure
### Data Fields
- **id**: unique identifier of the document.
- **document**: Whitespace separated list of words in the document.
- **doc_bio_tags**: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.
- **extractive_keyphrases**: List of all the present keyphrases.
- **abstractive_keyphrase**: List of all the absent keyphrases.
### Data Splits
|Split| #datapoints |
|--|--|
| Test | 182 |
## Usage
### Full Dataset
```python
from datasets import load_dataset
# get entire dataset
dataset = load_dataset("midas/citeulike180", "raw")
# sample from the test split
print("Sample from test dataset split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
**Output**
```bash
Sample from test data split
Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document: ['Vol', '450', '|', '8', 'November', '2007', '|', 'doi', ':', '10.1038', '/', 'nature06341', 'ARTICLES', 'Evolution', 'of', 'genes', 'and', 'genomes', 'on', 'the', 'Drosophila', 'phylogeny', 'Drosophila', '12', 'Genomes', 'Consortium', '*', 'Comparative', 'analysis', 'of', 'multiple', 'genomes', 'in', 'a', 'phylogenetic', 'framework', 'dramatically', 'improves', 'the', 'precision', 'and', 'sensitivity', 'of', 'evolutionary', 'inference', ',', 'producing', 'more', 'robust', 'results', 'than', 'single-genome', 'analyses', 'can', 'provide', '.', 'The', 'genomes', 'of', '12', 'Drosophila', 'species', ',', 'ten', 'of', 'which', 'are', 'presented', 'here', 'for', 'the', 'first', 'time', '-LRB-', 'sechellia', ',', 'simulans', ',', 'yakuba', ',', 'erecta', ',', 'ananassae', ',', 'persimilis', ',', 'willistoni', ',', 'mojavensis', ',', 'virilis', 'and', 'grimshawi', '-RRB-', ',', 'illustrate', 'how', 'rates', 'and', 'patterns', 'of', 'sequence', 'divergence', 'across', 'taxa', 'can', 'illuminate', 'evolutionary', 'processes', 'on', 'a', 'genomic', 'scale', '.', 'These', 'genome', 'sequences', 'augment', 'the', 'formidable', 'genetic', 'tools', 'that', 'have', 'made', 'Drosophila', 'melanogaster', 'a', 'pre-eminent', 'model', 'for', 'animal', 'genetics', ',', 'and', 'will', 'further', 'catalyse', 'fundamental', 'research', 'on', 'mechanisms', 'of', 'development', ',', 'cell', 'biology', ',', 'genetics', ',', 'disease', ',', 'neurobiology', ',', 'behaviour', ',', 'physiology', 'and', 'evolution', '.', 'Despite', 'remarkable', 'similarities', 'among', 'these', 'Drosophila', 'species', ',', 'we', 'identified', 'many', 'putatively', 'non-neutral', 'changes', 'in', 'protein-coding', 'genes', ',', 'non-coding', 'RNA', 'genes', ',', 'and', 'cis-regulatory', 'regions', '.', 'These', 'may', 'prove', 'to', 'underlie', 'differences', 'in', 'the', 'ecology', 'and', 'behaviour', 'of', 'these', 'diverse', 'species', '.', 'As', 'one', 'might', 'expect', 'from', 'a', 'genus', 'with', 'species', 'living', 'in', 'deserts', ',', 'in', 'the', 'tropics', ',', 'on', 'chains', 'of', 'volcanic', 'islands', 'and', ',', 'often', ',', 'commensally', 'with', 'humans', ',', 'Drosophila', 'species', 'vary', 'considerably', 'in', 'their', 'morphology', ',', 'ecology', 'and', 'behaviour1', '.', 'Species', 'in', 'this', 'genus', 'span', 'a', 'wide', 'range', 'of', 'global', 'distributions', ':', 'the', '12', 'sequenced', 'species', 'originate', 'from', 'Africa', ',', 'Asia', ',', 'the', 'Americas', 'and', 'the', 'Pacific', 'Islands', ',', 'and', 'also', 'include', 'cosmopolitan', 'species', 'that', 'have', 'colonized', 'the', 'planet', '-LRB-', 'D.', 'melanogaster', 'and', 'D.', 'simulans', '-RRB-', 'as', 'well', 'as', 'closely', 'related', 'species', 'that', 'live', 'on', 'single', 'islands', '-LRB-', 'D.', 'sechellia', '-RRB-', '2', '.', 'A', 'variety', 'of', 'behavioural', 'strategies', 'is', 'also', 'encompassed', 'by', 'the', 'sequenced', 'species', ',', 'ranging', 'in', 'feeding', 'habit', 'from', 'generalist', ',', 'such', 'as', 'D.', 'ananassae', ',', 'to', 'specialist', ',', 'such', 'as', 'D.', 'sechellia', ',', 'which', 'feeds', 'on', 'the', 'fruit', 'of', 'a', 'single', 'plant', 'species', '.', 'Despite', 'this', 'wealth', 'of', 'phenotypic', 'diversity', ',', 'Drosophila', 'species', 'share', 'a', 'distinctive', 'body', 'plan', 'and', 'life', 'cycle', '.', 'Although', 'only', 'D.', 'melanogaster', 'has', 'been', 'extensively', 'characterized', ',', 'it', 'seems', 'that', 'the', 'most', 'important', 'aspects', 'of', 'the', 'cellular', ',', 'molecular', 'and', 'developmental', 'biology', 'of', 'these', 'species', 'are', 'well', 'conserved', '.', 'Thus', ',', 'in', 'addition', 'to', 'providing', 'an', 'extensive', 'resource', 'for', 'the', 'study', 'of', 'the', 'relationship', 'between', 'sequence', 'and', 'phenotypic', 'diversity', ',', 'the', 'genomes', 'of', 'these', 'species', 'provide', 'an', 'excellent', 'model', 'for', 'studying', 'how', 'conserved', 'functions', 'are', 'maintained', 'in', 'the', 'face', 'of', 'sequence', 'divergence', '.', 'These', 'genome', 'sequences', 'provide', 'an', 'unprecedented', 'dataset', 'to', 'contrast', 'genome', 'structure', ',', 'genome', 'content', ',', 'and', 'evolutionary', 'dynamics', 'across', 'the', 'well-defined', 'phylogeny', 'of', 'the', 'sequenced', 'species', '-LRB-', 'Fig.', '1', '-RRB-', '.', 'Genome', 'assembly', ',', 'annotation', 'and', 'alignment', 'Genome', 'sequencing', 'and', 'assembly', '.', 'We', 'used', 'the', 'previously', 'published', 'sequence', 'and', 'updated', 'assemblies', 'for', 'two', 'Drosophila', 'species', ',', 'D.', 'melanogaster3', ',4', '-LRB-', 'release', '4', '-RRB-', 'and', 'D.', 'pseudoobscura5', '-LRB-', 'release', '2', '-RRB-', ',', 'and', 'generated', 'DNA', 'sequence', 'data', 'for', '10', 'additional', 'Drosophila', 'genomes', 'by', 'whole-genome', 'shotgun', 'sequencing6', ',7', '.', 'These', 'species', 'were', 'chosen', 'to', 'span', 'a', 'wide', 'variety', 'of', 'evolutionary', 'distances', ',', 'from', 'closely', 'related', 'pairs', 'such', 'as', 'D.', 'sechellia/D', '.', 'simulans', 'and', 'D.', 'persimilis/D', '.', 'pseudoobscura', 'to', 'the', 'distantly', 'related', 'species', 'of', 'the', 'Drosophila', 'and', 'Sophophora', 'subgenera', '.', 'Whereas', 'the', 'time', 'to', 'the', 'most', 'recent', 'common', 'ancestor', 'of', 'the', 'sequenced', 'species', 'may', 'seem', 'small', 'on', 'an', 'evolutionary', 'timescale', ',', 'the', 'evolutionary', 'divergence', 'spanned', 'by', 'the', 'genus', 'Drosophila', 'exceeds', '*', 'A', 'list', 'of', 'participants', 'and', 'affiliations', 'appears', 'at', 'the', 'end', 'of', 'the', 'paper', '.', 'that', 'of', 'the', 'entire', 'mammalian', 'radiation', 'when', 'generation', 'time', 'is', 'taken', 'into', 'account', ',', 'as', 'discussed', 'further', 'in', 'ref', '.', '8', '.', 'We', 'sequenced', 'seven', 'of', 'the', 'new', 'species', '-LRB-', 'D.', 'yakuba', ',', 'D.', 'erecta', ',', 'D.', 'ananassae', ',', 'D.', 'willistoni', ',', 'D.', 'virilis', ',', 'D.', 'mojavensis', 'and', 'D.', 'grimshawi', '-RRB-', 'to', 'deep', 'coverage', '-LRB-', '8.43', 'to', '11.03', '-RRB-', 'to', 'produce', 'high', 'quality', 'draft', 'sequences', '.', 'We', 'sequenced', 'two', 'species', ',', 'D.', 'sechellia', 'and', 'D.', 'persimilis', ',', 'to', 'intermediate', 'coverage', '-LRB-', '4.93', 'and', '4.13', ',', 'respectively', '-RRB-', 'under', 'the', 'assumption', 'that', 'the', 'availability', 'of', 'a', 'sister', 'species', 'sequenced', 'to', 'high', 'coverage', 'would', 'obviate', 'the', 'need', 'for', 'deep', 'sequencing', 'without', 'sacrificing', 'draft', 'genome', 'quality', '.', 'Finally', ',', 'seven', 'inbred', 'strains', 'of', 'D.', 'simulans', 'were', 'sequenced', 'to', 'low', 'coverage', '-LRB-', '2.93', 'coverage', 'from', 'w501', 'and', ',13', 'coverage', 'of', 'six', 'other', 'strains', '-RRB-', 'to', 'provide', 'population', 'variation', 'data9', '.', 'Further', 'details', 'of', 'the', 'sequencing', 'strategy', 'can', 'be', 'found', 'in', 'Table', '1', ',', 'Supplementary', 'Table', '1', 'and', 'section', '1', 'in', 'Supplementary', 'Information', '.', 'We', 'generated', 'an', 'initial', 'draft', 'assembly', 'for', 'each', 'species', 'using', 'one', 'of', 'three', 'different', 'whole-genome', 'shotgun', 'assembly', 'programs', '-LRB-', 'Table', '1', '-RRB-', '.', 'For', 'D.', 'ananassae', ',', 'D.', 'erecta', ',', 'D.', 'grimshawi', ',', 'D.', 'mojavensis', ',', 'D.', 'virilis', 'and', 'D.', 'willistoni', ',', 'we', 'also', 'generated', 'secondary', 'assemblies', ';', 'reconciliation', 'of', 'these', 'with', 'the', 'primary', 'assemblies', 'resulted', 'in', 'a', '7', '30', '%', 'decrease', 'in', 'the', 'estimated', 'number', 'of', 'misassembled', 'regions', 'and', 'a', '12', '23', '%', 'increase', 'in', 'the', 'N50', 'contig', 'size10', '-LRB-', 'Supplementary', 'Table', '2', '-RRB-', '.', 'For', 'D.', 'yakuba', ',', 'we', 'generated', '52,000', 'targeted', 'reads', 'across', 'low-quality', 'regions', 'and', 'gaps', 'to', 'improve', 'the', 'assembly', '.', 'This', 'doubled', 'the', 'mean', 'contig', 'and', 'scaffold', 'sizes', 'and', 'increased', 'the', 'total', 'fraction', 'of', 'high', 'quality', 'bases', '-LRB-', 'quality', 'score', '-LRB-', 'Q', '-RRB-', '.', '40', '-RRB-', 'from', '96.5', '%', 'to', '98.5', '%', '.', 'We', 'improved', 'the', 'initial', '2.93', 'D.', 'simulans', 'w501', 'whole-genome', 'shotgun', 'assembly', 'by', 'filling', 'assembly', 'gaps', 'with', 'contigs', 'and', 'unplaced', 'reads', 'from', 'the', ',13', 'assemblies', 'of', 'the', 'six', 'other', 'D.', 'simulans', 'strains', ',', 'generating', 'a', '`', 'mosaic', "'", 'assembly', '-LRB-', 'Supplementary', 'Table', '3', '-RRB-', '.', 'This', 'integration', 'markedly', 'improved', 'the', 'D.', 'simulans', 'assembly', ':', 'the', 'N50', 'contig', 'size', 'of', 'the', 'mosaic', 'assembly', ',', 'for', 'instance', ',', 'is', 'more', 'than', 'twice', 'that', 'of', 'the', 'initial', 'w501', 'assembly', '-LRB-', '17']
Document BIO Tags: ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'B', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
Extractive/present Keyphrases: ['phylogeny', 'drosophila', 'evolution', 'fly', 'genomics']
Abstractive/absent Keyphrases: ['droso', 'comparative genomics']
-----------
```
### Keyphrase Extraction
```python
from datasets import load_dataset
# get the dataset only for keyphrase extraction
dataset = load_dataset("midas/citeulike180", "extraction")
print("Samples for Keyphrase Extraction")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("\n-----------\n")
```
### Keyphrase Generation
```python
# get the dataset only for keyphrase generation
dataset = load_dataset("midas/citeulike180", "generation")
print("Samples for Keyphrase Generation")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
## Citation Information
```
@inproceedings{medelyan-etal-2009-human,
title = "Human-competitive tagging using automatic keyphrase extraction",
author = "Medelyan, Olena and
Frank, Eibe and
Witten, Ian H.",
booktitle = "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
month = aug,
year = "2009",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D09-1137",
pages = "1318--1327",
}
```
## Contributions
Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax) and [@ad6398](https://github.com/ad6398) for adding this dataset
|
mideind/icelandic-common-crawl-corpus-IC3 | 2022-10-22T15:44:37.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:is",
"license:unknown",
"region:us"
] | mideind | null | null | null | 0 | 7 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- is
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: Icelandic Common Crawl Corpus - IC3
---
This is the Icelandic Common Crawl Corpus (IC3).
|
mnemlaghi/widdd | 2022-10-22T15:02:03.000Z | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"arxiv:1810.09164",
"region:us"
] | mnemlaghi | WiDDD stands for WIkiData Disambig with Descriptions. The former dataset comes from [Cetoli & al](https://arxiv.org/pdf/1810.09164.pdf) paper, and is aimed at solving Named Entity Disambiguation. This datasets tries to extract relevant information from entities descriptions only, instead of working with graphs. In order to do so, we mapped every Wikidata id (correct id and wrong id) in the original paper with its WikiData description. If not found, row is discarded for this version. | \ | null | 1 | 7 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: Wikidisamb Dataset with Descriptions
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- named-entity-disambiguation
task_ids:
- wikidata-disambiguation
---
# Dataset Card for "Widdd"
## Dataset Description
WiDDD stands for WIkiData Disambig with Descriptions. The former dataset comes from [Cetoli & al](https://arxiv.org/pdf/1810.09164.pdf) paper, and is aimed at solving Named Entity Disambiguation. This datasets tries to extract relevant information from entities descriptions only, instead of working with graphs. In order to do so, we mapped every Wikidata id (correct id and wrong id) in the original paper with its WikiData description. If not found, row is discarded for the 1.+ versions.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
english
## Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 46.64 MB
An example of 'train' looks as follows.
```
{'example_id': 11,
'string': 'pausanias',
'text': ' mention the spear, which he would indeed have touched with excitement. But it was being shown in the time of Pausanias in the second century AD. Achilles and ',
'correct_id': 'Q192931',
'wrong_id': 'Q941521',
'correct_description': 'ancient Greek geographer, travel writer and mythographer',
'wrong_description': 'Wikimedia disambiguation page'}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `example_id`: an `int32` feature,
- `string`: a `string` feature,
- `text`: a `string` feature,
- `correct_id`: a `string` feature,
- `wrong_id`: a `string` feature,
- `correct_description`: a `string` feature,
- `wrong_description`: a `string` feature,
### Data Splits
| name |train|validation|test|
|----------|----:|-----:|-----:|
|plain_text|96523|9609|9584|
## 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
### Contributions
|
sentence-transformers/parallel-sentences | 2022-10-19T19:59:59.000Z | [
"region:us"
] | sentence-transformers | null | null | null | 10 | 7 | # Parallel Sentences for 50+ languages
This repository contains parallel sentences (i.e. English + same sentences in other language) for 50+ different languages in a simple tsv.gz format:
```
english_sentences\tsentence_in_other_language
```
Sentences stem from the [OPUS website](https://opus.nlpl.eu/).
The following datasets are included:
- [Europarl](https://opus.nlpl.eu/Europarl.php)
- [GlobalVoices](https://opus.nlpl.eu/GlobalVoices.php)
- [JW300](https://opus.nlpl.eu/JW300.php)
- [MUSE](https://github.com/facebookresearch/MUSE)
- [News-Commentary](https://opus.nlpl.eu/News-Commentary.php)
- [OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles.php)
- [Tatoeba](https://tatoeba.org/)
- Talks - Custom translated transcripts of talks
- [WikiMatrix](https://opus.nlpl.eu/WikiMatrix.php)
- WikiTitles - Custom dataset with parallel Wikipedia titles
## Usage
These sentences can be used to train multi-lingual sentence embedding models. For more details, see [SBERT.net - Multilingual-Model](https://www.sbert.net/examples/training/multilingual/README.html)
**This dataset can not yet be used with Hugging Face dataset library. You must download the individual TSV files.**
|
superb/superb-data | 2021-07-21T16:04:51.000Z | [
"region:us"
] | superb | null | null | null | 4 | 7 | Entry not found |
usc-isi/WikiConvert | 2022-10-24T17:40:43.000Z | [
"task_categories:fill-mask",
"task_categories:other",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia",
"language:en",
... | usc-isi | Language Modelling with Cardinal Number Annotations. | @inproceedings{thawani-etal-2021-numeracy,
title = "Numeracy enhances the Literacy of Language Models",
author = "Thawani, Avijit and
Pujara, Jay and
Ilievski, Filip",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.557",
pages = "6960--6967",
abstract = "Specialized number representations in NLP have shown improvements on numerical reasoning tasks like arithmetic word problems and masked number prediction. But humans also use numeracy to make better sense of world concepts, e.g., you can seat 5 people in your {`}room{'} but not 500. Does a better grasp of numbers improve a model{'}s understanding of other concepts and words? This paper studies the effect of using six different number encoders on the task of masked word prediction (MWP), as a proxy for evaluating literacy. To support this investigation, we develop Wiki-Convert, a 900,000 sentence dataset annotated with numbers and units, to avoid conflating nominal and ordinal number occurrences. We find a significant improvement in MWP for sentences containing numbers, that exponent embeddings are the best number encoders, yielding over 2 points jump in prediction accuracy over a BERT baseline, and that these enhanced literacy skills also generalize to contexts without annotated numbers. We release all code at https://git.io/JuZXn.",
} | null | 4 | 7 | ---
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- fill-mask
- other
- text-generation
task_ids:
- language-modeling
- masked-language-modeling
pretty_name: Wiki-Convert
YAML tags:
- {}
- found
language_bcp47:
- en-US
tags:
- numeracy
- natural-language-understanding
- tokenization
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Github](https://github.com/avi-jit/numeracy-literacy)
- **Paper:** [Anthology](https://aclanthology.org/2021.emnlp-main.557)
- **Point of Contact:** [Avijit Thawani](mailto:thawani@isi.edu)
### Dataset Summary
Wiki-Convert is a 900,000+ sentences dataset of precise number annotations from English Wikipedia. It relies on Wiki contributors' annotations in the form of a [{{Convert}}](https://en.wikipedia.org/wiki/Template:Convert) template.
### Supported Tasks and Leaderboards
- `sequence-modeling`: The dataset can be used to train a model for [Language Mddeling], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a low [perplexity](https://huggingface.co/transformers/perplexity.html).
### Languages
The dataset is extracted from English Wikipedia, hence overwhelmingly contains English text.
## Dataset Structure
### Data Instances
Each row in the json file contains metadata about the source Wikipedia sentence, along with annotations for a single number, e.g., `number: 10` in the below example. The annotations are inspired by Numeracy-600K and are in the form of `length` and `offset` from the beginning of the sentence.
```
{
'id': 1080801, 'UNIQUE_STORY_INDEX': '1080801', 'offset': 83, 'length': 2, 'magnitude': 0, 'comment': "Like all Type UB III submarines, UB-117 carried 10 torpedoes and was armed with a 10 cms deck gun. ''", 'number': 10
}
```
Please refer to https://github.com/avi-jit/numeracy-literacy for more details.
### Data Splits
| | Tain | Dev | Test |
| ----- | :------: | :-----: | :----: |
| Input Sentences | 739,583 | 92,447 | 92,449|
## License
Provided under MIT License.
## Citation
```
@inproceedings{thawani-etal-2021-numeracy,
title = "Numeracy enhances the Literacy of Language Models",
author = "Thawani, Avijit and
Pujara, Jay and
Ilievski, Filip",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.557",
pages = "6960--6967",
abstract = "Specialized number representations in NLP have shown improvements on numerical reasoning tasks like arithmetic word problems and masked number prediction. But humans also use numeracy to make better sense of world concepts, e.g., you can seat 5 people in your {`}room{'} but not 500. Does a better grasp of numbers improve a model{'}s understanding of other concepts and words? This paper studies the effect of using six different number encoders on the task of masked word prediction (MWP), as a proxy for evaluating literacy. To support this investigation, we develop Wiki-Convert, a 900,000 sentence dataset annotated with numbers and units, to avoid conflating nominal and ordinal number occurrences. We find a significant improvement in MWP for sentences containing numbers, that exponent embeddings are the best number encoders, yielding over 2 points jump in prediction accuracy over a BERT baseline, and that these enhanced literacy skills also generalize to contexts without annotated numbers. We release all code at https://git.io/JuZXn.",
}
```
Thanks to [@avi-jit](https://github.com/avi-jit) for adding this dataset. |
valurank/PoliticalBias | 2022-10-21T13:38:13.000Z | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | valurank | null | null | null | 3 | 7 | ---
license:
- other
language:
- en
multilinguality:
- monolingual
task_categories:
- classification
task_ids:
- classification
---
# Dataset Card for PoliticalBias
## Table of Contents
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Source Data](#source-data)
## Dataset Description
roughly 8200 articles written by the website’s editors, each article covering one topic with 3 links that describe the same piece of news from different angles (usually one from the right, one from the left, and one from the center)
## Languages
The text in the dataset is in English
## Dataset Structure
The dataset consists of four columns namely Left, Right, Center, and Main URL
## Source Data
The dataset is scrapped from http://allsides.com/
|
vasudevgupta/natural-questions-validation | 2021-05-04T18:25:07.000Z | [
"region:us"
] | vasudevgupta | null | null | null | 0 | 7 | Obtained using following code:
```python
from datasets import load_dataset
dataset = load_dataset("natural_questions", split="validation")
dataset.save_to_disk("natural-questions-validation")
``` |
yuvalkirstain/summ_screen_fd_t5 | 2022-01-09T06:22:00.000Z | [
"region:us"
] | yuvalkirstain | null | null | null | 0 | 7 | Entry not found |
zj88zj/PubMed_200k_RCT | 2021-12-11T18:12:48.000Z | [
"region:us"
] | zj88zj | null | null | null | 2 | 7 | Entry not found |
zloelias/lenta-ru | 2021-11-30T21:43:38.000Z | [
"region:us"
] | zloelias | null | null | null | 0 | 7 | Entry not found |
ai4bharat/IndicParaphrase | 2022-10-13T06:08:55.000Z | [
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:as",
"language:bn",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:or",
"language:pa",
... | ai4bharat | This is the paraphrasing dataset released as part of IndicNLG Suite. Each
input is paired with up to 5 references. We create this dataset in eleven
languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. The total
size of the dataset is 5.57M. | @inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
url = "https://arxiv.org/abs/2203.05437"
} | null | 1 | 7 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: IndicParaphrase
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- conditional-text-generation
task_ids:
- conditional-text-generation-other-paraphrase-generation
---
# Dataset Card for "IndicParaphrase"
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://indicnlp.ai4bharat.org/indicnlg-suite
- **Paper:** [IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages](https://arxiv.org/abs/2203.05437)
- **Point of Contact:**
### Dataset Summary
IndicParaphrase is the paraphrasing dataset released as part of IndicNLG Suite. Each
input is paired with up to 5 references. We create this dataset in eleven
languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. The total
size of the dataset is 5.57M.
### Supported Tasks and Leaderboards
**Tasks:** Paraphrase generation
**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
One example from the `hi` dataset is given below in JSON format.
```
{
'id': '1',
'input': 'निजी क्षेत्र में प्रदेश की 75 प्रतिशत नौकरियां हरियाणा के युवाओं के लिए आरक्षित की जाएगी।',
'references': ['प्रदेश के युवाओं को निजी उद्योगों में 75 प्रतिशत आरक्षण देंगे।',
'युवाओं के लिए हरियाणा की सभी प्राइवेट नौकरियों में 75 प्रतिशत आरक्षण लागू किया जाएगा।',
'निजी क्षेत्र में 75 प्रतिशत आरक्षित लागू कर प्रदेश के युवाओं का रोजगार सुनिश्चत किया जाएगा।',
'प्राईवेट कम्पनियों में हरियाणा के नौजवानों को 75 प्रतिशत नौकरियां में आरक्षित की जाएगी।',
'प्रदेश की प्राइवेट फैक्टरियों में 75 फीसदी रोजगार हरियाणा के युवाओं के लिए आरक्षित किए जाएंगे।'],
'target': 'प्रदेश के युवाओं को निजी उद्योगों में 75 प्रतिशत आरक्षण देंगे।'
}
```
### Data Fields
- `id (string)`: Unique identifier.
- `pivot (string)`: English sentence used as the pivot
- `input (string)`: Input sentence
- `references (list of strings)`: Paraphrases of `input`, ordered according to the least n-gram overlap
- `target (string)`: The first reference (most dissimilar paraphrase)
### Data Splits
We first select 10K instances each for the validation and test and put remaining in the training dataset. `Assamese (as)`, due to its low-resource nature, could only be split into validation and test sets with 4,420 examples each.
Individual dataset with train-dev-test example counts are given below:
Language | ISO 639-1 Code |Train | Dev | Test |
--------------|----------------|-------|-----|------|
Assamese | as | - | 4,420 | 4,420 |
Bengali | bn | 890,445 | 10,000 | 10,000 |
Gujarati | gu | 379,202 | 10,000 | 10,000 |
Hindi | hi | 929,507 | 10,000 | 10,000 |
Kannada | kn | 522,148 | 10,000 | 10,000 |
Malayalam | ml |761,933 | 10,000 | 10,000 |
Marathi | mr |406,003 | 10,000 | 10,000 |
Oriya | or | 105,970 | 10,000 | 10,000 |
Punjabi | pa | 266,704 | 10,000 | 10,000 |
Tamil | ta | 497,798 | 10,000 | 10,000 |
Telugu | te | 596,283 | 10,000 | 10,000 |
## Dataset Creation
### Curation Rationale
[More information needed]
### Source Data
[Samanantar dataset](https://indicnlp.ai4bharat.org/samanantar/)
#### Initial Data Collection and Normalization
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
#### Who are the source language producers?
[Detailed in the paper](https://arxiv.org/abs/2203.05437)
### Annotations
[More information needed]
#### Annotation process
[More information needed]
#### Who are the annotators?
[More information needed]
### Personal and Sensitive Information
[More information needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More information needed]
### Discussion of Biases
[More information needed]
### Other Known Limitations
[More information needed]
## Additional Information
### Dataset Curators
[More information needed]
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
If you use any of the datasets, models or code modules, please cite the following paper:
```
@inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
url = "https://arxiv.org/abs/2203.05437"
}
```
### Contributions
|
metashift | 2023-01-25T15:03:59.000Z | [
"task_categories:image-classification",
"task_categories:other",
"task_ids:multi-label-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-b... | null | The MetaShift is a dataset of datasets for evaluating distribution shifts and training conflicts.
The MetaShift dataset is a collection of 12,868 sets of natural images across 410 classes.
It was created for understanding the performance of a machine learning model across diverse data distributions. | @InProceedings{liang2022metashift,
title={MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts},
author={Weixin Liang and James Zou},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=MTex8qKavoS}
} | null | 2 | 7 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
- other
task_ids:
- multi-label-image-classification
paperswithcode_id: metashift
pretty_name: MetaShift
tags:
- domain-generalization
dataset_info:
features:
- name: image_id
dtype: string
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': cat
'1': dog
'2': bus
'3': truck
'4': elephant
'5': horse
'6': bowl
'7': cup
- name: context
dtype: string
config_name: metashift
splits:
- name: train
num_bytes: 16333509
num_examples: 86808
download_size: 21878013674
dataset_size: 16333509
---
# Dataset Card for MetaShift
## 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:** [MetaShift homepage](https://metashift.readthedocs.io/)
- **Repository:** [MetaShift repository](https://github.com/Weixin-Liang/MetaShift)
- **Paper:** [MetaShift paper](https://arxiv.org/abs/2202.06523v1)
- **Point of Contact:** [Weixin Liang](mailto:wxliang@stanford.edu)
### Dataset Summary
The MetaShift dataset is a collection of 12,868 sets of natural images across 410 classes. It was created for understanding the performance of a machine learning model across diverse data distributions.
The authors leverage the natural heterogeneity of Visual Genome and its annotations to construct MetaShift.
The key idea is to cluster images using its metadata which provides context for each image.
For example : cats with cars or cats in bathroom.
The main advantage is the dataset contains many more coherent sets of data compared to other benchmarks.
Two important benefits of MetaShift :
- Contains orders of magnitude more natural data shifts than previously available.
- Provides explicit explanations of what is unique about each of its data sets and a distance score that measures the amount of distribution shift between any two of its data sets.
### Dataset Usage
The dataset has the following configuration parameters:
- selected_classes: `list[string]`, optional, list of the classes to generate the MetaShift dataset for. If `None`, the list is equal to `['cat', 'dog', 'bus', 'truck', 'elephant', 'horse']`.
- attributes_dataset: `bool`, default `False`, if `True`, the script generates the MetaShift-Attributes dataset. Refer [MetaShift-Attributes Dataset](https://github.com/Weixin-Liang/MetaShift#bonus-generate-the-metashift-attributes-dataset-subsets-defined-by-subject-attributes) for more information.
- attributes: `list[string]`, optional, list of attributes classes included in the Attributes dataset. If `None` and `attributes_dataset` is `True`, it's equal to `["cat(orange)", "cat(white)", "dog(sitting)", "dog(jumping)"]`. You can find the full attribute ontology in the above link.
- with_image_metadata: `bool`, default `False`, whether to include image metadata. If set to `True`, this will give additional metadata about each image. See [Scene Graph](https://cs.stanford.edu/people/dorarad/gqa/download.html) for more information.
- image_subset_size_threshold: `int`, default `25`, the number of images required to be considered a subset. If the number of images is less than this threshold, the subset is ignored.
- min_local_groups: `int`, default `5`, the minimum number of local groups required to be considered an object class.
Consider the following examples to get an idea of how you can use the configuration parameters :
1. To generate the MetaShift Dataset :
```python
load_dataset("metashift", selected_classes=['cat', 'dog', 'bus'])
```
The full object vocabulary and its hierarchy can be seen [here](https://github.com/Weixin-Liang/MetaShift/blob/main/dataset/meta_data/class_hierarchy.json).
The default classes are `['cat', 'dog', 'bus', 'truck', 'elephant', 'horse']`
2. To generate the MetaShift-Attributes Dataset (subsets defined by subject attributes) :
```python
load_dataset("metashift", attributes_dataset = True, attributes=["dog(smiling)", "cat(resting)"])
```
The default attributes are `["cat(orange)", "cat(white)", "dog(sitting)", "dog(jumping)"]`
3. To generate the dataset with additional image metadata information :
```python
load_dataset("metashift", selected_classes=['cat', 'dog', 'bus'], with_image_metadata=True)
```
4. Further, you can specify your own configuration different from those used in the papers as follows:
```python
load_dataset("metashift", image_subset_size_threshold=20, min_local_groups=3)
```
### Dataset Meta-Graphs
From the MetaShift Github Repo :
> MetaShift splits the data points of each class (e.g., Cat) into many subsets based on visual contexts. Each node in the meta-graph represents one subset. The weight of each edge is the overlap coefficient between the corresponding two subsets. Node colors indicate the graph-based community detection results. Inter-community edges are colored. Intra-community edges are grayed out for better visualization. The border color of each example image indicates its community in the meta-graph. We have one such meta-graph for each of the 410 classes in the MetaShift.
The following are the metagraphs for the default classes, these have been generated using the `generate_full_MetaShift.py` file.
<p align='center'>
<img width='75%' src='https://i.imgur.com/wrpezCK.jpg' alt="Cat Meta-graph" /> </br>
<b>Figure: Meta-graph: visualizing the diverse data distributions within the “cat” class. </b>
</p>
<p align='center'>
<img width='75%' src='https://i.imgur.com/FhuAwfT.jpg' alt="Dog Meta-graph" /> </br>
<b>Figure: Meta-graph for the “Dog” class, which captures meaningful semantics of the multi-modal data distribution of “Dog”. </b>
</p>
<p align='center'>
<img width='75%' src='https://i.imgur.com/FFCcN6L.jpg' alt="Bus Meta-graph" /> </br>
<b>Figure: Meta-graph for the “Bus” class. </b>
</p>
<p align='center'>
<img width='75%' src='https://i.imgur.com/rx5b5Vo.jpg' alt="Elephant Meta-graph" /> </br>
<b>Figure: Meta-graph for the "Elephant" class. </b>
</p>
<p align='center'>
<img width='75%' src='https://i.imgur.com/6f6U3S8.jpg' alt="Horse Meta-graph" /> </br>
<b>Figure: Meta-graph for the "Horse" class. </b>
</p>
<p align='center'>
<img width='75%' src='https://i.imgur.com/x9zhQD7.jpg' alt="Truck Meta-graph"/> </br>
<b>Figure: Meta-graph for the Truck class. </b>
</p>
### Supported Tasks and Leaderboards
From the paper:
> MetaShift supports evaluation on both :
> - domain generalization and subpopulation shifts settings,
> - assessing training conflicts.
### Languages
All the classes and subsets use English as their primary language.
## Dataset Structure
### Data Instances
A sample from the MetaShift dataset is provided below:
```
{
'image_id': '2411520',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x7F99115B8D90>,
'label': 2,
'context': 'fence'
}
```
A sample from the MetaShift-Attributes dataset is provided below:
```
{
'image_id': '2401643',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x333 at 0x7FED371CE350>
'label': 0
}
```
The format of the dataset with image metadata included by passing `with_image_metadata=True` to `load_dataset` is provided below:
```
{
'image_id': '2365745',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x333 at 0x7FEBCD39E4D0>
'label': 0,
'context': 'ground',
'width': 500,
'height': 333,
'location': None,
'weather': None,
'objects':
{
'object_id': ['2676428', '3215330', '1962110', '2615742', '3246028', '3232887', '3215329', '1889633', '3882667', '3882663', '1935409', '3882668', '3882669'],
'name': ['wall', 'trailer', 'floor', 'building', 'walkway', 'head', 'tire', 'ground', 'dock', 'paint', 'tail', 'cat', 'wall'],
'x': [194, 12, 0, 5, 3, 404, 27, 438, 2, 142, 324, 328, 224],
'y': [1, 7, 93, 10, 100, 46, 215, 139, 90, 172, 157, 45, 246],
'w': [305, 477, 499, 492, 468, 52, 283, 30, 487, 352, 50, 122, 274],
'h': [150, 310, 72, 112, 53, 59, 117, 23, 240, 72, 107, 214, 85],
'attributes': [['wood', 'green'], [], ['broken', 'wood'], [], [], [], ['black'], [], [], [], ['thick'], ['small'], ['blue']],
'relations': [{'name': [], 'object': []}, {'name': [], 'object': []}, {'name': [], 'object': []}, {'name': [], 'object': []}, {'name': [], 'object': []}, {'name': ['of'], 'object': ['3882668']}, {'name': ['to the left of'], 'object': ['3882669']}, {'name': ['to the right of'], 'object': ['3882668']}, {'name': [], 'object': []}, {'name': [], 'object': []}, {'name': ['of'], 'object': ['3882668']}, {'name': ['perched on', 'to the left of'], 'object': ['3882667', '1889633']}, {'name': ['to the right of'], 'object': ['3215329']}]
}
}
```
### Data Fields
- `image_id`: Unique numeric ID of the image in Base Visual Genome dataset.
- `image`: A PIL.Image.Image object containing the image.
- `label`: an int classification label.
- `context`: represents the context in which the label is seen. A given label could have multiple contexts.
Image Metadata format can be seen [here](https://cs.stanford.edu/people/dorarad/gqa/download.html) and a sample above has been provided for reference.
### Data Splits
All the data is contained in training set.
## Dataset Creation
### Curation Rationale
From the paper:
> We present MetaShift as an important resource for studying the behavior of
ML algorithms and training dynamics across data with heterogeneous contexts. In order to assess the reliability and fairness of a model, we need to evaluate
its performance and training behavior across heterogeneous types of data. MetaShift contains many more coherent sets of data compared to other benchmarks. Importantly, we have explicit annotations of what makes each subset unique (e.g. cats with cars or dogs next to a bench) as well as a score that measures the distance between any two subsets, which is not available in previous benchmarks of natural data.
### Source Data
#### Initial Data Collection and Normalization
From the paper:
> We leverage the natural heterogeneity of Visual Genome and its annotations to construct MetaShift. Visual Genome contains over 100k images across 1,702 object classes. MetaShift is constructed on a class-by-class basis. For each class, say “cat”, we pull out all cat images and proceed with generating candidate subests, constructing meta-graphs and then duantify distances of distribution shifts.
#### Who are the source language producers?
[More Information Needed]
### Annotations
The MetaShift dataset uses Visual Genome as its base, therefore the annotations process is same as the Visual Genome dataset.
#### Annotation process
From the Visual Genome paper :
> We used Amazon Mechanical Turk (AMT) as our primary source of annotations. Overall, a total of over 33,000 unique workers contributed to the dataset. The dataset was collected over the course of 6 months after 15 months of experimentation and iteration on the data representation. Approximately 800, 000 Human Intelligence Tasks (HITs) were launched on AMT, where each HIT involved creating descriptions, questions and answers, or region graphs.
#### Who are the annotators?
From the Visual Genome paper :
> Visual Genome was collected and verified entirely by crowd workers from Amazon Mechanical Turk.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
From the paper:
> One limitation is that our MetaShift might inherit existing biases in Visual Genome, which is the
base dataset of our MetaShift. Potential concerns include minority groups being under-represented
in certain classes (e.g., women with snowboard), or annotation bias where people in images are
by default labeled as male when gender is unlikely to be identifiable. Existing work in analyzing,
quantifying, and mitigating biases in general computer vision datasets can help with addressing this
potential negative societal impact.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
From the paper :
> Our MetaShift and the code would use the Creative Commons Attribution 4.0 International License. Visual Genome (Krishna et al., 2017) is licensed under a Creative Commons Attribution 4.0 International License. MS-COCO (Lin et al., 2014) is licensed under CC-BY 4.0. The Visual Genome dataset uses 108, 077 images from the intersection of the YFCC100M (Thomee et al., 2016) and MS-COCO. We use the pre-processed and cleaned version of Visual Genome by GQA (Hudson & Manning, 2019).
### Citation Information
```bibtex
@InProceedings{liang2022metashift,
title={MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts},
author={Weixin Liang and James Zou},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=MTex8qKavoS}
}
```
### Contributions
Thanks to [@dnaveenr](https://github.com/dnaveenr) for adding this dataset. |
israel/Amharic-News-Text-classification-Dataset | 2022-04-06T09:27:52.000Z | [
"license:cc-by-4.0",
"arxiv:2103.05639",
"region:us"
] | israel | null | null | null | 0 | 7 | ---
license: cc-by-4.0
---
# An Amharic News Text classification Dataset
> In NLP, text classification is one of the primary problems we try to solve and its uses in language analyses are indisputable. The lack of labeled training data made it harder to do these tasks in low resource languages like Amharic. The task of collecting, labeling, annotating, and making valuable this kind of data will encourage junior researchers, schools, and machine learning practitioners to implement existing classification models in their language. In this short paper, we aim to introduce the Amharic text classification dataset that consists of more than 50k news articles that were categorized into 6 classes. This dataset is made available with easy baseline performances to encourage studies and better performance experiments.
```
@misc{https://doi.org/10.48550/arxiv.2103.05639,
doi = {10.48550/ARXIV.2103.05639},
url = {https://arxiv.org/abs/2103.05639},
author = {Azime, Israel Abebe and Mohammed, Nebil},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {An Amharic News Text classification Dataset},
publisher = {arXiv},
year = {2021},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
Lexi/spanextract | 2022-10-25T10:08:42.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-4.0",
... | Lexi | null | null | null | 0 | 7 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: squad
pretty_name: SQuAD
---
# Dataset Card for "squad"
## 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://rajpurkar.github.io/SQuAD-explorer/](https://rajpurkar.github.io/SQuAD-explorer/)
- **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:** 33.51 MB
- **Size of the generated dataset:** 85.75 MB
- **Total amount of disk used:** 119.27 MB
### Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 33.51 MB
- **Size of the generated dataset:** 85.75 MB
- **Total amount of disk used:** 119.27 MB
An example of 'train' looks as follows.
```
{
"answers": {
"answer_start": [1],
"text": ["This is a test text"]
},
"context": "This is a test context.",
"id": 1,
"question": "Is this a test?",
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `int32` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name |train|validation|
|----------|----:|---------:|
|plain_text|---| ---|
## 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)
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
cfilt/HiNER-collapsed | 2023-03-07T16:32:27.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:hi",
"license:cc-by-sa-4.0",
"arxiv:2204.137... | cfilt | This is the repository for HiNER - a large Hindi Named Entity Recognition dataset. | XX | null | 0 | 7 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- hi
license: "cc-by-sa-4.0"
multilinguality:
- monolingual
paperswithcode_id: hiner-collapsed-1
pretty_name: HiNER - Large Hindi Named Entity Recognition dataset
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
<p align="center"><img src="https://huggingface.co/datasets/cfilt/HiNER-collapsed/raw/main/cfilt-dark-vec.png" alt="Computation for Indian Language Technology Logo" width="150" height="150"/></p>
# Dataset Card for HiNER-original
[](https://twitter.com/cfiltnlp)
[](https://twitter.com/PeopleCentredAI)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/cfiltnlp/HiNER
- **Repository:** https://github.com/cfiltnlp/HiNER
- **Paper:** https://arxiv.org/abs/2204.13743
- **Leaderboard:** https://paperswithcode.com/sota/named-entity-recognition-on-hiner-collapsed
- **Point of Contact:** Rudra Murthy V
### Dataset Summary
This dataset was created for the fundamental NLP task of Named Entity Recognition for the Hindi language at CFILT Lab, IIT Bombay. We gathered the dataset from various government information webpages and manually annotated these sentences as a part of our data collection strategy.
**Note:** The dataset contains sentences from ILCI and other sources. ILCI dataset requires license from Indian Language Consortium due to which we do not distribute the ILCI portion of the data. Please send us a mail with proof of ILCI data acquisition to obtain the full dataset.
### Supported Tasks and Leaderboards
Named Entity Recognition
### Languages
Hindi
## Dataset Structure
### Data Instances
{'id': '0', 'tokens': ['प्राचीन', 'समय', 'में', 'उड़ीसा', 'को', 'कलिंग', 'के', 'नाम', 'से', 'जाना', 'जाता', 'था', '।'], 'ner_tags': [0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0]}
### Data Fields
- `id`: The ID value of the data point.
- `tokens`: Raw tokens in the dataset.
- `ner_tags`: the NER tags for this dataset.
### Data Splits
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| original | 76025 | 10861 | 21722|
| collapsed | 76025 | 10861 | 21722|
## About
This repository contains the Hindi Named Entity Recognition dataset (HiNER) published at the Langauge Resources and Evaluation conference (LREC) in 2022. A pre-print via arXiv is available [here](https://arxiv.org/abs/2204.13743).
### Recent Updates
* Version 0.0.5: HiNER initial release
## 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 original dataset with all the tags, please use:<br/>
```python
from datasets import load_dataset
hiner = load_dataset('cfilt/HiNER-original')
```
To use the collapsed dataset with only PER, LOC, and ORG tags, please use:<br/>
```python
from datasets import load_dataset
hiner = load_dataset('cfilt/HiNER-collapsed')
```
However, the CoNLL format dataset files can also be found on this Git repository under the [data](data/) folder.
## Model(s)
Our best performing models are hosted on the HuggingFace models repository:
1. [HiNER-Collapsed-XLM-R](https://huggingface.co/cfilt/HiNER-Collapse-XLM-Roberta-Large)
2. [HiNER-Original-XLM-R](https://huggingface.co/cfilt/HiNER-Original-XLM-Roberta-Large)
## Dataset Creation
### Curation Rationale
HiNER was built on data extracted from various government websites handled by the Government of India which provide information in Hindi. This dataset was built for the task of Named Entity Recognition. The dataset was introduced to introduce new resources to the Hindi language that was under-served for Natural Language Processing.
### Source Data
#### Initial Data Collection and Normalization
HiNER was built on data extracted from various government websites handled by the Government of India which provide information in Hindi
#### Who are the source language producers?
Various Government of India webpages
### Annotations
#### Annotation process
This dataset was manually annotated by a single annotator of a long span of time.
#### Who are the annotators?
Pallab Bhattacharjee
### Personal and Sensitive Information
We ensured that there was no sensitive information present in the dataset. All the data points are curated from publicly available information.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to provide a large Hindi Named Entity Recognition dataset. 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
Any biases contained in the data released by the Indian government are bound to be present in our data.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Pallab Bhattacharjee
### Licensing Information
CC-BY-SA 4.0
### Citation Information
```latex
@misc{https://doi.org/10.48550/arxiv.2204.13743,
doi = {10.48550/ARXIV.2204.13743},
url = {https://arxiv.org/abs/2204.13743},
author = {Murthy, Rudra and Bhattacharjee, Pallab and Sharnagat, Rahul and Khatri, Jyotsana and Kanojia, Diptesh and Bhattacharyya, Pushpak},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {HiNER: A Large Hindi Named Entity Recognition Dataset},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
``` |
pietrolesci/dnc | 2022-04-25T08:59:06.000Z | [
"region:us"
] | pietrolesci | null | null | null | 0 | 7 | ## Overview
Original dataset [here](https://github.com/decompositional-semantics-initiative/DNC).
This dataset has been proposed in [Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation](https://www.aclweb.org/anthology/D18-1007/).
## Dataset curation
This version of the dataset does not include the `type-of-inference` "KG" as its label set is
`[1, 2, 3, 4, 5]` while here we focus on NLI-related label sets, i.e. `[entailed, not-entailed]`.
For this reason, I named the dataset DNLI for _Diverse_ NLI, as in [Liu et al 2020](https://aclanthology.org/2020.conll-1.48/), instead of DNC.
This version of the dataset contains columns from the `*_data.json` and the `*_metadata.json` files available in the repo.
In the original repo, each data file has the following keys and values:
- `context`: The context sentence for the NLI pair. The context is already tokenized.
- `hypothesis`: The hypothesis sentence for the NLI pair. The hypothesis is already tokenized.
- `label`: The label for the NLI pair
- `label-set`: The set of possible labels for the specific NLI pair
- `binary-label`: A `True` or `False` label. See the paper for details on how we convert the `label` into a binary label.
- `split`: This can be `train`, `dev`, or `test`.
- `type-of-inference`: A string indicating what type of inference is tested in this example.
- `pair-id`: A unique integer id for the NLI pair. The `pair-id` is used to find the corresponding metadata for any given NLI pair
while each metadata file has the following columns
- `pair-id`: A unique integer id for the NLI pair.
- `corpus`: The original corpus where this example came from.
- `corpus-sent-id`: The id of the sentence (or example) in the original dataset that we recast.
- `corpus-license`: The license for the data from the original dataset.
- `creation-approach`: Determines the method used to recast this example. Options are `automatic`, `manual`, or `human-labeled`.
- `misc`: A dictionary of other relevant information. This is an optional field.
The files are merged on the `pair-id` key. I **do not** include the `misc` column as it is not essential for NLI.
NOTE: the label mapping is **not** the custom (i.e., 3 class) for NLI tasks. They used a binary target and I encoded them
with the following mapping `{"not-entailed": 0, "entailed": 1}`.
NOTE: some instances are present in multiple splits (matching performed by exact matching on "context", "hypothesis", and "label").
## Code to create the dataset
```python
import pandas as pd
from datasets import Dataset, ClassLabel, Value, Features, DatasetDict, Sequence
from pathlib import Path
paths = {
"train": "<path_to_folder>/DNC-master/train",
"dev": "<path_to_folder>/DNC-master/dev",
"test": "<path_to_folder>/DNC-master/test",
}
# read all data files
dfs = []
for split, path in paths.items():
for f_name in Path(path).rglob("*_data.json"):
df = pd.read_json(str(f_name))
df["file_split_data"] = split
dfs.append(df)
data = pd.concat(dfs, ignore_index=False, axis=0)
# read all metadata files
meta_dfs = []
for split, path in paths.items():
for f_name in Path(path).rglob("*_metadata.json"):
df = pd.read_json(str(f_name))
meta_dfs.append(df)
metadata = pd.concat(meta_dfs, ignore_index=False, axis=0)
# merge
dataset = pd.merge(data, metadata, on="pair-id", how="left")
# check that the split column reflects file splits
assert sum(dataset["split"] != dataset["file_split_data"]) == 0
dataset = dataset.drop(columns=["file_split_data"])
# fix `binary-label` column
dataset.loc[~dataset["label"].isin(["entailed", "not-entailed"]), "binary-label"] = False
dataset.loc[dataset["label"].isin(["entailed", "not-entailed"]), "binary-label"] = True
# fix datatype
dataset["corpus-sent-id"] = dataset["corpus-sent-id"].astype(str)
# order columns as shown in the README.md
columns = [
"context",
"hypothesis",
"label",
"label-set",
"binary-label",
"split",
"type-of-inference",
"pair-id",
"corpus",
"corpus-sent-id",
"corpus-license",
"creation-approach",
"misc",
]
dataset = dataset.loc[:, columns]
# remove misc column
dataset = dataset.drop(columns=["misc"])
# remove KG for NLI
dataset.loc[(dataset["label"].isin([1, 2, 3, 4, 5])), "type-of-inference"].value_counts()
# > the only split with label-set [1, 2, 3, 4, 5], so remove as we focus on NLI
dataset = dataset.loc[~(dataset["type-of-inference"] == "KG")]
# encode labels
dataset["label"] = dataset["label"].map({"not-entailed": 0, "entailed": 1})
# fill NA in label-set
dataset["label-set"] = dataset["label-set"].ffill()
features = Features(
{
"context": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=2, names=["not-entailed", "entailed"]),
"label-set": Sequence(length=2, feature=Value(dtype="string")),
"binary-label": Value(dtype="bool"),
"split": Value(dtype="string"),
"type-of-inference": Value(dtype="string"),
"pair-id": Value(dtype="int64"),
"corpus": Value(dtype="string"),
"corpus-sent-id": Value(dtype="string"),
"corpus-license": Value(dtype="string"),
"creation-approach": Value(dtype="string"),
}
)
dataset_splits = {}
for split in ("train", "dev", "test"):
df_split = dataset.loc[dataset["split"] == split]
dataset_splits[split] = Dataset.from_pandas(df_split, features=features)
dataset_splits = DatasetDict(dataset_splits)
dataset_splits.push_to_hub("pietrolesci/dnli", token="<your token>")
# check overlap between splits
from itertools import combinations
for i, j in combinations(dataset_splits.keys(), 2):
print(
f"{i} - {j}: ",
pd.merge(
dataset_splits[i].to_pandas(),
dataset_splits[j].to_pandas(),
on=["context", "hypothesis", "label"],
how="inner",
).shape[0],
)
#> train - dev: 127
#> train - test: 55
#> dev - test: 54
```
|
taln-ls2n/kptimes | 2022-09-23T07:38:28.000Z | [
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"region:us"
] | taln-ls2n | KPTimes benchmark dataset for keyphrase extraction an generation. | @inproceedings{gallina-etal-2019-kptimes,
title = "{KPT}imes: A Large-Scale Dataset for Keyphrase Generation on News Documents",
author = "Gallina, Ygor and
Boudin, Florian and
Daille, Beatrice",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
month = oct # "{--}" # nov,
year = "2019",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-8617",
doi = "10.18653/v1/W19-8617",
pages = "130--135",
abstract = "Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily available for the scholarly domain and include non-expert annotations. In this paper we present KPTimes, a large-scale dataset of news texts paired with editor-curated keyphrases. Exploring the dataset, we show how editors tag documents, and how their annotations differ from those found in existing datasets. We also train and evaluate state-of-the-art neural keyphrase generation models on KPTimes to gain insights on how well they perform on the news domain. The dataset is available online at https:// github.com/ygorg/KPTimes.",
} | null | 1 | 7 | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
task_categories:
- text-mining
- text-generation
task_ids:
- keyphrase-generation
- keyphrase-extraction
size_categories:
- 100K<n<1M
pretty_name: KPTimes
---
# KPTimes Benchmark Dataset for Keyphrase Generation
## About
KPTimes is a dataset for benchmarking keyphrase extraction and generation models.
The dataset is composed of 290K news articles in English collected from the [New York Times](https://www.nytimes.com/) and the [Japan
Times](https://www.japantimes.co.jp/).
Keyphrases were annotated by editors in a semi-automated manner (that is, editors revise a set of keyphrases proposed by an algorithm and provide additional keyphrases).
Details about the dataset can be found in the original paper [(Gallina et al., 2019)][gallina-2019].
Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in [(Boudin and Gallina, 2021)][boudin-2021].
Text pre-processing (tokenization) is carried out using `spacy` (`en_core_web_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
Stemming (Porter's stemmer implementation provided in `nltk`) is applied before reference keyphrases are matched against the source text.
Details about the process can be found in `prmu.py`. <u>P</u>resent keyphrases are ordered according to their first occurrence position in the text.
## Content and statistics
The dataset contains the following test split:
| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
| :--------- | ----------: | -----: | -----------: | --------: | ----------: | ------: | -------: |
| Train | 259,923 | 921 | 5.03 | 45.61 | 15.57 | 29.63 | 9.19 |
| Validation | 10,000 | 921 | 5.02 | 45.22 | 15.78 | 29.60 | 9.41 |
| Test | 20,000 | 648 | 5.03 | 60.64 | 8.90 | 18.95 | 11.51 |
The following data fields are available :
- **id**: unique identifier of the document.
- **title**: title of the document.
- **abstract**: abstract of the document.
- **keyphrases**: list of reference keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
- **date**: publishing date (YYYY/MM/DD)
- **categories**: categories of the article (1 or 2 categories)
## References
- (Gallina et al., 2019) Ygor Gallina, Florian Boudin, and Beatrice Daille. 2019.
[KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents][gallina-2019].
In Proceedings of the 12th International Conference on Natural Language Generation, pages 130–135, Tokyo, Japan. Association for Computational Linguistics.
- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021].
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
[gallina-2019]: https://aclanthology.org/W19-8617/
[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/ |
HuggingFaceM4/charades | 2022-10-20T21:35:42.000Z | [
"task_categories:other",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:1604.01753",
"region:us"
] | HuggingFaceM4 | Charades is dataset composed of 9848 videos of daily indoors activities collected through Amazon Mechanical Turk. 267 different users were presented with a sentence, that includes objects and actions from a fixed vocabulary, and they recorded a video acting out the sentence (like in a game of Charades). The dataset contains 66,500 temporal annotations for 157 action classes, 41,104 labels for 46 object classes, and 27,847 textual descriptions of the videos. | @article{sigurdsson2016hollywood,
author = {Gunnar A. Sigurdsson and G{\"u}l Varol and Xiaolong Wang and Ivan Laptev and Ali Farhadi and Abhinav Gupta},
title = {Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding},
journal = {ArXiv e-prints},
eprint = {1604.01753},
year = {2016},
url = {http://arxiv.org/abs/1604.01753},
} | null | 1 | 7 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: charades
pretty_name: Charades
tags: []
---
# Dataset Card for Charades
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://prior.allenai.org/projects/charades
- **Repository:** https://github.com/gsig/charades-algorithms
- **Paper:** https://arxiv.org/abs/1604.01753
- **Leaderboard:** https://paperswithcode.com/sota/action-classification-on-charades
- **Point of Contact:** mailto: vision.amt@allenai.org
### Dataset Summary
Charades is dataset composed of 9848 videos of daily indoors activities collected through Amazon Mechanical Turk. 267 different users were presented with a sentence, that includes objects and actions from a fixed vocabulary, and they recorded a video acting out the sentence (like in a game of Charades). The dataset contains 66,500 temporal annotations for 157 action classes, 41,104 labels for 46 object classes, and 27,847 textual descriptions of the videos
### Supported Tasks and Leaderboards
- `multilabel-action-classification`: The goal of this task is to classify actions happening in a video. This is a multilabel classification. The leaderboard is available [here](https://paperswithcode.com/sota/action-classification-on-charades)
### Languages
The annotations in the dataset are in English.
## Dataset Structure
### Data Instances
```
{
"video_id": "46GP8",
"video": "/home/amanpreet_huggingface_co/.cache/huggingface/datasets/downloads/extracted/3f022da5305aaa189f09476dbf7d5e02f6fe12766b927c076707360d00deb44d/46GP8.mp4",
"subject": "HR43",
"scene": "Kitchen",
"quality": 6,
"relevance": 7,
"verified": "Yes",
"script": "A person cooking on a stove while watching something out a window.",
"objects": ["food", "stove", "window"],
"descriptions": [
"A person cooks food on a stove before looking out of a window."
],
"labels": [92, 147],
"action_timings": [
[11.899999618530273, 21.200000762939453],
[0.0, 12.600000381469727]
],
"length": 24.829999923706055
}
```
### Data Fields
- `video_id`: `str` Unique identifier for each video.
- `video`: `str` Path to the video file
- `subject`: `str` Unique identifier for each subject in the dataset
- `scene`: `str` One of 15 indoor scenes in the dataset, such as Kitchen
- `quality`: `int` The quality of the video judged by an annotator (7-point scale, 7=high quality), -100 if missing
- `relevance`: `int` The relevance of the video to the script judged by an annotated (7-point scale, 7=very relevant), -100 if missing
- `verified`: `str` 'Yes' if an annotator successfully verified that the video matches the script, else 'No'
- `script`: `str` The human-generated script used to generate the video
- `descriptions`: `List[str]` List of descriptions by annotators watching the video
- `labels`: `List[int]` Multi-label actions found in the video. Indices from 0 to 156.
- `action_timings`: `List[Tuple[int, int]]` Timing where each of the above actions happened.
- `length`: `float` The length of the video in seconds
<details>
<summary>
Click here to see the full list of Charades class labels mapping:
</summary>
|id|Class|
|--|-----|
|c000 | Holding some clothes |
|c001 | Putting clothes somewhere |
|c002 | Taking some clothes from somewhere |
|c003 | Throwing clothes somewhere |
|c004 | Tidying some clothes |
|c005 | Washing some clothes |
|c006 | Closing a door |
|c007 | Fixing a door |
|c008 | Opening a door |
|c009 | Putting something on a table |
|c010 | Sitting on a table |
|c011 | Sitting at a table |
|c012 | Tidying up a table |
|c013 | Washing a table |
|c014 | Working at a table |
|c015 | Holding a phone/camera |
|c016 | Playing with a phone/camera |
|c017 | Putting a phone/camera somewhere |
|c018 | Taking a phone/camera from somewhere |
|c019 | Talking on a phone/camera |
|c020 | Holding a bag |
|c021 | Opening a bag |
|c022 | Putting a bag somewhere |
|c023 | Taking a bag from somewhere |
|c024 | Throwing a bag somewhere |
|c025 | Closing a book |
|c026 | Holding a book |
|c027 | Opening a book |
|c028 | Putting a book somewhere |
|c029 | Smiling at a book |
|c030 | Taking a book from somewhere |
|c031 | Throwing a book somewhere |
|c032 | Watching/Reading/Looking at a book |
|c033 | Holding a towel/s |
|c034 | Putting a towel/s somewhere |
|c035 | Taking a towel/s from somewhere |
|c036 | Throwing a towel/s somewhere |
|c037 | Tidying up a towel/s |
|c038 | Washing something with a towel |
|c039 | Closing a box |
|c040 | Holding a box |
|c041 | Opening a box |
|c042 | Putting a box somewhere |
|c043 | Taking a box from somewhere |
|c044 | Taking something from a box |
|c045 | Throwing a box somewhere |
|c046 | Closing a laptop |
|c047 | Holding a laptop |
|c048 | Opening a laptop |
|c049 | Putting a laptop somewhere |
|c050 | Taking a laptop from somewhere |
|c051 | Watching a laptop or something on a laptop |
|c052 | Working/Playing on a laptop |
|c053 | Holding a shoe/shoes |
|c054 | Putting shoes somewhere |
|c055 | Putting on shoe/shoes |
|c056 | Taking shoes from somewhere |
|c057 | Taking off some shoes |
|c058 | Throwing shoes somewhere |
|c059 | Sitting in a chair |
|c060 | Standing on a chair |
|c061 | Holding some food |
|c062 | Putting some food somewhere |
|c063 | Taking food from somewhere |
|c064 | Throwing food somewhere |
|c065 | Eating a sandwich |
|c066 | Making a sandwich |
|c067 | Holding a sandwich |
|c068 | Putting a sandwich somewhere |
|c069 | Taking a sandwich from somewhere |
|c070 | Holding a blanket |
|c071 | Putting a blanket somewhere |
|c072 | Snuggling with a blanket |
|c073 | Taking a blanket from somewhere |
|c074 | Throwing a blanket somewhere |
|c075 | Tidying up a blanket/s |
|c076 | Holding a pillow |
|c077 | Putting a pillow somewhere |
|c078 | Snuggling with a pillow |
|c079 | Taking a pillow from somewhere |
|c080 | Throwing a pillow somewhere |
|c081 | Putting something on a shelf |
|c082 | Tidying a shelf or something on a shelf |
|c083 | Reaching for and grabbing a picture |
|c084 | Holding a picture |
|c085 | Laughing at a picture |
|c086 | Putting a picture somewhere |
|c087 | Taking a picture of something |
|c088 | Watching/looking at a picture |
|c089 | Closing a window |
|c090 | Opening a window |
|c091 | Washing a window |
|c092 | Watching/Looking outside of a window |
|c093 | Holding a mirror |
|c094 | Smiling in a mirror |
|c095 | Washing a mirror |
|c096 | Watching something/someone/themselves in a mirror |
|c097 | Walking through a doorway |
|c098 | Holding a broom |
|c099 | Putting a broom somewhere |
|c100 | Taking a broom from somewhere |
|c101 | Throwing a broom somewhere |
|c102 | Tidying up with a broom |
|c103 | Fixing a light |
|c104 | Turning on a light |
|c105 | Turning off a light |
|c106 | Drinking from a cup/glass/bottle |
|c107 | Holding a cup/glass/bottle of something |
|c108 | Pouring something into a cup/glass/bottle |
|c109 | Putting a cup/glass/bottle somewhere |
|c110 | Taking a cup/glass/bottle from somewhere |
|c111 | Washing a cup/glass/bottle |
|c112 | Closing a closet/cabinet |
|c113 | Opening a closet/cabinet |
|c114 | Tidying up a closet/cabinet |
|c115 | Someone is holding a paper/notebook |
|c116 | Putting their paper/notebook somewhere |
|c117 | Taking paper/notebook from somewhere |
|c118 | Holding a dish |
|c119 | Putting a dish/es somewhere |
|c120 | Taking a dish/es from somewhere |
|c121 | Wash a dish/dishes |
|c122 | Lying on a sofa/couch |
|c123 | Sitting on sofa/couch |
|c124 | Lying on the floor |
|c125 | Sitting on the floor |
|c126 | Throwing something on the floor |
|c127 | Tidying something on the floor |
|c128 | Holding some medicine |
|c129 | Taking/consuming some medicine |
|c130 | Putting groceries somewhere |
|c131 | Laughing at television |
|c132 | Watching television |
|c133 | Someone is awakening in bed |
|c134 | Lying on a bed |
|c135 | Sitting in a bed |
|c136 | Fixing a vacuum |
|c137 | Holding a vacuum |
|c138 | Taking a vacuum from somewhere |
|c139 | Washing their hands |
|c140 | Fixing a doorknob |
|c141 | Grasping onto a doorknob |
|c142 | Closing a refrigerator |
|c143 | Opening a refrigerator |
|c144 | Fixing their hair |
|c145 | Working on paper/notebook |
|c146 | Someone is awakening somewhere |
|c147 | Someone is cooking something |
|c148 | Someone is dressing |
|c149 | Someone is laughing |
|c150 | Someone is running somewhere |
|c151 | Someone is going from standing to sitting |
|c152 | Someone is smiling |
|c153 | Someone is sneezing |
|c154 | Someone is standing up from somewhere |
|c155 | Someone is undressing |
|c156 | Someone is eating something |
</details>
### Data Splits
| |train |validation| test |
|-------------|------:|---------:|------:|
|# of examples|1281167|50000 |100000 |
## Dataset Creation
### Curation Rationale
> Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. While most of such scenes are not particularly exciting, they typically do not appear on YouTube, in movies or TV broadcasts. So how do we collect sufficiently many diverse but boring samples representing our lives? We propose a novel Hollywood in Homes approach to collect such data. Instead of shooting videos in the lab, we ensure diversity by distributing and crowdsourcing the whole process of video creation from script writing to video recording and annotation.
### Source Data
#### Initial Data Collection and Normalization
> Similar to filming, we have a three-step process for generating a video. The first step is generating the script of the indoor video. The key here is to allow workers to generate diverse scripts yet ensure that we have enough data for each category. The second step in the process is to use the script and ask workers to record a video of that sentence being acted out. In the final step, we ask the workers to verify if the recorded video corresponds to script, followed by an annotation procedure.
#### Who are the source language producers?
Amazon Mechnical Turk annotators
### Annotations
#### Annotation process
> Similar to filming, we have a three-step process for generating a video. The first step is generating the script of the indoor video. The key here is to allow workers to generate diverse scripts yet ensure that we have enough data for each category. The second step in the process is to use the script and ask workers to record a video of that sentence being acted out. In the final step, we ask the workers to verify if the recorded video corresponds to script, followed by an annotation procedure.
#### Who are the annotators?
Amazon Mechnical Turk annotators
### Personal and Sensitive Information
Nothing specifically mentioned in the paper.
## 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
AMT annotators
### Licensing Information
License for Non-Commercial Use
If this software is redistributed, this license must be included. The term software includes any source files, documentation, executables, models, and data.
This software and data is available for general use by academic or non-profit, or government-sponsored researchers. It may also be used for evaluation purposes elsewhere. This license does not grant the right to use this software or any derivation of it in a for-profit enterprise. For commercial use, please contact The Allen Institute for Artificial Intelligence.
This license does not grant the right to modify and publicly release the data in any form.
This license does not grant the right to distribute the data to a third party in any form.
The subjects in this data should be treated with respect and dignity. This license only grants the right to publish short segments or still images in an academic publication where necessary to present examples, experimental results, or observations.
This software comes with no warranty or guarantee of any kind. By using this software, the user accepts full liability.
The Allen Institute for Artificial Intelligence (C) 2016.
### Citation Information
```bibtex
@article{sigurdsson2016hollywood,
author = {Gunnar A. Sigurdsson and G{\"u}l Varol and Xiaolong Wang and Ivan Laptev and Ali Farhadi and Abhinav Gupta},
title = {Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding},
journal = {ArXiv e-prints},
eprint = {1604.01753},
year = {2016},
url = {http://arxiv.org/abs/1604.01753},
}
```
### Contributions
Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset.
|
Leyo/ActivityNet_Captions | 2022-07-01T15:57:56.000Z | [
"task_ids:closed-domain-qa",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10k<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:1705.00754",
"region:us"
] | Leyo | The ActivityNet Captions dataset connects videos to a series of temporally annotated sentence descriptions.
Each sentence covers an unique segment of the video, describing multiple events that occur. These events
may occur over very long or short periods of time and are not limited in any capacity, allowing them to
co-occur. On average, each of the 20k videos contains 3.65 temporally localized sentences, resulting in
a total of 100k sentences. We find that the number of sentences per video follows a relatively normal
distribution. Furthermore, as the video duration increases, the number of sentences also increases.
Each sentence has an average length of 13.48 words, which is also normally distributed. You can find more
details of the dataset under the ActivityNet Captions Dataset section, and under supplementary materials
in the paper. | @inproceedings{krishna2017dense,
title={Dense-Captioning Events in Videos},
author={Krishna, Ranjay and Hata, Kenji and Ren, Frederic and Fei-Fei, Li and Niebles, Juan Carlos},
booktitle={International Conference on Computer Vision (ICCV)},
year={2017}
} | null | 0 | 7 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: ActivityNet Captions
size_categories:
- 10k<n<100K
source_datasets:
- original
task_categories:
- video-captionning
task_ids:
- closed-domain-qa
---
# Dataset Card for ActivityNet Captions
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://cs.stanford.edu/people/ranjaykrishna/densevid/
- **Paper:** https://arxiv.org/abs/1705.00754
### Dataset Summary
The ActivityNet Captions dataset connects videos to a series of temporally annotated sentence descriptions. Each sentence covers an unique segment of the video, describing multiple events that occur. These events may occur over very long or short periods of time and are not limited in any capacity, allowing them to co-occur. On average, each of the 20k videos contains 3.65 temporally localized sentences, resulting in a total of 100k sentences. We find that the number of sentences per video follows a relatively normal distribution. Furthermore, as the video duration increases, the number of sentences also increases. Each sentence has an average length of 13.48 words, which is also normally distributed. You can find more details of the dataset under the ActivityNet Captions Dataset section, and under supplementary materials in the paper.
### Languages
The captions in the dataset are in English.
## Dataset Structure
### Data Fields
- `video_id` : `str` unique identifier for the video
- `video_path`: `str` Path to the video file
-`duration`: `float32` Duration of the video
- `captions_starts`: `List_float32` List of timestamps denoting the time at which each caption starts
- `captions_ends`: `List_float32` List of timestamps denoting the time at which each caption ends
- `en_captions`: `list_str` List of english captions describing parts of the video
### Data Splits
| |train |validation| test | Overall |
|-------------|------:|---------:|------:|------:|
|# of videos|10,009 |4,917 |4,885 |19,811 |
### Annotations
Quoting [ActivityNet Captions' paper](https://arxiv.org/abs/1705.00754): \
"Each annotation task was divided into two steps: (1)
Writing a paragraph describing all major events happening
in the videos in a paragraph, with each sentence of the paragraph describing one event, and (2) Labeling the
start and end time in the video in which each sentence in the
paragraph event occurred."
### Who annotated the dataset?
Amazon Mechnical Turk annotators
### Personal and Sensitive Information
Nothing specifically mentioned in the paper.
## 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
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@InProceedings{tgif-cvpr2016,
@inproceedings{krishna2017dense,
title={Dense-Captioning Events in Videos},
author={Krishna, Ranjay and Hata, Kenji and Ren, Frederic and Fei-Fei, Li and Niebles, Juan Carlos},
booktitle={International Conference on Computer Vision (ICCV)},
year={2017}
}
```
### Contributions
Thanks to [@leot13](https://github.com/leot13) for adding this dataset. |
bigscience-data/roots_en_wikivoyage | 2022-12-12T11:03:13.000Z | [
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | bigscience-data | null | null | null | 0 | 7 | ---
language: en
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_en_wikivoyage
# wikivoyage_filtered
- Dataset uid: `wikivoyage_filtered`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.0334 % of total
- 0.1097 % of en
- 0.0432 % of fr
- 0.0863 % of es
- 0.0084 % of zh
- 0.0892 % of vi
- 0.0464 % of indic-bn
- 0.0443 % of pt
- 0.0130 % of indic-hi
### BigScience processing steps
#### Filters applied to: en
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_en
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: fr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_fr
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: es
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_es
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: zh
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_zhs
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: vi
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_vi
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-bn
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-bn
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: pt
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_pt
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-hi
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
|
AlekseyKorshuk/fairy-tale-books | 2022-06-09T21:53:41.000Z | [
"region:us"
] | AlekseyKorshuk | null | null | null | 3 | 7 | Entry not found |
AlekseyKorshuk/fantasy-books | 2022-06-10T04:36:42.000Z | [
"region:us"
] | AlekseyKorshuk | null | null | null | 2 | 7 | Entry not found |
khalidalt/ultimate_arabic_news | 2022-06-15T14:46:10.000Z | [
"region:us"
] | khalidalt | null | null | null | 1 | 7 |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Ultimate Arabic News Dataset is a collection of single-label modern Arabic texts that are used in news websites and press articles.
Arabic news data was collected by web scraping techniques from many famous news sites such as Al-Arabiya, Al-Youm Al-Sabea (Youm7), the news published on the Google search engine and other various sources.
### 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
license: cc-by-4.0
### Citation Information
```
@book{url,
author = {Al-Dulaimi, Ahmed Hashim},
year = {2022},
month = {05},
website = {Mendeley Data, V1},
title = {Ultimate Arabic News Dataset},
doi = {10.17632/jz56k5wxz7.1}
}
```
### Contributions
[More Information Needed] |
PiC/phrase_similarity | 2023-01-20T16:32:19.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"l... | PiC | Phrase in Context is a curated benchmark for phrase understanding and semantic search, consisting of three tasks of increasing difficulty: Phrase Similarity (PS), Phrase Retrieval (PR) and Phrase Sense Disambiguation (PSD). The datasets are annotated by 13 linguistic experts on Upwork and verified by two groups: ~1000 AMT crowdworkers and another set of 5 linguistic experts. PiC benchmark is distributed under CC-BY-NC 4.0. | @article{pham2022PiC,
title={PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search},
author={Pham, Thang M and Yoon, Seunghyun and Bui, Trung and Nguyen, Anh},
journal={arXiv preprint arXiv:2207.09068},
year={2022}
} | null | 6 | 7 | ---
annotations_creators:
- expert-generated
language_creators:
- found
- expert-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
paperswithcode_id: phrase-in-context
pretty_name: 'PiC: Phrase Similarity (PS)'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
---
# Dataset Card for "PiC: Phrase Similarity"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://phrase-in-context.github.io/](https://phrase-in-context.github.io/)
- **Repository:** [https://github.com/phrase-in-context](https://github.com/phrase-in-context)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Thang Pham](<thangpham@auburn.edu>)
- **Size of downloaded dataset files:** 4.60 MB
- **Size of the generated dataset:** 2.96 MB
- **Total amount of disk used:** 7.56 MB
### Dataset Summary
PS is a binary classification task with the goal of predicting whether two multi-word noun phrases are semantically similar or not given *the same context* sentence.
This dataset contains ~10K pairs of two phrases along with their contexts used for disambiguation, since two phrases are not enough for semantic comparison.
Our ~10K examples were annotated by linguistic experts on <upwork.com> and verified in two rounds by 1000 Mturkers and 5 linguistic experts.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English.
## Dataset Structure
### Data Instances
**PS**
* Size of downloaded dataset files: 4.60 MB
* Size of the generated dataset: 2.96 MB
* Total amount of disk used: 7.56 MB
```
{
"phrase1": "annual run",
"phrase2": "yearlong performance",
"sentence1": "since 2004, the club has been a sponsor of the annual run for rigby to raise money for off-campus housing safety awareness.",
"sentence2": "since 2004, the club has been a sponsor of the yearlong performance for rigby to raise money for off-campus housing safety awareness.",
"label": 0,
"idx": 0,
}
```
### Data Fields
The data fields are the same among all splits.
* phrase1: a string feature.
* phrase2: a string feature.
* sentence1: a string feature.
* sentence2: a string feature.
* label: a classification label, with negative (0) and positive (1).
* idx: an int32 feature.
### Data Splits
| name |train |validation|test |
|--------------------|----:|--------:|----:|
|PS |7362| 1052|2102|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from [Upwork.com](https://upwork.com).
#### Who are the source language producers?
We hired 13 linguistic experts from [Upwork.com](https://upwork.com) for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
13 linguistic experts from [Upwork.com](https://upwork.com).
### Personal and Sensitive Information
No annotator identifying details are provided.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset is a joint work between Adobe Research and Auburn University.
Creators: [Thang M. Pham](https://scholar.google.com/citations?user=eNrX3mYAAAAJ), [David Seunghyun Yoon](https://david-yoon.github.io/), [Trung Bui](https://sites.google.com/site/trungbuistanford/), and [Anh Nguyen](https://anhnguyen.me).
[@PMThangXAI](https://twitter.com/pmthangxai) added this dataset to HuggingFace.
### Licensing Information
This dataset is distributed under [Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)
### Citation Information
```
@article{pham2022PiC,
title={PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search},
author={Pham, Thang M and Yoon, Seunghyun and Bui, Trung and Nguyen, Anh},
journal={arXiv preprint arXiv:2207.09068},
year={2022}
}
``` |
nateraw/airbnb-stock-price | 2022-06-16T21:10:27.000Z | [
"region:us"
] | nateraw | null | null | null | 0 | 7 | Entry not found |
BeIR/fiqa-generated-queries | 2022-10-23T06:13:18.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | null | 2 | 7 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## 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/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
Nexdata/Indonesian_Speech_Data_by_Mobile_Phone | 2023-08-28T08:58:32.000Z | [
"region:us"
] | Nexdata | null | null | null | 0 | 7 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Indonesian_Speech_Data_by_Mobile_Phone
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/991?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
1285 Indonesian native speakers participated in the recording with authentic accent. The recorded script is designed by linguists and cover a wide range of topics including generic, interactive, on-board and home. The text is manually proofread with high accuracy. It matches with mainstream Android and Apple system phones. The data set can be applied for automatic speech recognition, and machine translation scenes.
For more details, please refer to the link: https://www.nexdata.ai/datasets/991?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Indonesian
## 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
ConvLab/sgd | 2022-11-25T08:55:38.000Z | [
"task_categories:conversational",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:1909.05855",
"region:us"
] | ConvLab | null | null | null | 0 | 7 | ---
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: SGD
size_categories:
- 10K<n<100K
task_categories:
- conversational
---
# Dataset Card for Schema-Guided Dialogue
- **Repository:** https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
- **Paper:** https://arxiv.org/pdf/1909.05855.pdf
- **Leaderboard:** None
- **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)
To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via:
```
from convlab.util import load_dataset, load_ontology, load_database
dataset = load_dataset('sgd')
ontology = load_ontology('sgd')
database = load_database('sgd')
```
For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).
### Dataset Summary
The **Schema-Guided Dialogue (SGD)** dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, such as banks, events, media, calendar, travel, and weather. For most of these domains, the dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, and user simulation learning, among other tasks for developing large-scale virtual assistants. Additionally, the dataset contains unseen domains and services in the evaluation set to quantify the performance in zero-shot or few-shot settings.
- **How to get the transformed data from original data:**
- Download [dstc8-schema-guided-dialogue-master.zip](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/archive/refs/heads/master.zip).
- Run `python preprocess.py` in the current directory.
- **Main changes of the transformation:**
- Lower case original `act` as `intent`.
- Add `count` slot for each domain, non-categorical, find span by text matching.
- Categorize `dialogue acts` according to the `intent`.
- Concatenate multiple values using `|`.
- Retain `active_intent`, `requested_slots`, `service_call`.
- **Annotations:**
- dialogue acts, state, db_results, service_call, active_intent, requested_slots.
### Supported Tasks and Leaderboards
NLU, DST, Policy, NLG, E2E
### Languages
English
### Data Splits
| split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) |
| ---------- | --------- | ---------- | ------- | ---------- | ----------- | --------------------- | -------------------- | ---------------------------- | ------------------------------- |
| train | 16142 | 329964 | 20.44 | 9.75 | 1.84 | 100 | - | 100 | 100 |
| validation | 2482 | 48726 | 19.63 | 9.66 | 1.84 | 100 | - | 100 | 100 |
| test | 4201 | 84594 | 20.14 | 10.4 | 2.02 | 100 | - | 100 | 100 |
| all | 22825 | 463284 | 20.3 | 9.86 | 1.87 | 100 | - | 100 | 100 |
45 domains: ['Banks_1', 'Buses_1', 'Buses_2', 'Calendar_1', 'Events_1', 'Events_2', 'Flights_1', 'Flights_2', 'Homes_1', 'Hotels_1', 'Hotels_2', 'Hotels_3', 'Media_1', 'Movies_1', 'Music_1', 'Music_2', 'RentalCars_1', 'RentalCars_2', 'Restaurants_1', 'RideSharing_1', 'RideSharing_2', 'Services_1', 'Services_2', 'Services_3', 'Travel_1', 'Weather_1', 'Alarm_1', 'Banks_2', 'Flights_3', 'Hotels_4', 'Media_2', 'Movies_2', 'Restaurants_2', 'Services_4', 'Buses_3', 'Events_3', 'Flights_4', 'Homes_2', 'Media_3', 'Messaging_1', 'Movies_3', 'Music_3', 'Payment_1', 'RentalCars_3', 'Trains_1']
- **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage.
- **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage.
### Citation
```
@article{rastogi2019towards,
title={Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset},
author={Rastogi, Abhinav and Zang, Xiaoxue and Sunkara, Srinivas and Gupta, Raghav and Khaitan, Pranav},
journal={arXiv preprint arXiv:1909.05855},
year={2019}
}
```
### Licensing Information
[**CC BY-SA 4.0**](https://creativecommons.org/licenses/by-sa/4.0/) |
Zaib/java-vulnerability | 2022-07-14T11:09:57.000Z | [
"license:afl-3.0",
"region:us"
] | Zaib | null | null | null | 3 | 7 | ---
license: afl-3.0
---
|
rungalileo/newsgroups | 2022-10-05T22:49:15.000Z | [
"region:us"
] | rungalileo | null | null | null | 0 | 7 | Entry not found |
scikit-learn/churn-prediction | 2022-08-08T17:56:29.000Z | [
"license:cc-by-4.0",
"region:us"
] | scikit-learn | null | null | null | 2 | 7 | ---
license: cc-by-4.0
---
Customer churn prediction dataset of a fictional telecommunication company made by IBM Sample Datasets.
Context
Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.
Content
Each row represents a customer, each column contains customer’s attributes described on the column metadata.
The data set includes information about:
- Customers who left within the last month: the column is called Churn
- Services that each customer has signed up for: phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
- Customer account information: how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
- Demographic info about customers: gender, age range, and if they have partners and dependents
Credits for the dataset and the card:
- [Kaggle](https://www.kaggle.com/datasets/blastchar/telco-customer-churn)
- [Latest version of the dataset by IBM Samples team](https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113)
|
nsarker/flower-detection | 2022-08-23T15:54:28.000Z | [
"region:us"
] | nsarker | null | null | null | 0 | 7 | Entry not found |
wushan/vehicle_qa | 2022-08-25T13:14:33.000Z | [
"license:apache-2.0",
"region:us"
] | wushan | null | null | null | 4 | 7 | ---
license: apache-2.0
---
|
nid989/EssayFroum-Dataset | 2022-09-02T04:45:37.000Z | [
"license:apache-2.0",
"region:us"
] | nid989 | null | null | null | 2 | 7 | ---
license: apache-2.0
---
|
priyank-m/chinese_text_recognition | 2022-09-21T09:08:19.000Z | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:zh",
"ocr",
"text-recognition",
"chinese",
"region:us"
] | priyank-m | null | null | null | 6 | 7 | ---
annotations_creators: []
language:
- zh
language_creators: []
license: []
multilinguality:
- monolingual
pretty_name: chinese_text_recognition
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- ocr
- text-recognition
- chinese
task_categories:
- image-to-text
task_ids:
- image-captioning
---
Source of data: https://github.com/FudanVI/benchmarking-chinese-text-recognition |
jamescalam/reddit-demo | 2022-09-07T12:12:43.000Z | [
"region:us"
] | jamescalam | null | null | null | 0 | 7 | # Reddit Demo dataset
|
nbroad/basic_text_dataset | 2022-09-08T04:21:31.000Z | [
"region:us"
] | nbroad | null | null | null | 0 | 7 | Entry not found |
viola77data/recycling-dataset | 2022-09-13T13:17:15.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"recycling",
"image-classification",
"region:us"
] | viola77data | null | null | null | 5 | 7 | ---
annotations_creators: []
language:
- en
language_creators:
- crowdsourced
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: recycling-dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- recycling
- image-classification
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card for recycling-dataset
### Dataset Summary
This is a recycling dataset that can be used for image classification. It has 11 categories:
- aluminium
- batteries
- cardboard
- disposable plates
- glass
- hard plastic
- paper
- paper towel
- polystyrene
- soft plastics
- takeaway cups
It was scrapped from DuckDuckGo using this tool: https://pypi.org/project/jmd-imagescraper/
|
cjvt/gkomet | 2022-11-27T16:40:19.000Z | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:sl",
"license:cc-by-nc-sa-4.0",
"metaphor-classification",
"metonymy-classification",
"metaphor-frame-classification",
... | cjvt | G-KOMET 1.0 (a corpus of metaphorical expressions in spoken Slovene language) is a corpus of speech transcriptions and
conversations that covers 50,000 lexical units. The corpus contains samples from the Gos corpus of spoken Slovene
and includes a balanced set of transcriptions of informative, educational, entertaining, private, and public discourse.
The annotation scheme was based on the MIPVU metaphor identification process.
This protocol was modified and adapted to the specifics of the Slovene language and the specifics of the spoken
language. Corpus was annotated for the following relations to metaphor: indirect metaphor, direct metaphor, borderline
cases and metaphor signals. In addition, the corpus introduces a new ‘frame’ tag, which gives information about the
concept to which it refers. | @InProceedings{antloga2022gkomet,
title = {Korpusni pristopi za identifikacijo metafore in metonimije: primer metonimije v korpusu gKOMET},
author={Antloga, \v{S}pela},
booktitle={Proceedings of the Conference on Language Technologies and Digital Humanities (Student papers)},
year={2022},
pages={271-277}
} | null | 0 | 7 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- sl
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets: []
task_categories:
- token-classification
task_ids: []
pretty_name: G-KOMET
tags:
- metaphor-classification
- metonymy-classification
- metaphor-frame-classification
- multiword-expression-detection
---
# Dataset Card for G-KOMET
### Dataset Summary
G-KOMET 1.0 is a corpus of metaphorical expressions in spoken Slovene language, covering around 50,000 lexical units across 5695 sentences. The corpus contains samples from the Gos corpus of spoken Slovene and includes a balanced set of transcriptions of informative, educational, entertaining, private, and public discourse.
It is also annotated with idioms and metonymies. Note that these are both annotated as metaphor types. This is different from the annotations in [KOMET](https://huggingface.co/datasets/cjvt/komet), where these are both considered a type of frame. We keep the data as untouched as possible and let the user decide how they want to handle this.
### Supported Tasks and Leaderboards
Metaphor detection, metonymy detection, metaphor type classification, metaphor frame classification.
### Languages
Slovenian.
## Dataset Structure
### Data Instances
A sample instance from the dataset:
```
{
'document_name': 'G-Komet001.xml',
'idx': 3,
'idx_paragraph': 0,
'idx_sentence': 3,
'sentence_words': ['no', 'zdaj', 'samo', 'še', 'za', 'eno', 'orientacijo'],
'met_type': [
{'type': 'MRWi', 'word_indices': [6]}
],
'met_frame': [
{'type': 'spatial_orientation', 'word_indices': [6]}
]
}
```
The sentence comes from the document `G-Komet001.xml`, is the 3rd sentence in the document and is the 3rd sentence inside the 0th paragraph in the document.
The word "orientacijo" is annotated as an indirect metaphor-related word (`MRWi`).
It is also annotated with the frame "spatial_orientation".
### Data Fields
- `document_name`: a string containing the name of the document in which the sentence appears;
- `idx`: a uint32 containing the index of the sentence inside its document;
- `idx_paragraph`: a uint32 containing the index of the paragraph in which the sentence appears;
- `idx_sentence`: a uint32 containing the index of the sentence inside its paragraph;
containing the consecutive number of the paragraph inside the current news article;
- `sentence_words`: words in the sentence;
- `met_type`: metaphors in the sentence, marked by their type and word indices;
- `met_frame`: metaphor frames in the sentence, marked by their type (frame name) and word indices.
## Dataset Creation
The corpus contains samples from the GOS corpus of spoken Slovene and includes a balanced set of transcriptions of informative, educational, entertaining, private, and public discourse. It contains hand-annotated metaphor-related words, i.e. linguistic expressions that have the potential for people to interpret them as metaphors, idioms, i.e. multi-word units in which at least one word has been used metaphorically, and metonymies, expressions that we use to express something else.
For more information, please check out the paper (which is in Slovenian language) or contact the dataset author.
## Additional Information
### Dataset Curators
Špela Antloga.
### Licensing Information
CC BY-NC-SA 4.0
### Citation Information
```
@InProceedings{antloga2022gkomet,
title = {Korpusni pristopi za identifikacijo metafore in metonimije: primer metonimije v korpusu gKOMET},
author={Antloga, \v{S}pela},
booktitle={Proceedings of the Conference on Language Technologies and Digital Humanities (Student papers)},
year={2022},
pages={271-277}
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
|
arbml/shakkelha | 2022-11-03T14:22:56.000Z | [
"region:us"
] | arbml | null | null | null | 0 | 7 | Entry not found |
dougtrajano/olid-br | 2023-07-13T12:45:43.000Z | [
"language:pt",
"license:cc-by-4.0",
"region:us"
] | dougtrajano | null | null | null | 2 | 7 | ---
language: pt
license: cc-by-4.0
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: is_offensive
dtype: string
- name: is_targeted
dtype: string
- name: targeted_type
dtype: string
- name: toxic_spans
sequence: int64
- name: health
dtype: bool
- name: ideology
dtype: bool
- name: insult
dtype: bool
- name: lgbtqphobia
dtype: bool
- name: other_lifestyle
dtype: bool
- name: physical_aspects
dtype: bool
- name: profanity_obscene
dtype: bool
- name: racism
dtype: bool
- name: religious_intolerance
dtype: bool
- name: sexism
dtype: bool
- name: xenophobia
dtype: bool
splits:
- name: train
num_bytes: 1763684
num_examples: 5214
- name: test
num_bytes: 590953
num_examples: 1738
download_size: 1011742
dataset_size: 2354637
---
# OLID-BR
Offensive Language Identification Dataset for Brazilian Portuguese (OLID-BR) is a dataset with multi-task annotations for the detection of offensive language.
The current version (v1.0) contains **7,943** (extendable to 13,538) comments from different sources, including social media (YouTube and Twitter) and related datasets.
OLID-BR contains a collection of annotated sentences in Brazilian Portuguese using an annotation model that encompasses the following levels:
- [Offensive content detection](#offensive-content-detection): Detect offensive content in sentences and categorize it.
- [Offense target identification](#offense-target-identification): Detect if an offensive sentence is targeted to a person or group of people.
- [Offensive spans identification](#offensive-spans-identification): Detect curse words in sentences.
## Categorization
### Offensive Content Detection
This level is used to detect offensive content in the sentence.
**Is this text offensive?**
We use the [Perspective API](https://www.perspectiveapi.com/) to detect if the sentence contains offensive content with double-checking by our [qualified annotators](annotation/index.en.md#who-are-qualified-annotators).
- `OFF` Offensive: Inappropriate language, insults, or threats.
- `NOT` Not offensive: No offense or profanity.
**Which kind of offense does it contain?**
The following labels were tagged by our annotators:
`Health`, `Ideology`, `Insult`, `LGBTQphobia`, `Other-Lifestyle`, `Physical Aspects`, `Profanity/Obscene`, `Racism`, `Religious Intolerance`, `Sexism`, and `Xenophobia`.
See the [**Glossary**](glossary.en.md) for further information.
### Offense Target Identification
This level is used to detect if an offensive sentence is targeted to a person or group of people.
**Is the offensive text targeted?**
- `TIN` Targeted Insult: Targeted insult or threat towards an individual, a group or other.
- `UNT` Untargeted: Non-targeted profanity and swearing.
**What is the target of the offense?**
- `IND` The offense targets an individual, often defined as “cyberbullying”.
- `GRP` The offense targets a group of people based on ethnicity, gender, sexual
- `OTH` The target can belong to other categories, such as an organization, an event, an issue, etc.
### Offensive Spans Identification
As toxic spans, we define a sequence of words that attribute to the text's toxicity.
For example, let's consider the following text:
> "USER `Canalha` URL"
The toxic spans are:
```python
[5, 6, 7, 8, 9, 10, 11, 12, 13]
```
## Dataset Structure
### Data Instances
Each instance is a social media comment with a corresponding ID and annotations for all the tasks described below.
### Data Fields
The simplified configuration includes:
- `id` (string): Unique identifier of the instance.
- `text` (string): The text of the instance.
- `is_offensive` (string): Whether the text is offensive (`OFF`) or not (`NOT`).
- `is_targeted` (string): Whether the text is targeted (`TIN`) or untargeted (`UNT`).
- `targeted_type` (string): Type of the target (individual `IND`, group `GRP`, or other `OTH`). Only available if `is_targeted` is `True`.
- `toxic_spans` (string): List of toxic spans.
- `health` (boolean): Whether the text contains hate speech based on health conditions such as disability, disease, etc.
- `ideology` (boolean): Indicates if the text contains hate speech based on a person's ideas or beliefs.
- `insult` (boolean): Whether the text contains insult, inflammatory, or provocative content.
- `lgbtqphobia` (boolean): Whether the text contains harmful content related to gender identity or sexual orientation.
- `other_lifestyle` (boolean): Whether the text contains hate speech related to life habits (e.g. veganism, vegetarianism, etc.).
- `physical_aspects` (boolean): Whether the text contains hate speech related to physical appearance.
- `profanity_obscene` (boolean): Whether the text contains profanity or obscene content.
- `racism` (boolean): Whether the text contains prejudiced thoughts or discriminatory actions based on differences in race/ethnicity.
- `religious_intolerance` (boolean): Whether the text contains religious intolerance.
- `sexism` (boolean): Whether the text contains discriminatory content based on differences in sex/gender (e.g. sexism, misogyny, etc.).
- `xenophobia` (boolean): Whether the text contains hate speech against foreigners.
See the [**Get Started**](get-started.en.md) page for more information.
## Considerations for Using the Data
### Social Impact of Dataset
Toxicity detection is a worthwhile problem that can ensure a safer online environment for everyone.
However, toxicity detection algorithms have focused on English and do not consider the specificities of other languages.
This is a problem because the toxicity of a comment can be different in different languages.
Additionally, the toxicity detection algorithms focus on the binary classification of a comment as toxic or not toxic.
Therefore, we believe that the OLID-BR dataset can help to improve the performance of toxicity detection algorithms in Brazilian Portuguese.
### Discussion of Biases
We are aware that the dataset contains biases and is not representative of global diversity.
We are aware that the language used in the dataset could not represent the language used in different contexts.
Potential biases in the data include: Inherent biases in the social media and user base biases, the offensive/vulgar word lists used for data filtering, and inherent or unconscious bias in the assessment of offensive identity labels.
All these likely affect labeling, precision, and recall for a trained model.
## Citation
Pending |
csebuetnlp/BanglaParaphrase | 2022-11-14T15:39:43.000Z | [
"task_categories:text2text-generation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100k<n<1M",
"source_datasets:original",
"language:bn",
"license:cc-by-nc-sa-4.0",
"conditional-text-generation",
"paraphrase-generation",
"arxiv:2210.0... | csebuetnlp | We present a high quality bangla paraphrase dataset containing about 466k paraphrase pairs. The paraphrases ensures high quality by being semantically coherent and syntactically diverse. | to be added | null | 3 | 7 | ---
annotations_creators:
- found
language_creators:
- found
language:
- bn
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100k<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: BanglaParaphrase
tags:
- conditional-text-generation
- paraphrase-generation
---
# Dataset Card for "BanglaParaphrase"
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/csebuetnlp/banglaparaphrase](https://github.com/csebuetnlp/banglaparaphrase)
- **Paper:** [BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset](https://arxiv.org/abs/2210.05109)
- **Point of Contact:** [Najrin Sultana](mailto:nazrinshukti@gmail.com)
### Dataset Summary
We present BanglaParaphrase, a high quality synthetic Bangla paraphrase dataset containing about 466k paraphrase pairs.
The paraphrases ensures high quality by being semantically coherent and syntactically diverse.
### Supported Tasks and Leaderboards
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Languages
- `bengali`
## Loading the dataset
```python
from datasets import load_dataset
from datasets import load_dataset
ds = load_dataset("csebuetnlp/BanglaParaphrase")
```
## Dataset Structure
### Data Instances
One example from the `train` part of the dataset is given below in JSON format.
```
{
"source": "বেশিরভাগ সময় প্রকৃতির দয়ার ওপরেই বেঁচে থাকতেন উপজাতিরা।",
"target": "বেশিরভাগ সময়ই উপজাতিরা প্রকৃতির দয়ার উপর নির্ভরশীল ছিল।"
}
```
### Data Fields
- 'source': A string representing the source sentence.
- 'target': A string representing the target sentence.
### Data Splits
Dataset with train-dev-test example counts are given below:
Language | ISO 639-1 Code | Train | Validation | Test |
-------------- | ---------------- | ------- | ----- | ------ |
Bengali | bn | 419, 967 | 233, 31 | 233, 32 |
## Dataset Creation
### Curation Rationale
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Source Data
[Roar Bangla](https://roar.media/bangla)
#### Initial Data Collection and Normalization
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
#### Who are the source language producers?
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
### Annotations
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
#### Annotation process
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
#### Who are the annotators?
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
### Personal and Sensitive Information
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
## Considerations for Using the Data
### Social Impact of Dataset
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Discussion of Biases
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Other Known Limitations
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
## Additional Information
### Dataset Curators
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
```
@article{akil2022banglaparaphrase,
title={BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset},
author={Akil, Ajwad and Sultana, Najrin and Bhattacharjee, Abhik and Shahriyar, Rifat},
journal={arXiv preprint arXiv:2210.05109},
year={2022}
}
```
### Contributions
|
lcw99/oscar-ko-only | 2022-10-21T05:52:05.000Z | [
"language:ko",
"region:us"
] | lcw99 | null | null | null | 1 | 7 | ---
language:
- ko
---
# oscar dataset only korean |
ju-resplande/qa-pt | 2022-11-25T20:31:56.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:extended|mqa",
"language:pt",
"license:cc0-1.0",
"region:us"
] | ju-resplande | null | null | null | 6 | 7 | ---
annotations_creators:
- no-annotation
language_creators:
- other
language:
- pt
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: qa-portuguese
size_categories:
- 1M<n<10M
source_datasets:
- extended|mqa
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
---
# Dataset Card for QA-Portuguese
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Portuguese preprocessed split from [MQA dataset](https://huggingface.co/datasets/clips/mqa).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is Portuguese.
## 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 [@ju-resplande](https://github.com/ju-resplande) for adding this dataset.
|
jpwahle/autoregressive-paraphrase-dataset | 2022-11-19T12:14:43.000Z | [
"task_categories:text-classification",
"task_categories:text-generation",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"plagiarism",
"pa... | jpwahle | null | null | null | 1 | 7 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Machine Paraphrase Dataset (T5, GPT-3)
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- plagiarism
- paraphrase
- academic integrity
- arxiv
- wikipedia
- theses
- bert
- roberta
- t5
- gpt-3
task_categories:
- text-classification
- text-generation
task_ids: []
---
# 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
- **Size:** 163MB
- **Repository:** https://github.com/jpwahle/emnlp22-transforming
- **Paper:** https://arxiv.org/abs/2210.03568
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
dvitel/hearthstone | 2022-11-10T01:24:14.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:other-en-python",
"size_categories:n<1K",
"language:en",
"license:mit",
"code-synthesis",
"semantic-parsing",
"python",
"hearthstone",
"region:us"
] | dvitel | null | null | null | 1 | 7 | ---
annotations_creators: []
language:
- en
language_creators: []
license:
- mit
multilinguality:
- other-en-python
pretty_name: HEARTHSTONE - synthesis of python code for card game descriptions
size_categories:
- n<1K
source_datasets: []
tags:
- code-synthesis
- semantic-parsing
- python
- hearthstone
task_categories:
- text-generation
task_ids:
- language-modeling
---
Datasets for HEARTHSTONE card game. Taken from [this source](https://github.com/deepmind/card2code/tree/master/third_party/hearthstone)
|
bigbio/bioasq_2021_mesinesp | 2022-12-22T15:43:30.000Z | [
"multilinguality:monolingual",
"language:es",
"license:cc-by-4.0",
"region:us"
] | bigbio | The main aim of MESINESP2 is to promote the development of practically relevant semantic indexing tools for biomedical content in non-English language. We have generated a manually annotated corpus, where domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts. All the documents were labeled with DeCS descriptors, which is a structured controlled vocabulary created by BIREME to index scientific publications on BvSalud, the largest database of scientific documents in Spanish, which hosts records from the databases LILACS, MEDLINE, IBECS, among others.
MESINESP track at BioASQ9 explores the efficiency of systems for assigning DeCS to different types of biomedical documents. To that purpose, we have divided the task into three subtracks depending on the document type. Then, for each one we generated an annotated corpus which was provided to participating teams:
- [Subtrack 1 corpus] MESINESP-L – Scientific Literature: It contains all Spanish records from LILACS and IBECS databases at the Virtual Health Library (VHL) with non-empty abstract written in Spanish.
- [Subtrack 2 corpus] MESINESP-T- Clinical Trials contains records from Registro Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the structure title/abstract needed in BioASQ, for that reason we have built artificial abstracts based on the content available in the data crawled using the REEC API.
- [Subtrack 3 corpus] MESINESP-P – Patents: This corpus includes patents in Spanish extracted from Google Patents which have the IPC code “A61P” and “A61K31”. In addition, we also provide a set of complementary data such as: the DeCS terminology file, a silver standard with the participants' predictions to the task background set and the entities of medications, diseases, symptoms and medical procedures extracted from the BSC NERs documents. | @conference {396,
title = {Overview of BioASQ 2021-MESINESP track. Evaluation of
advance hierarchical classification techniques for scientific
literature, patents and clinical trials.},
booktitle = {Proceedings of the 9th BioASQ Workshop
A challenge on large-scale biomedical semantic indexing
and question answering},
year = {2021},
url = {http://ceur-ws.org/Vol-2936/paper-11.pdf},
author = {Gasco, Luis and Nentidis, Anastasios and Krithara, Anastasia
and Estrada-Zavala, Darryl and Toshiyuki Murasaki, Renato and Primo-Pe{\~n}a,
Elena and Bojo-Canales, Cristina and Paliouras, Georgios and Krallinger, Martin}
} | null | 0 | 7 |
---
language:
- es
bigbio_language:
- Spanish
license: cc-by-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_4p0
pretty_name: MESINESP 2021
homepage: https://zenodo.org/record/5602914#.YhSXJ5PMKWt
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- TEXT_CLASSIFICATION
---
# Dataset Card for MESINESP 2021
## Dataset Description
- **Homepage:** https://zenodo.org/record/5602914#.YhSXJ5PMKWt
- **Pubmed:** False
- **Public:** True
- **Tasks:** TXTCLASS
The main aim of MESINESP2 is to promote the development of practically relevant semantic indexing tools for biomedical content in non-English language. We have generated a manually annotated corpus, where domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts. All the documents were labeled with DeCS descriptors, which is a structured controlled vocabulary created by BIREME to index scientific publications on BvSalud, the largest database of scientific documents in Spanish, which hosts records from the databases LILACS, MEDLINE, IBECS, among others.
MESINESP track at BioASQ9 explores the efficiency of systems for assigning DeCS to different types of biomedical documents. To that purpose, we have divided the task into three subtracks depending on the document type. Then, for each one we generated an annotated corpus which was provided to participating teams:
- [Subtrack 1 corpus] MESINESP-L – Scientific Literature: It contains all Spanish records from LILACS and IBECS databases at the Virtual Health Library (VHL) with non-empty abstract written in Spanish.
- [Subtrack 2 corpus] MESINESP-T- Clinical Trials contains records from Registro Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the structure title/abstract needed in BioASQ, for that reason we have built artificial abstracts based on the content available in the data crawled using the REEC API.
- [Subtrack 3 corpus] MESINESP-P – Patents: This corpus includes patents in Spanish extracted from Google Patents which have the IPC code “A61P” and “A61K31”. In addition, we also provide a set of complementary data such as: the DeCS terminology file, a silver standard with the participants' predictions to the task background set and the entities of medications, diseases, symptoms and medical procedures extracted from the BSC NERs documents.
## Citation Information
```
@conference {396,
title = {Overview of BioASQ 2021-MESINESP track. Evaluation of
advance hierarchical classification techniques for scientific
literature, patents and clinical trials.},
booktitle = {Proceedings of the 9th BioASQ Workshop
A challenge on large-scale biomedical semantic indexing
and question answering},
year = {2021},
url = {http://ceur-ws.org/Vol-2936/paper-11.pdf},
author = {Gasco, Luis and Nentidis, Anastasios and Krithara, Anastasia
and Estrada-Zavala, Darryl and Toshiyuki Murasaki, Renato and Primo-Pe{\~n}a,
Elena and Bojo-Canales, Cristina and Paliouras, Georgios and Krallinger, Martin}
}
```
|
bigbio/pharmaconer | 2022-12-22T15:46:15.000Z | [
"multilinguality:monolingual",
"language:es",
"license:cc-by-4.0",
"region:us"
] | bigbio | PharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track
This dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.
It is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).
The annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.
The PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.
For further information, please visit https://temu.bsc.es/pharmaconer/ or send an email to encargo-pln-life@bsc.es | @inproceedings{gonzalez2019pharmaconer,
title = "PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track",
author = "Gonzalez-Agirre, Aitor and
Marimon, Montserrat and
Intxaurrondo, Ander and
Rabal, Obdulia and
Villegas, Marta and
Krallinger, Martin",
booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5701",
doi = "10.18653/v1/D19-5701",
pages = "1--10",
} | null | 0 | 7 |
---
language:
- es
bigbio_language:
- Spanish
license: cc-by-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_4p0
pretty_name: PharmaCoNER
homepage: https://temu.bsc.es/pharmaconer/index.php/datasets/
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- TEXT_CLASSIFICATION
---
# Dataset Card for PharmaCoNER
## Dataset Description
- **Homepage:** https://temu.bsc.es/pharmaconer/index.php/datasets/
- **Pubmed:** False
- **Public:** True
- **Tasks:** NER,TXTCLASS
### Subtrack 1
PharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track
This dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.
It is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).
The annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.
The PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.
For further information, please visit https://temu.bsc.es/pharmaconer/ or send an email to encargo-pln-life@bsc.es
SUBTRACK 1: NER offset and entity type classification
The first subtrack consists in the classical entity-based or instanced-based evaluation that requires that system outputs match exactly the beginning and end locations of each entity tag, as well as match the entity annotation type of the gold standard annotations.
### Subtrack 2
PharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track
This dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.
It is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).
The annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.
The PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.
For further information, please visit https://temu.bsc.es/pharmaconer/ or send an email to encargo-pln-life@bsc.es
SUBTRACK 2: CONCEPT INDEXING
In the second subtask, a list of unique SNOMED concept identifiers have to be generated for each document. The predictions are compared to the manually annotated concept ids corresponding to chemical compounds and pharmacological substances.
### Full Task
PharmaCoNER: Pharmacological Substances, Compounds and Proteins Named Entity Recognition track
This dataset is designed for the PharmaCoNER task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.
It is a manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from SciELO (Scientific Electronic Library Online).
The annotation of the entire set of entity mentions was carried out by medicinal chemistry experts and it includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.
The PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets. The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.
For further information, please visit https://temu.bsc.es/pharmaconer/ or send an email to encargo-pln-life@bsc.es
SUBTRACK 1: NER offset and entity type classification
The first subtrack consists in the classical entity-based or instanced-based evaluation that requires that system outputs match exactly the beginning and end locations of each entity tag, as well as match the entity annotation type of the gold standard annotations.
SUBTRACK 2: CONCEPT INDEXING
In the second subtask, a list of unique SNOMED concept identifiers have to be generated for each document. The predictions are compared to the manually annotated concept ids corresponding to chemical compounds and pharmacological substances.
## Citation Information
```
@inproceedings{gonzalez2019pharmaconer,
title = "PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track",
author = "Gonzalez-Agirre, Aitor and
Marimon, Montserrat and
Intxaurrondo, Ander and
Rabal, Obdulia and
Villegas, Marta and
Krallinger, Martin",
booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5701",
doi = "10.18653/v1/D19-5701",
pages = "1--10",
}
```
|
bigbio/scielo | 2022-12-22T15:46:40.000Z | [
"multilinguality:multilingual",
"language:en",
"language:es",
"language:pt",
"license:cc-by-4.0",
"region:us"
] | bigbio | A parallel corpus of full-text scientific articles collected from Scielo database in the following languages: English, Portuguese and Spanish. The corpus is sentence aligned for all language pairs, as well as trilingual aligned for a small subset of sentences. Alignment was carried out using the Hunalign algorithm. | @inproceedings{soares2018large,
title = {A Large Parallel Corpus of Full-Text Scientific Articles},
author = {Soares, Felipe and Moreira, Viviane and Becker, Karin},
year = 2018,
booktitle = {
Proceedings of the Eleventh International Conference on Language Resources
and Evaluation (LREC-2018)
}
} | null | 1 | 7 |
---
language:
- en
- es
- pt
bigbio_language:
- English
- Spanish
- Portuguese
license: cc-by-4.0
multilinguality: multilingual
bigbio_license_shortname: CC_BY_4p0
pretty_name: SciELO
homepage: https://sites.google.com/view/felipe-soares/datasets#h.p_92uSCyAjWSRB
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- TRANSLATION
---
# Dataset Card for SciELO
## Dataset Description
- **Homepage:** https://sites.google.com/view/felipe-soares/datasets#h.p_92uSCyAjWSRB
- **Pubmed:** False
- **Public:** True
- **Tasks:** TRANSL
A parallel corpus of full-text scientific articles collected from Scielo database in the following languages: English, Portuguese and Spanish. The corpus is sentence aligned for all language pairs, as well as trilingual aligned for a small subset of sentences. Alignment was carried out using the Hunalign algorithm.
## Citation Information
```
@inproceedings{soares2018large,
title = {A Large Parallel Corpus of Full-Text Scientific Articles},
author = {Soares, Felipe and Moreira, Viviane and Becker, Karin},
year = 2018,
booktitle = {
Proceedings of the Eleventh International Conference on Language Resources
and Evaluation (LREC-2018)
}
}
```
|
shahidul034/text_summarization_dataset9 | 2022-12-02T05:02:51.000Z | [
"region:us"
] | shahidul034 | null | null | null | 0 | 7 | ---
dataset_info:
features:
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 130228352
num_examples: 104575
download_size: 45376452
dataset_size: 130228352
---
# Dataset Card for "text_summarization_dataset9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
egm517/hupd_augmented | 2022-12-10T19:02:49.000Z | [
"task_categories:fill-mask",
"task_categories:summarization",
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:masked-language-modeling",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"task_ids:named-entity-recognition",
"language:en"... | egm517 | The Harvard USPTO Patent Dataset (HUPD) is a large-scale, well-structured, and multi-purpose corpus
of English-language patent applications filed to the United States Patent and Trademark Office (USPTO)
between 2004 and 2018. With more than 4.5 million patent documents, HUPD is two to three times larger
than comparable corpora. Unlike other NLP patent datasets, HUPD contains the inventor-submitted versions
of patent applications, not the final versions of granted patents, allowing us to study patentability at
the time of filing using NLP methods for the first time. | @InProceedings{suzgun2021:hupd,
title = {The Harvard USPTO Patent Dataset},
authors={Mirac Suzgun and Suproteem Sarkar and Luke Melas-Kyriazi and Scott Kominers and Stuart Shieber},
year={2021}
} | null | 0 | 7 | ---
language:
- en
license:
- cc-by-sa-4.0
task_categories:
- fill-mask
- summarization
- text-classification
- token-classification
task_ids:
- masked-language-modeling
- multi-class-classification
- topic-classification
- named-entity-recognition
pretty_name: "HUPD"
tags:
- patents
---
# Dataset Card for The Harvard USPTO Patent Dataset (HUPD)
## Dataset Description
- **Homepage:** [https://patentdataset.org/](https://patentdataset.org/)
- **Repository:** [HUPD GitHub repository](https://github.com/suzgunmirac/hupd)
- **Paper:** [HUPD arXiv Submission](https://arxiv.org/abs/2207.04043)
- **Point of Contact:** Mirac Suzgun
### Dataset Summary
The Harvard USPTO Dataset (HUPD) is a large-scale, well-structured, and multi-purpose corpus of English-language utility patent applications filed to the United States Patent and Trademark Office (USPTO) between January 2004 and December 2018.
### Experiments and Tasks Considered in the Paper
- **Patent Acceptance Prediction**: Given a section of a patent application (in particular, the abstract, claims, or description), predict whether the application will be accepted by the USPTO.
- **Automated Subject (IPC/CPC) Classification**: Predict the primary IPC or CPC code of a patent application given (some subset of) the text of the application.
- **Language Modeling**: Masked/autoregressive language modeling on the claims and description sections of patent applications.
- **Abstractive Summarization**: Given the claims or claims section of a patent application, generate the abstract.
### Languages
The dataset contains English text only.
### Domain
Patents (intellectual property).
### Dataset Curators
The dataset was created by Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart M. Shieber.
## Dataset Structure
Each patent application is defined by a distinct JSON file, named after its application number, and includes information about
the application and publication numbers,
title,
decision status,
filing and publication dates,
primary and secondary classification codes,
inventor(s),
examiner,
attorney,
abstract,
claims,
background,
summary, and
full description of the proposed invention, among other fields. There are also supplementary variables, such as the small-entity indicator (which denotes whether the applicant is considered to be a small entity by the USPTO) and the foreign-filing indicator (which denotes whether the application was originally filed in a foreign country).
In total, there are 34 data fields for each application. A full list of data fields used in the dataset is listed in the next section.
### Data Instances
Each patent application in our patent dataset is defined by a distinct JSON file (e.g., ``8914308.json``), named after its unique application number. The format of the JSON files is as follows:
```python
{
"application_number": "...",
"publication_number": "...",
"title": "...",
"decision": "...",
"date_produced": "...",
"date_published": "...",
"main_cpc_label": "...",
"cpc_labels": ["...", "...", "..."],
"main_ipcr_label": "...",
"ipcr_labels": ["...", "...", "..."],
"patent_number": "...",
"filing_date": "...",
"patent_issue_date": "...",
"abandon_date": "...",
"uspc_class": "...",
"uspc_subclass": "...",
"examiner_id": "...",
"examiner_name_last": "...",
"examiner_name_first": "...",
"examiner_name_middle": "...",
"inventor_list": [
{
"inventor_name_last": "...",
"inventor_name_first": "...",
"inventor_city": "...",
"inventor_state": "...",
"inventor_country": "..."
}
],
"abstract": "...",
"claims": "...",
"background": "...",
"summary": "...",
"full_description": "..."
}
```
## Usage
### Loading the Dataset
#### Sample (January 2016 Subset)
The following command can be used to load the `sample` version of the dataset, which contains all the patent applications that were filed to the USPTO during the month of January in 2016. This small subset of the dataset can be used for debugging and exploration purposes.
```python
from datasets import load_dataset
dataset_dict = load_dataset('HUPD/hupd',
name='sample',
data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather",
icpr_label=None,
train_filing_start_date='2016-01-01',
train_filing_end_date='2016-01-21',
val_filing_start_date='2016-01-22',
val_filing_end_date='2016-01-31',
)
```
#### Full Dataset
If you would like to use the **full** version of the dataset, please make sure that change the `name` field from `sample` to `all`, specify the training and validation start and end dates carefully, and set `force_extract` to be `True` (so that you would only untar the files that you are interested in and not squander your disk storage space). In the following example, for instance, we set the training set year range to be [2011, 2016] (inclusive) and the validation set year range to be 2017.
```python
from datasets import load_dataset
dataset_dict = load_dataset('HUPD/hupd',
name='all',
data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather",
icpr_label=None,
force_extract=True,
train_filing_start_date='2011-01-01',
train_filing_end_date='2016-12-31',
val_filing_start_date='2017-01-01',
val_filing_end_date='2017-12-31',
)
```
### Google Colab Notebook
You can also use the following Google Colab notebooks to explore HUPD.
- [](https://colab.research.google.com/drive/1_ZsI7WFTsEO0iu_0g3BLTkIkOUqPzCET?usp=sharing)[ HUPD Examples: Loading the Dataset](https://colab.research.google.com/drive/1_ZsI7WFTsEO0iu_0g3BLTkIkOUqPzCET?usp=sharing)
- [](https://colab.research.google.com/drive/1TzDDCDt368cUErH86Zc_P2aw9bXaaZy1?usp=sharing)[ HUPD Examples: Loading HUPD By Using HuggingFace's Libraries](https://colab.research.google.com/drive/1TzDDCDt368cUErH86Zc_P2aw9bXaaZy1?usp=sharing)
- [](https://colab.research.google.com/drive/1TzDDCDt368cUErH86Zc_P2aw9bXaaZy1?usp=sharing)[ HUPD Examples: Using the HUPD DistilRoBERTa Model](https://colab.research.google.com/drive/11t69BWcAVXndQxAOCpKaGkKkEYJSfydT?usp=sharing)
- [](https://colab.research.google.com/drive/1TzDDCDt368cUErH86Zc_P2aw9bXaaZy1?usp=sharing)[ HUPD Examples: Using the HUPD T5-Small Summarization Model](https://colab.research.google.com/drive/1VkCtrRIryzev_ixDjmJcfJNK-q6Vx24y?usp=sharing)
## Dataset Creation
### Source Data
HUPD synthesizes multiple data sources from the USPTO: While the full patent application texts were obtained from the USPTO Bulk Data Storage System (Patent Application Data/XML Versions 4.0, 4.1, 4.2, 4.3, 4.4 ICE, as well as Version 1.5) as XML files, the bibliographic filing metadata were obtained from the USPTO Patent Examination Research Dataset (in February, 2021).
### Annotations
Beyond our patent decision label, for which construction details are provided in the paper, the dataset does not contain any human-written or computer-generated annotations beyond those produced by patent applicants or the USPTO.
### Data Shift
A major feature of HUPD is its structure, which allows it to demonstrate the evolution of concepts over time. As we illustrate in the paper, the criteria for patent acceptance evolve over time at different rates, depending on category. We believe this is an important feature of the dataset, not only because of the social scientific questions it raises, but also because it facilitates research on models that can accommodate concept shift in a real-world setting.
### Personal and Sensitive Information
The dataset contains information about the inventor(s) and examiner of each patent application. These details are, however, already in the public domain and available on the USPTO's Patent Application Information Retrieval (PAIR) system, as well as on Google Patents and PatentsView.
### Social Impact of the Dataset
The authors of the dataset hope that HUPD will have a positive social impact on the ML/NLP and Econ/IP communities. They discuss these considerations in more detail in [the paper](https://arxiv.org/abs/2207.04043).
### Impact on Underserved Communities and Discussion of Biases
The dataset contains patent applications in English, a language with heavy attention from the NLP community. However, innovation is spread across many languages, cultures, and communities that are not reflected in this dataset. HUPD is thus not representative of all kinds of innovation. Furthermore, patent applications require a fixed cost to draft and file and are not accessible to everyone. One goal of this dataset is to spur research that reduces the cost of drafting applications, potentially allowing for more people to seek intellectual property protection for their innovations.
### Discussion of Biases
Section 4 of [the HUPD paper](https://arxiv.org/abs/2207.04043) provides an examination of the dataset for potential biases. It shows, among other things, that female inventors are notably underrepresented in the U.S. patenting system, that small and micro entities (e.g., independent inventors, small companies, non-profit organizations) are less likely to have positive outcomes in patent obtaining than large entities (e.g., companies with more than 500 employees), and that patent filing and acceptance rates are not uniformly distributed across the US. Our empirical findings suggest that any study focusing on the acceptance prediction task, especially if it is using the inventor information or the small-entity indicator as part of the input, should be aware of the the potential biases present in the dataset and interpret their results carefully in light of those biases.
- Please refer to Section 4 and Section D for an in-depth discussion of potential biases embedded in the dataset.
### Licensing Information
HUPD is released under the CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International.
### Citation Information
```
@article{suzgun2022hupd,
title={The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications},
author={Suzgun, Mirac and Melas-Kyriazi, Luke and Sarkar, Suproteem K. and Kominers, Scott Duke and Shieber, Stuart M.},
year={2022},
publisher={arXiv preprint arXiv:2207.04043},
url={https://arxiv.org/abs/2207.04043},
``` |
babelbox/babelbox_voice | 2023-02-13T21:27:17.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:sv",
"license:cc0-1.0",
"NST",
"region:us"
] | babelbox | null | @inproceedings{babelboxvoice:2022,
author = {Andersson, O. and Bjelkenhed, M. and Bielsa, M. et al},
title = {Babelbox Voice: A Speech Corpus for training Whisper},
year = 2022
} | null | 0 | 7 | ---
annotations_creators:
- crowdsourced
language:
- sv
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: Babelbox Voice
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- NST
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for Babelbox Voice
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Swedish.
It is redistributed as a Hugging Face dataset for convienience.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Swedish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
xusenlin/clue-ner | 2022-12-07T14:22:37.000Z | [
"language:zh",
"license:apache-2.0",
"named entity recognition",
"clue",
"region:us"
] | xusenlin | null | null | null | 7 | 7 | ---
dataset_info:
features:
- name: text
dtype: string
- name: entities
list:
- name: id
dtype: int64
- name: entity
dtype: string
- name: start_offset
dtype: int64
- name: end_offset
dtype: int64
- name: label
dtype: string
splits:
- name: train
num_bytes: 2443356
num_examples: 10748
- name: test
num_bytes: 154492
num_examples: 1345
- name: validation
num_bytes: 309106
num_examples: 1343
download_size: 1658426
dataset_size: 2906954
language:
- zh
tags:
- named entity recognition
- clue
license: apache-2.0
---
# CLUE-NER 命名实体识别数据集
字段说明
+ `text`: 文本
+ `entities`: 文本中包含的实体
+ `id`: 实体 `id`
+ `entity`: 实体对应的字符串
+ `start_offset`: 实体开始位置
+ `end_offset`: 实体结束位置的下一位
+ `label`: 实体对应的开始位置
|
facebook/panda | 2022-12-10T14:01:45.000Z | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"lice... | facebook | null | null | null | 5 | 7 | ---
annotations_creators:
- expert-generated
- crowdsourced
language:
- en
language_creators:
- crowdsourced
- expert-generated
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: winobias
pretty_name: panda
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- fairness
- nlp
- demographic
- diverse
- gender
- non-binary
- race
- age
task_categories:
- token-classification
task_ids: []
---
# Dataset Card for PANDA
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/facebookresearch/ResponsibleNLP/
- **Paper:** https://arxiv.org/abs/2205.12586
- **Point of Contact:** rebeccaqian@meta.com, ccross@meta.com, douwe@huggingface.co, adinawilliams@meta.com
### Dataset Summary
PANDA (Perturbation Augmentation NLP DAtaset) consists of approximately 100K pairs of crowdsourced human-perturbed text snippets (original, perturbed). Annotators were given selected terms and target demographic attributes, and instructed to rewrite text snippets along three demographic axes: gender, race and age, while preserving semantic meaning. Text snippets were sourced from a range of text corpora (BookCorpus, Wikipedia, ANLI, MNLI, SST, SQuAD). PANDA can be used for training a learned perturber that can rewrite text with control. PANDA can also be used to evaluate the demographic robustness of language models.
### Languages
English
## Dataset Structure
### Data Instances
- Size of training data: 198.6 MB
- Size of validation data: 22.2 MB
Examples of data instances:
```
{
"original": "the moment the girl mentions the subject she will be yours .",
"selected_word": "girl",
"target_attribute": "man",
"perturbed": "the moment the boy mentions the subject he will be yours.\n\n"
}
{
"original": "are like magic tricks, says the New York Times ' Michael Kimmelman. <SEP> Michael Kimmelman has never likened anything to a magic trick.",
"selected_word": "Michael",
"target_attribute": "woman",
"perturbed": "are like magic tricks, says the New York Times' Michelle Kimmelman. <SEP> Michelle Kimmelman has never likened anything to a magic trick."
}
{
"original": "lilly ann looked at him asking herself how he cold not know .",
"selected_word": "he",
"target_attribute": "non-binary",
"perturbed": "Lilly Ann looked at them, asking herself how they could not know."
}
```
Examples with <SEP> tokens are the result of concatenation of text fields in source datasets, such as the premise and hypothesis of NLI datasets.
### Data Fields
- `original`: Source (unperturbed) text snippet, sampled from a variety of English text corpora.
- `selected_word`: Demographic term that needs to be perturbed.
- `target_attribute`: Target demographic category.
- `perturbed`: Perturbed text snippet, which is the source text rewritten to alter the selected word along the specified target demographic attribute. For example, if the selected word is "Lily" and target is "man", all references to "Lily" (eg. pronouns) in the source text are altered to refer to a man. Note that some examples may be unchanged, either due to the lack of demographic information, or ambiguity of the task; given the subjective nature of identifying demographic terms and attributes, we allow some room for interpretation provided the rewrite does not perpetuate harmful social biases.
### Data Splits
- `train`: 94966
- `valid`: 10551
## Dataset Creation
### Curation Rationale
We constructed PANDA to create and release the first large scale dataset of demographic text perturbations. This enables the training of the first neural perturber model, which outperforms heuristic approaches.
### Source Data
#### Initial Data Collection and Normalization
We employed 524 crowdworkers to create PANDA examples over the span of several months. Annotators were tasked with rewriting text snippets sourced from popular English text corpora. For more information on the task UI and methodology, see our paper *Perturbation Augmentation for Fairer NLP*.
### Annotations
#### Annotation process
PANDA was collected in a 3 stage annotation process:
1. Span identification: Annotators select demographic terms in source text samples.
2. Attribute identification: Identified demographic terms are annotated for gender/race/age attributes, such as "man", "Asian", "old" etc.
3. Rewrite text: Annotators rewrite text by modifying the selected entity to reflect the target demographic attribute. Annotators are encouraged to create minimal edits, eg. "George" -> "Georgina".
The annotation process is explained in more detail in our paper.
#### Who are the annotators?
PANDA was annotated by English speaking Amazon Mechanical Turk workers. We included a voluntary demographic survey along with annotation tasks that did not contribute to pay. For a breakdown of annotators' demographic identities, see our paper.
### Personal and Sensitive Information
PANDA does not contain identifying information about annotators.
## Considerations for Using the Data
### Social Impact of Dataset
By releasing the first large scale dataset of demographic text rewrites, we hope to enable exciting future work in fairness in NLP toward more scalable, automated approaches to reducing biases in datasets and language models.
Furthermore, PANDA aims to be diverse in text domain and demographic representation. PANDA includes a large proportion of non-binary gender annotations, which are underrepresented in existing text corpora and prior fairness datasets. Text examples vary in length, with examples spanning single sentences and long Wikipedia passages, and are sourced from a variety of text corpora that can be used to train a domain agnostic perturber.
### Discussion of Biases
For this work, we sourced our annotated data from a range of sources to ensure: (i) permissive data licensing, (ii) that our perturber works well on downstream applications such as NLU classification tasks, and (iii) that our perturber can handle data from multiple domains to be maximally useful. However, we acknowledge that there may be other existing biases in PANDA as a result of our data sourcing choices. For example, it is possible that data sources like BookWiki primarily contain topics of interest to people with a certain amount of influence and educational access, people from the so-called “Western world”, etc. Other topics that might be interesting and relevant to others may be missing or only present in limited quantities. The present approach can only weaken associations inherited from the data sources we use, but in future work, we would love to explore the efficacy of our approach on text from other sources that contain a wider range of topics and text domain differences.
### Other Known Limitations
Our augmentation process can sometimes create nonexistent versions of real people, such as discussing an English King Victor (not a historical figure), as opposed to a Queen Victoria (a historical figure). We embrace the counterfactuality of many of our perturbations, but the lack of guaranteed factuality means that our approach may not be well-suited to all NLP tasks. For example, it might not be suitable for augmenting misinformation detection datasets, because peoples’ names, genders, and other demographic information should not be changed.
## Additional Information
### Dataset Curators
Rebecca Qian, Candace Ross, Jude Fernandes, Douwe Kiela and Adina Williams.
### Licensing Information
PANDA is released under the MIT license.
### Citation Information
https://arxiv.org/abs/2205.12586
### Contributions
Thanks to [@Rebecca-Qian](https://github.com/Rebecca-Qian) for adding this dataset. |
parambharat/malayalam_asr_corpus | 2022-12-11T13:05:27.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|common_voice",
"source_datasets:extended|openslr",
"language:ml",
"license:cc-by-4.0",
"region:us"
] | parambharat | The corpus contains roughly 10 hours of audio and trasncripts in Malayalam language. The transcripts have beedn de-duplicated using exact match deduplication. | @misc{https://doi.org/10.48550/arxiv.2211.09536,
doi = {10.48550/ARXIV.2211.09536},
url = {https://arxiv.org/abs/2211.09536},
author = {Kumar, Gokul Karthik and S, Praveen and Kumar, Pratyush and Khapra, Mitesh M. and Nandakumar, Karthik},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
title = {Towards Building Text-To-Speech Systems for the Next Billion Users},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
@misc{https://doi.org/10.48550/arxiv.2205.12446,
doi = {10.48550/ARXIV.2205.12446},
url = {https://arxiv.org/abs/2205.12446},
author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
} | null | 3 | 7 | ---
annotations_creators:
- found
language:
- ml
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Malayalam ASR Corpus
size_categories:
- 1K<n<10K
source_datasets:
- extended|common_voice
- extended|openslr
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for [Malayalam Asr Corpus]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@parambharat](https://github.com/parambharat) for adding this dataset. |
ZongqianLi/qa_sc_lv7simple | 2022-12-27T09:31:32.000Z | [
"region:us"
] | ZongqianLi | null | null | null | 0 | 7 | Entry not found |
Ruth-Ann/jampatoisnli | 2022-12-31T03:25:34.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"multilinguality:other-english-based-creole",
"size_categories:n<1K",
"source_dataset... | Ruth-Ann | null | null | null | 0 | 7 | ---
annotations_creators:
- expert-generated
language:
- jam
language_creators:
- expert-generated
- found
license:
- other
multilinguality:
- monolingual
- other-english-based-creole
pretty_name: JamPatoisNLI
size_categories:
- n<1K
source_datasets:
- original
tags:
- creole
- low-resource-language
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# 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:**
- jampatoisnli.github.io
- **Repository:**
- https://github.com/ruth-ann/jampatoisnli
- **Paper:**
- https://arxiv.org/abs/2212.03419
- **Point of Contact:**
- Ruth-Ann Armsrong: armstrongruthanna@gmail.com
### Dataset Summary
JamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois.
Many of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from
a major world language and a distinctive grammar reflecting the languages of the original speakers and the process
of language birth by creolization. This gives them a distinctive place in exploring the effectiveness of transfer
from large monolingual or multilingual pretrained models.
### Supported Tasks and Leaderboards
Natural language inference
### Languages
Jamaican Patois
### Data Fields
premise, hypothesis, label
### Data Splits
Train: 250
Val: 200
Test: 200
### Data set creation + Annotations
Premise collection:
97% of examples from Twitter; remaining pulled from literature and online cultural website
Hypothesis construction:
For each premise, hypothesis written by native speaker (our first author) so that pair’s classification would be E, N or C
Label validation:
Random sample of 100 sentence pairs double annotated by fluent speakers
### Social Impact of Dataset
JamPatoisNLI is a low-resource language dataset in an English-based Creole spoken in the Caribbean,
Jamaican Patois. The creation of the dataset contributes to expanding the scope of NLP research
to under-explored languages across the world.
### Dataset Curators
[@ruth-ann](https://github.com/ruth-ann)
### Citation Information
@misc{https://doi.org/10.48550/arxiv.2212.03419,
doi = {10.48550/ARXIV.2212.03419},
url = {https://arxiv.org/abs/2212.03419},
author = {Armstrong, Ruth-Ann and Hewitt, John and Manning, Christopher},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7},
title = {JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
### Contributions
Thanks to Prof. Christopher Manning and John Hewitt for their contributions, guidance, facilitation and support related to the creation of this dataset.
|
hanamizuki-ai/genshin-voice-v3.3-mandarin | 2022-12-31T05:01:47.000Z | [
"task_categories:text-to-speech",
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"source_datasets:original",
"language:zh",
"region:us"
] | hanamizuki-ai | null | null | null | 14 | 7 | ---
language:
- zh
multilinguality:
- monolingual
pretty_name: Genshin Voice
source_datasets:
- original
task_categories:
- text-to-speech
- automatic-speech-recognition
dataset_info:
features:
- name: audio
dtype: audio
- name: language
dtype: string
- name: npcName
dtype: string
- name: text
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 36412736429.25
num_examples: 75033
download_size: 18251937481
dataset_size: 36412736429.25
---
# Dataset Card for Genshin Voice
## Dataset Description
### Dataset Summary
The Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game.
### Languages
The text in the dataset is in Mandarin.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The data was obtained by unpacking the [Genshin Impact](https://genshin.hoyoverse.com/) game.
#### Who are the source language producers?
The language producers are the employee of [Hoyoverse](https://hoyoverse.com/) and contractors from [EchoSky Studio](http://qx.asiacu.com/).
### Annotations
The dataset contains official annotations from the game, including ingame speaker name and transcripts.
## Additional Information
### Dataset Curators
The dataset was created by [w4123](https://github.com/w4123) initially in his [GitHub repository](https://github.com/w4123/GenshinVoice).
### Licensing Information
Copyright © COGNOSPHERE. All Rights Reserved. |
theatticusproject/maud | 2023-01-02T22:50:04.000Z | [
"license:cc-by-4.0",
"region:us"
] | theatticusproject | null | null | null | 1 | 7 | ---
license: cc-by-4.0
---
|
irds/clinicaltrials_2021_trec-ct-2021 | 2023-01-05T02:54:09.000Z | [
"task_categories:text-retrieval",
"source_datasets:irds/clinicaltrials_2021",
"region:us"
] | irds | null | null | null | 1 | 7 | ---
pretty_name: '`clinicaltrials/2021/trec-ct-2021`'
viewer: false
source_datasets: ['irds/clinicaltrials_2021']
task_categories:
- text-retrieval
---
# Dataset Card for `clinicaltrials/2021/trec-ct-2021`
The `clinicaltrials/2021/trec-ct-2021` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/clinicaltrials#clinicaltrials/2021/trec-ct-2021).
# Data
This dataset provides:
- `queries` (i.e., topics); count=75
- `qrels`: (relevance assessments); count=35,832
- For `docs`, use [`irds/clinicaltrials_2021`](https://huggingface.co/datasets/irds/clinicaltrials_2021)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/clinicaltrials_2021_trec-ct-2021', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/clinicaltrials_2021_trec-ct-2021', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
|
irds/gov_trec-web-2002 | 2023-01-05T03:04:33.000Z | [
"task_categories:text-retrieval",
"source_datasets:irds/gov",
"region:us"
] | irds | null | null | null | 0 | 7 | ---
pretty_name: '`gov/trec-web-2002`'
viewer: false
source_datasets: ['irds/gov']
task_categories:
- text-retrieval
---
# Dataset Card for `gov/trec-web-2002`
The `gov/trec-web-2002` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov#gov/trec-web-2002).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50
- `qrels`: (relevance assessments); count=56,650
- For `docs`, use [`irds/gov`](https://huggingface.co/datasets/irds/gov)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov_trec-web-2002', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'description': ..., 'narrative': ...}
qrels = load_dataset('irds/gov_trec-web-2002', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Craswell2002TrecWeb,
title={Overview of the TREC-2002 Web Track},
author={Nick Craswell and David Hawking},
booktitle={TREC},
year={2002}
}
```
|
irds/msmarco-qna | 2023-01-05T03:42:08.000Z | [
"task_categories:text-retrieval",
"region:us"
] | irds | null | null | null | 0 | 7 | ---
pretty_name: '`msmarco-qna`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `msmarco-qna`
The `msmarco-qna` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-qna#msmarco-qna).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=9,048,606
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/msmarco-qna', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ..., 'url': ..., 'msmarco_passage_id': ..., 'msmarco_document_id': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Bajaj2016Msmarco,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang},
booktitle={InCoCo@NIPS},
year={2016}
}
```
|
irds/nyt | 2023-01-05T03:47:43.000Z | [
"task_categories:text-retrieval",
"region:us"
] | irds | null | null | null | 0 | 7 | ---
pretty_name: '`nyt`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `nyt`
The `nyt` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/nyt#nyt).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,864,661
This dataset is used by: [`nyt_trec-core-2017`](https://huggingface.co/datasets/irds/nyt_trec-core-2017), [`nyt_wksup`](https://huggingface.co/datasets/irds/nyt_wksup), [`nyt_wksup_train`](https://huggingface.co/datasets/irds/nyt_wksup_train), [`nyt_wksup_valid`](https://huggingface.co/datasets/irds/nyt_wksup_valid)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/nyt', 'docs')
for record in docs:
record # {'doc_id': ..., 'headline': ..., 'body': ..., 'source_xml': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Sandhaus2008Nyt,
title={The new york times annotated corpus},
author={Sandhaus, Evan},
journal={Linguistic Data Consortium, Philadelphia},
volume={6},
number={12},
pages={e26752},
year={2008}
}
```
|
rachit8562/mel_spectogram_bird_audio | 2023-01-07T08:18:21.000Z | [
"region:us"
] | rachit8562 | null | null | null | 0 | 7 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Chlorischloris
'1': Columbapalumbus
'2': Corvusfrugilegus
'3': Delichonurbicum
'4': Dendrocoposmajor
'5': Passermontanus
'6': Phoenicurusochruros
'7': Sittaeuropaea
'8': Turdusmerula
'9': Turduspilaris
splits:
- name: train
num_bytes: 1732741674.28153
num_examples: 61376
- name: test
num_bytes: 311839995.5024702
num_examples: 10832
download_size: 1955670248
dataset_size: 2044581669.7840002
---
# Dataset Card for "mel_spectogram_bird_audio"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Hack90/virus_dna_dataset | 2023-08-26T13:07:54.000Z | [
"region:us"
] | Hack90 | null | null | null | 2 | 7 | ---
dataset_info:
features:
- name: id
dtype: string
- name: sequence
dtype: string
- name: name
dtype: string
- name: description
dtype: string
- name: features
dtype: int64
- name: seq_length
dtype: int64
splits:
- name: train
num_bytes: 6621468623
num_examples: 2602437
download_size: 2319826398
dataset_size: 6621468623
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
[Needs More Information]
# Dataset Card for virus_dna_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)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
A collection of full virus genome dna, the dataset was built from NCBI data
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
DNA
## Dataset Structure
### Data Instances
{ 'Description' : 'NC_030848.1 Haloarcula californiae icosahedral...', 'dna_sequence' : 'TCATCTC TCTCTCT CTCTCTT GTTCCCG CGCCCGC CCGCCC...',
'sequence_length':'35787', 'organism_id':' AB063393.2'}
### Data Fields
{ 'Description' : 'this contains the description about the DNA sequence contained in the NCBI dataset', 'dna_sequence' : 'this contains the dna sequence grouped by 7 nucleotides',
'sequence_length':'this contains the length of the dna sequence'}
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
The goal of this dataset was to make it easier to train an LLM on virus DNA
### Source Data
#### Initial Data Collection and Normalization
DNA sequences were grouped by 7 nucleotides to make it easier to tokenize. Only full genomes were selected
#### Who are the source language producers?
Viruses :)
### Annotations
#### Annotation process
NCBI
#### Who are the annotators?
NCBI
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
Make it easier to train LLMs on virus DNA
### Discussion of Biases
Only virus data that has been sequenced and upload into NCBI is contained in here
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Hassan Ahmed
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] |
Laplace04/KoreanSummarizeAiHub | 2023-01-10T10:33:39.000Z | [
"license:other",
"region:us"
] | Laplace04 | null | null | null | 0 | 7 | ---
license: other
---
> 《 License 》
>
> 1. 본 AI데이터 등을 이용할 때에는 반드시 한국지능정보사회진흥원의 사업결과임을 밝혀야 하며, 본 AI데이터 등을 이용한 2차적 저작물에도 동일하게 밝혀야 합니다.
>
> 2. 국외에 소재하는 법인, 단체 또는 개인이 AI데이터 등을 이용하기 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다.
>
> 3. 본 AI데이터 등의 국외 반출을 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다.
>
> 4. 본 AI데이터는 인공지능 학습모델의 학습용으로만 사용할 수 있습니다. 한국지능정보사회진흥원은 AI데이터 등의 이용의 목적이나 방법, 내용 등이 위법하거나 부적합하다고 판단될 경우 제공을 거부할 수 있으며, 이미 제공한 경우 이용의 중지와 AI 데이터 등의 환수, 폐기 등을 요구할 수 있습니다.
>
> 5. 제공 받은 AI데이터 등을 수행기관 등과 한국지능정보사회진흥원의 승인을 받지 않은 다른 법인, 단체 또는 개인에게 열람하게 하거나 제공, 양도, 대여, 판매하여서는 안됩니다.
>
> 6. AI데이터 등에 대해서 제 4항에 따른 목적 외 이용, 제5항에 따른 무단 열람, 제공, 양도, 대여, 판매 등의 결과로 인하여 발생하는 모든 민・형사 상의 책임은 AI데이터 등을 이용한 법인, 단체 또는 개인에게 있습니다.
>
> 7. 이용자는 AI 허브 제공 데이터셋 내에 개인정보 등이 포함된 것이 발견된 경우, 즉시 AI 허브에 해당 사실을 신고하고 다운로드 받은 데이터셋을 삭제하여야 합니다.
>
> 8. AI 허브로부터 제공받은 비식별 정보(재현정보 포함)를 인공지능 서비스 개발 등의 목적으로 안전하게 이용하여야 하며, 이를 이용해서 개인을 재식별하기 위한 어떠한 행위도 하여서는 안됩니다.
>
> 9. 향후 한국지능정보사회진흥원에서 활용사례・성과 등에 관한 실태조사를 수행 할 경우 이에 성실하게 임하여야 합니다.
|
kuroneko5943/jd21 | 2023-01-10T15:51:26.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:zh",
"license:apache-2.0",
"jd",
"region:us"
] | kuroneko5943 | GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems. | \ | null | 3 | 7 | ---
annotations_creators:
- found
language:
- zh
language_creators:
- crowdsourced
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: jd21
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- jd
task_categories:
- text-classification
task_ids:
- sentiment-classification
--- |
MatthewWaller/cifar_stable_diffusion | 2023-01-16T20:51:00.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"region:us"
] | MatthewWaller | null | null | null | 0 | 7 | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 136240130
num_examples: 60000
download_size: 137069319
dataset_size: 136240130
task_categories:
- text-to-image
size_categories:
- n<1K
--- |
gabrielaltay/pmcoa | 2023-01-17T01:13:20.000Z | [
"region:us"
] | gabrielaltay | null | null | null | 0 | 7 | ---
dataset_info:
features:
- name: text
dtype: string
- name: pmid
dtype: string
- name: accession_id
dtype: string
- name: license
dtype: string
- name: last_updated
dtype: string
- name: retracted
dtype: string
- name: citation
dtype: string
- name: decoded_as
dtype: string
- name: journal
dtype: string
- name: year
dtype: int32
- name: doi
dtype: string
- name: oa_subset
dtype: string
splits:
- name: train
num_bytes: 206274456770
num_examples: 4935779
- name: validation
num_bytes: 4046140044
num_examples: 87794
download_size: 111297924087
dataset_size: 210320596814
---
# Dataset Card for "pmcoa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tomekkorbak/pile-pii-scrubadub | 2023-02-07T15:26:41.000Z | [
"task_categories:text-classification",
"task_categories:other",
"task_ids:acceptability-classification",
"task_ids:text-scoring",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:extended|the_pile",
"la... | tomekkorbak | null | null | null | 2 | 7 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
pretty_name: pile-pii-scrubadub
size_categories:
- 1M<n<10M
source_datasets:
- extended|the_pile
tags:
- pii
- personal
- identifiable
- information
- pretraining-with-human-feedback
task_categories:
- text-classification
- other
task_ids:
- acceptability-classification
- text-scoring
---
# Dataset Card for pile-pii-scrubadub
## Dataset Description
- **Repository: https://github.com/tomekkorbak/aligned-pretraining-objectives**
- **Paper: Arxiv link to be added**
### Dataset Summary
This dataset contains text from [The Pile](https://huggingface.co/datasets/the_pile), annotated based on the personal idenfitiable information (PII) in each sentence.
Each document (row in the dataset) is segmented into sentences, and each sentence is given a score: the percentage of words in it that are classified as PII by [Scrubadub](https://scrubadub.readthedocs.io/en/stable/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
This dataset is taken from [The Pile](https://huggingface.co/datasets/the_pile), which is English text.
## Dataset Structure
### Data Instances
1949977
### Data Fields
- texts (sequence): a list of the sentences in the document (segmented using [SpaCy](https://spacy.io/))
- meta (dict): the section of [The Pile](https://huggingface.co/datasets/the_pile) from which it originated
- scores (sequence): a score for each sentence in the `texts` column indicating the percent of words that are detected as PII by [Scrubadub](https://scrubadub.readthedocs.io/en/stable/)
- avg_score (float64): the average of the scores listed in the `scores` column
- num_sents (int64): the number of sentences (and scores) in that document
### Data Splits
Training set only
## Dataset Creation
### Curation Rationale
This is labeled text from [The Pile](https://huggingface.co/datasets/the_pile), a large dataset of text in English. The PII is labeled so that generative language models can be trained to avoid generating PII.
### Source Data
#### Initial Data Collection and Normalization
This is labeled text from [The Pile](https://huggingface.co/datasets/the_pile).
#### Who are the source language producers?
Please see [The Pile](https://huggingface.co/datasets/the_pile) for the source of the dataset.
### Annotations
#### Annotation process
For each sentence, [Scrubadub](https://scrubadub.readthedocs.io/en/stable/) was used to detect:
- email addresses
- addresses and postal codes
- phone numbers
- credit card numbers
- US social security numbers
- vehicle plates numbers
- dates of birth
- URLs
- login credentials
#### Who are the annotators?
[Scrubadub](https://scrubadub.readthedocs.io/en/stable/)
### Personal and Sensitive Information
This dataset contains all PII that was originally contained in [The Pile](https://huggingface.co/datasets/the_pile), with all detected PII annotated.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset contains examples of real PII (conveniently annotated in the text!). Please take care to avoid misusing it or putting anybody in danger by publicizing their information.
This dataset is intended for research purposes only. We cannot guarantee that all PII has been detected, and we cannot guarantee that models trained using it will avoid generating PII.
We do not recommend deploying models trained on this data.
### Discussion of Biases
This dataset contains all biases from The Pile discussed in their paper: https://arxiv.org/abs/2101.00027
### Other Known Limitations
The PII in this dataset was detected using imperfect automated detection methods. We cannot guarantee that the labels are 100% accurate.
## Additional Information
### Dataset Curators
[The Pile](https://huggingface.co/datasets/the_pile)
### Licensing Information
From [The Pile](https://huggingface.co/datasets/the_pile): PubMed Central: [MIT License](https://github.com/EleutherAI/pile-pubmedcentral/blob/master/LICENSE)
### Citation Information
Paper information to be added
### Contributions
[The Pile](https://huggingface.co/datasets/the_pile) |
svjack/bloom-dialogue-generate-ds-en | 2023-01-26T03:08:24.000Z | [
"region:us"
] | svjack | null | null | null | 0 | 7 | ---
dataset_info:
features:
- name: question
dtype: string
- name: dialogue_text
dtype: string
- name: dialogue
sequence: string
- name: repo
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 33783729
num_examples: 8378
download_size: 34957337
dataset_size: 33783729
---
# Dataset Card for "bloom-dialogue-generate-ds-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nbalepur/expository_documents_medicine | 2023-01-29T20:16:36.000Z | [
"region:us"
] | nbalepur | null | null | null | 0 | 7 | ---
dataset_info:
features:
- name: aspect
dtype: string
- name: title
dtype: string
- name: web_sentences_with_desc
sequence: string
- name: web_sentences_no_desc
sequence: string
- name: output
dtype: string
- name: output_aug
dtype: string
splits:
- name: test
num_bytes: 52889067
num_examples: 169
- name: train
num_bytes: 177551118.56296295
num_examples: 590
- name: val
num_bytes: 25579398.437037036
num_examples: 85
download_size: 140551296
dataset_size: 256019584.0
---
# Dataset Card for "expository_documents_medicine"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rasyidf/coffee-beans | 2023-02-07T22:06:44.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"size_categories:n<1K",
"source_datasets:original",
"language:id",
"license:mit",
"region:us"
] | rasyidf | null | null | null | 0 | 7 | ---
license: mit
annotations_creators:
- expert-generated
language_creators:
- expert-generated
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
language:
- id
pretty_name: Cofee Beans Grading
size_categories:
- n<1K
dataset_info:
features:
- name: image_file_path
dtype: string
- name: image
dtype: image
- name: labels
dtype:
class_label:
names:
'1': 0
'2': 1
'3': 2
'0': 3
splits:
- name: train
num_bytes: 202.173.747
num_examples: 200
- name: validation
num_bytes: 57.633.053
num_examples: 400
- name: test
num_bytes: 28.985.470
num_examples: 1400
download_size: 288792270
dataset_size: 2000
---
# Dataset Card for Beans
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** N/A
### Dataset Summary
Coffee Beans Grading
### Supported Tasks and Leaderboards
- `image-classification`: Based on a coffee bean grading, the goal of this task is to grade single beans for clusterization.
### Languages
Indonesia
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
'image_file_path': '/root/.cache/huggingface/datasets/downloads/extracted/0aaa78294d4bf5114f58547e48d91b7826649919505379a167decb629aa92b0a/train/bean_rust/bean_rust_train.109.jpg',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x500 at 0x16BAA72A4A8>,
'labels': 1
}
```
### Data Fields
The data instances have the following fields:
- `image_file_path`: a `string` filepath to an image.
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `labels`: an `int` classification label.
Class Label Mappings:
```json
{
"1": 0,
"2": 1,
"3": 2,
}
```
### Data Splits
| |train|validation|test|
|-------------|----:|---------:|---:|
|# of examples|1400 |400 |200 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
### Contributions
|
karukas/pubmed-abstract-matching | 2023-02-09T21:18:46.000Z | [
"region:us"
] | karukas | null | null | null | 0 | 7 | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 2237510856
num_examples: 119924
- name: validation
num_bytes: 126574623
num_examples: 6633
- name: test
num_bytes: 126357120
num_examples: 6658
download_size: 1156008015
dataset_size: 2490442599
---
# Dataset Card for "pubmed-abstract-matching"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
andstor/output | 2023-07-09T14:22:59.000Z | [
"task_categories:text-generation",
"language:en",
"license:mit",
"region:us"
] | andstor | This is a dataset consisting of the output from various language models and datasets. | @misc{storhaug2022output,
title = {Output Dataset},
author={André Storhaug},
year={2023}
} | null | 0 | 7 | ---
license: mit
task_categories:
- text-generation
language:
- en
dataset_info:
- config_name: gpt2-xl
features:
- name: id
dtype: string
- name: part
sequence: int32
- name: prompt
dtype: string
- name: reference
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- name: prediction
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- name: meta
struct:
- name: subset
dtype: string
splits:
- name: andstor.the_pile_github.greedy
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num_examples: 22169
download_size: 66419674
dataset_size: 60221138
- config_name: EleutherAI.gpt-j-6B
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splits:
- name: andstor.the_pile_github.greedy
num_bytes: 67625587
num_examples: 20665
download_size: 73049509
dataset_size: 67625587
- config_name: NinedayWang.PolyCoder-2.7B
features:
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dtype: string
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sequence: int32
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splits:
- name: andstor.the_pile_github.greedy
num_bytes: 58822858
num_examples: 20342
download_size: 63717236
dataset_size: 58822858
- config_name: Salesforce.codegen-16B-multi
features:
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dtype: string
- name: part
sequence: int32
- name: prompt
dtype: string
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dtype: string
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- name: meta
struct:
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dtype: string
splits:
- name: THUDM.humaneval_x.greedy
num_bytes: 2509745
num_examples: 820
download_size: 2694784
dataset_size: 2509745
- config_name: openai.gpt-3.5-turbo-0613
features:
- name: id
dtype: string
- name: part
sequence: int32
- name: prompt
dtype: string
- name: reference
dtype: string
- name: prediction
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- name: meta
struct:
- name: subset
dtype: string
splits:
- name: THUDM.humaneval_x.greedy
num_bytes: 958178
num_examples: 820
download_size: 1067958
dataset_size: 958178
- config_name: openai.gpt-4-0613
features:
- name: id
dtype: string
- name: part
sequence: int32
- name: prompt
dtype: string
- name: reference
dtype: string
- name: prediction
dtype: string
- name: ended
dtype: bool
- name: meta
struct:
- name: subset
dtype: string
splits:
- name: THUDM.humaneval_x.greedy
num_bytes: 875401
num_examples: 820
- name: THUDM.humaneval_x.random
num_bytes: 906274
num_examples: 820
download_size: 1995455
dataset_size: 1781675
---
# Dataset Card for Output
## 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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/andstor/lm-output-dataset
- **Repository:** https://github.com/andstor/lm-output-dataset
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [André Storhaug](mailto:andr3.storhaug@gmail.com)
### Dataset Summary
This is a dataset of various language model outputs from different datasets.
### 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 [@andstor](https://github.com/andstor) for adding this dataset.
|
HuggingFaceH4/helpful-anthropic-raw | 2023-02-20T09:00:56.000Z | [
"license:mit",
"human-feedback",
"region:us"
] | HuggingFaceH4 | null | null | null | 2 | 7 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: demonstration
dtype: string
splits:
- name: train
num_bytes: 34540085.04363476
num_examples: 65499
download_size: 0
dataset_size: 34540085.04363476
license: mit
pretty_name: Helpful Raw Anthropic
tags:
- human-feedback
---
# Dataset Card for "helpful-raw-anthropic"
This is a dataset derived from Anthropic's [HH-RLHF data](https://huggingface.co/datasets/Anthropic/hh-rlhf) of instructions and model-generated demonstrations. We combined training splits from the following two subsets:
* `helpful-base`
* `helpful-online`
To convert the multi-turn dialogues into `(instruction, demonstration)` pairs, just the first response from the Assistant was included. This heuristic captures the most obvious answers, but overlooks more complex questions where multiple turns were required to get a helpful response. Some additional filtering is likely required (e.g. defining a minimun length or computing ROUGE-L scores with the instruction/demonstration). |
jonathan-roberts1/Satellite-Images-of-Hurricane-Damage | 2023-03-31T14:53:28.000Z | [
"license:cc-by-4.0",
"arxiv:1807.01688",
"region:us"
] | jonathan-roberts1 | null | null | null | 0 | 7 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': flooded or damaged buildings
'1': undamaged buildings
splits:
- name: train
num_bytes: 25588780
num_examples: 10000
download_size: 26998688
dataset_size: 25588780
license: cc-by-4.0
---
# Dataset Card for "Satellite-Images-of-Hurricane-Damage"
## Dataset Description
- **Paper** [Deep learning based damage detection on post-hurricane satellite imagery](https://arxiv.org/pdf/1807.01688.pdf)
- **Data** [IEEE-Dataport](https://ieee-dataport.org/open-access/detecting-damaged-buildings-post-hurricane-satellite-imagery-based-customized)
- **Split** Train_another
- **GitHub** [DamageDetection](https://github.com/qcao10/DamageDetection)
## Split Information
This HuggingFace dataset repository contains just the Train_another split.
### Licensing Information
[CC BY 4.0](https://ieee-dataport.org/open-access/detecting-damaged-buildings-post-hurricane-satellite-imagery-based-customized)
## Citation Information
[Deep learning based damage detection on post-hurricane satellite imagery](https://arxiv.org/pdf/1807.01688.pdf)
[IEEE-Dataport](https://ieee-dataport.org/open-access/detecting-damaged-buildings-post-hurricane-satellite-imagery-based-customized)
```
@misc{sdad-1e56-18,
title = {Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks},
author = {Cao, Quoc Dung and Choe, Youngjun},
year = 2018,
publisher = {IEEE Dataport},
doi = {10.21227/sdad-1e56},
url = {https://dx.doi.org/10.21227/sdad-1e56}
}
@article{cao2018deep,
title={Deep learning based damage detection on post-hurricane satellite imagery},
author={Cao, Quoc Dung and Choe, Youngjun},
journal={arXiv preprint arXiv:1807.01688},
year={2018}
}
``` |
ontocord/oig-retrieval | 2023-02-19T18:06:12.000Z | [
"license:cc-by-4.0",
"region:us"
] | ontocord | null | null | null | 0 | 7 | ---
license: cc-by-4.0
---
|
tasksource/folio | 2023-05-31T13:40:30.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"language:en",
"license:cc",
"arxiv:2209.00840",
"region:us"
] | tasksource | null | null | null | 4 | 7 | ---
license: cc
task_categories:
- text-classification
language:
- en
task_ids:
- natural-language-inference
- multi-input-text-classification
---
https://github.com/Yale-LILY/FOLIO
```
@article{han2022folio,
title={FOLIO: Natural Language Reasoning with First-Order Logic},
author = {Han, Simeng and Schoelkopf, Hailey and Zhao, Yilun and Qi, Zhenting and Riddell, Martin and Benson, Luke and Sun, Lucy and Zubova, Ekaterina and Qiao, Yujie and Burtell, Matthew and Peng, David and Fan, Jonathan and Liu, Yixin and Wong, Brian and Sailor, Malcolm and Ni, Ansong and Nan, Linyong and Kasai, Jungo and Yu, Tao and Zhang, Rui and Joty, Shafiq and Fabbri, Alexander R. and Kryscinski, Wojciech and Lin, Xi Victoria and Xiong, Caiming and Radev, Dragomir},
journal={arXiv preprint arXiv:2209.00840},
url = {https://arxiv.org/abs/2209.00840},
year={2022}
}
``` |
podbilabs/sroie-donut | 2023-02-22T23:42:13.000Z | [
"region:us"
] | podbilabs | null | null | null | 0 | 7 | Entry not found |
Duskfallcrew/Photography | 2023-02-26T10:25:35.000Z | [
"task_categories:text-to-image",
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"new zealand",
"photography",
"region:us"
] | Duskfallcrew | null | null | null | 2 | 7 | ---
license: creativeml-openrail-m
task_categories:
- text-to-image
- image-classification
language:
- en
tags:
- new zealand
- photography
pretty_name: Duskfall Photography
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
## Dataset Description
- **https://duskfallcrew.carrd.co/:**
- **https://discord.gg/Da7s8d3KJ7**
### Dataset Summary
A mixture of photography and other goods from Dusfkallcrew that has been either curated or taken by duskfall crew. Some may or may not be AI generated.
This template was generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Languages
English mainly, but that's because the data is largely of New Zealand.
### Source Data
### Personal and Sensitive Information
No personal data has been included in this data, it is ALL a mixture of AI generated and personally created photography.
If data is not from what is said, then the data set will be cleaned of any errors.
## Considerations for Using the Data
### Social Impact of Dataset
Too much time on my hands.
### Discussion of Biases
It's a DSLR, it's a samsung phne - its' a BIRD ITS A - you get my point. There shoudl be no bias other than where I can actually take photos.
### Licensing Information
Do not sell this dataset, however you may use it as you see fit in TEXT TO IMAGE stable diffusion models.
Your outputs are your own, and the datawithin is free to be used for AI generation models.
### Citation Information
None needed.
### Contributions
If you'd like to contribute please do so! |
SaylorTwift/Gutenberg | 2023-03-02T14:33:50.000Z | [
"region:us"
] | SaylorTwift | null | null | null | 3 | 7 | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: author
dtype: string
- name: authoryearofbirth
dtype: int32
- name: authoryearofdeath
dtype: int32
- name: downloads
dtype: int32
- name: text
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 20279073235
num_examples: 54810
download_size: 12344747182
dataset_size: 20279073235
---
# Dataset Card for "Gutenberg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sebchw/musdb18 | 2023-03-23T08:28:41.000Z | [
"region:us"
] | sebchw | MUSDB18 music source separation dataset
to open original stem file (mp4), which is done internally you need stempeg library.
Outcome of mp4 file is a 3 dimensional np_array [n_stems, n_samples, sample_rate].
firt dimension meanings: {
0: mixture.
1: drugs,
2: bass,
3: others,
4:vocals,
}
Original dataset is not cutted in any parts, but here I cut each song in 10 seconds chunks with 1 sec overlap. | null | null | 0 | 7 | Entry not found |
rcds/occlusion_swiss_judgment_prediction | 2023-03-28T08:19:29.000Z | [
"task_categories:text-classification",
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|swiss_judgment_prediction",
"language:de",
... | rcds | This dataset contains an implementation of occlusion for the SwissJudgmentPrediction task. | @misc{baumgartner_nina_occlusion_2022,
title = {From Occlusion to Transparancy – An Occlusion-Based Explainability Approach for Legal Judgment Prediction in Switzerland},
shorttitle = {From Occlusion to Transparancy},
abstract = {Natural Language Processing ({NLP}) models have been used for more and more complex tasks such as Legal Judgment Prediction ({LJP}). A {LJP} model predicts the outcome of a legal case by utilizing its facts. This increasing deployment of Artificial Intelligence ({AI}) in high-stakes domains such as law and the involvement of sensitive data has increased the need for understanding such systems. We propose a multilingual occlusion-based explainability approach for {LJP} in Switzerland and conduct a study on the bias using Lower Court Insertion ({LCI}). We evaluate our results using different explainability metrics introduced in this thesis and by comparing them to high-quality Legal Expert Annotations using Inter Annotator Agreement. Our findings show that the model has a varying understanding of the semantic meaning and context of the facts section, and struggles to distinguish between legally relevant and irrelevant sentences. We also found that the insertion of a different lower court can have an effect on the prediction, but observed no distinct effects based on legal areas, cantons, or regions. However, we did identify a language disparity with Italian performing worse than the other languages due to representation inequality in the training data, which could lead to potential biases in the prediction in multilingual regions of Switzerland. Our results highlight the challenges and limitations of using {NLP} in the judicial field and the importance of addressing concerns about fairness, transparency, and potential bias in the development and use of {NLP} systems. The use of explainable artificial intelligence ({XAI}) techniques, such as occlusion and {LCI}, can help provide insight into the decision-making processes of {NLP} systems and identify areas for improvement. Finally, we identify areas for future research and development in this field in order to address the remaining limitations and challenges.},
author = {{Baumgartner, Nina}},
year = {2022},
langid = {english}
} | null | 0 | 7 | ---
annotations_creators:
- expert-generated
language:
- de
- fr
- it
- en
language_creators:
- expert-generated
- found
license: cc-by-sa-4.0
multilinguality:
- multilingual
pretty_name: OcclusionSwissJudgmentPrediction
size_categories:
- 1K<n<10K
source_datasets:
- extended|swiss_judgment_prediction
tags:
- explainability-judgment-prediction
- occlusion
task_categories:
- text-classification
- other
task_ids: []
---
# Dataset Card for "OcclusionSwissJudgmentPrediction": An implementation of an occlusion based explainability method for Swiss judgment prediction
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Summary](#dataset-summary)
- [Documents](#documents)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset **str**ucture](#dataset-**str**ucture)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Summary
This dataset contains an implementation of occlusion for the SwissJudgmentPrediction task.
Note that this dataset only provides a test set and should be used in comination with the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset.
### Documents
Occlusion-Swiss-Judgment-Prediction is a subset of the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset.
The Swiss-Judgment-Prediction dataset is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), the publication year, the legal area and the canton of origin per case. Occlusion-Swiss-Judgment-Prediction extends this dataset by adding sentence splitting with explainability labels.
### Supported Tasks and Leaderboards
OcclusionSwissJudgmentPrediction can be used for performing the occlusion in the legal judgment prediction task.
### Languages
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
## Dataset structure
### Data Instances
## Data Instances
**Multilingual use of the dataset**
When the dataset is used in a multilingual setting selecting the the 'all_languages' flag:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/occlusion_swiss_judgment_prediction', 'all')
```
**Monolingual use of the dataset**
When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/occlusion_swiss_judgment_prediction', 'de')
```
### Data Fields
The following data fields are provided for documents (Test_1/Test_2/Test_3/Test_4):
id: (**int**) a unique identifier of the for the document <br/>
year: (**int**) the publication year<br/>
label: (**str**) the judgment outcome: dismissal or approval<br/>
language: (**str**) one of (de, fr, it)<br/>
region: (**str**) the region of the lower court<br/>
canton: (**str**) the canton of the lower court<br/>
legal area: (**str**) the legal area of the case<br/>
explainability_label (**str**): the explainability label assigned to the occluded text: Supports judgment, Opposes judgment, Neutral, Baseline<br/>
occluded_text (**str**): the occluded text<br/>
text: (**str**) the facts of the case w/o the occluded text except for cases w/ explainability label "Baseline" (contain entire facts)<br/>
Note that Baseline cases are only contained in version 1 of the occlusion test set, since they do not change from experiment to experiment.
### Data Splits (Including Swiss Judgment Prediction)
Language | Subset | Number of Rows (Test_1/Test_2/Test_3/Test_4)
| ----------- | ----------- | ----------- |
German| de | __427__ / __1366__ / __3567__ / __7235__
French | fr | __307__ / __854__ / __1926__ / __3279__
Italian | it | __299__ /__919__ / __2493__ / __5733__
All | all | __1033__ / __3139__ / __7986__/ __16247__
Language | Subset | Number of Documents (is the same for Test_1/Test_2/Test_3/Test_4)
| ----------- | ----------- | ----------- |
German| de | __38__
French | fr | __36__
Italian | it | __34__
All | all | __108__
## Dataset Creation
### Curation Rationale
The dataset was curated by Niklaus et al. (2021) and Nina Baumgartner.
### Source Data
#### Initial Data Collection and Normalization
The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition a subset of the test set (27 cases in German, 24 in French and 23 in Italian spanning over the years 2017 an 20200) was annotated by legal experts, splitting sentences/group of sentences and annotated with one of the following explainability label: Supports judgment, Opposes Judgment and Neutral. The test sets have each sentence/ group of sentence once occluded, enabling an analysis of the changes in the model's performance. The legal expert annotation were conducted from April 2020 to August 2020.
#### Who are the annotators?
Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch). The group of legal experts consists of Thomas Lüthi (lawyer), Lynn Grau (law student at master's level) and Angela Stefanelli (law student at master's level).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## Additional Information
### Dataset Curators
Niklaus et al. (2021) and Nina Baumgartner
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2000-2020
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
```
@misc{baumgartner_nina_occlusion_2022,
title = {From Occlusion to Transparancy – An Occlusion-Based Explainability Approach for Legal Judgment Prediction in Switzerland},
shorttitle = {From Occlusion to Transparancy},
abstract = {Natural Language Processing ({NLP}) models have been used for more and more complex tasks such as Legal Judgment Prediction ({LJP}). A {LJP} model predicts the outcome of a legal case by utilizing its facts. This increasing deployment of Artificial Intelligence ({AI}) in high-stakes domains such as law and the involvement of sensitive data has increased the need for understanding such systems. We propose a multilingual occlusion-based explainability approach for {LJP} in Switzerland and conduct a study on the bias using Lower Court Insertion ({LCI}). We evaluate our results using different explainability metrics introduced in this thesis and by comparing them to high-quality Legal Expert Annotations using Inter Annotator Agreement. Our findings show that the model has a varying understanding of the semantic meaning and context of the facts section, and struggles to distinguish between legally relevant and irrelevant sentences. We also found that the insertion of a different lower court can have an effect on the prediction, but observed no distinct effects based on legal areas, cantons, or regions. However, we did identify a language disparity with Italian performing worse than the other languages due to representation inequality in the training data, which could lead to potential biases in the prediction in multilingual regions of Switzerland. Our results highlight the challenges and limitations of using {NLP} in the judicial field and the importance of addressing concerns about fairness, transparency, and potential bias in the development and use of {NLP} systems. The use of explainable artificial intelligence ({XAI}) techniques, such as occlusion and {LCI}, can help provide insight into the decision-making processes of {NLP} systems and identify areas for improvement. Finally, we identify areas for future research and development in this field in order to address the remaining limitations and challenges.},
author = {{Baumgartner, Nina}},
year = {2022},
langid = {english}
}
```
### Contributions
Thanks to [@ninabaumgartner](https://github.com/ninabaumgartner) for adding this dataset. |
rcds/lower_court_insertion_swiss_judgment_prediction | 2023-03-28T08:19:04.000Z | [
"task_categories:text-classification",
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|swiss_judgment_prediction",
"language:de",
... | rcds | This dataset contains an implementation of lower court insertion for the SwissJudgmentPrediction task. | @misc{baumgartner_nina_occlusion_2022,
title = {From Occlusion to Transparancy – An Occlusion-Based Explainability Approach for Legal Judgment Prediction in Switzerland},
shorttitle = {From Occlusion to Transparancy},
abstract = {Natural Language Processing ({NLP}) models have been used for more and more complex tasks such as Legal Judgment Prediction ({LJP}). A {LJP} model predicts the outcome of a legal case by utilizing its facts. This increasing deployment of Artificial Intelligence ({AI}) in high-stakes domains such as law and the involvement of sensitive data has increased the need for understanding such systems. We propose a multilingual occlusion-based explainability approach for {LJP} in Switzerland and conduct a study on the bias using Lower Court Insertion ({LCI}). We evaluate our results using different explainability metrics introduced in this thesis and by comparing them to high-quality Legal Expert Annotations using Inter Annotator Agreement. Our findings show that the model has a varying understanding of the semantic meaning and context of the facts section, and struggles to distinguish between legally relevant and irrelevant sentences. We also found that the insertion of a different lower court can have an effect on the prediction, but observed no distinct effects based on legal areas, cantons, or regions. However, we did identify a language disparity with Italian performing worse than the other languages due to representation inequality in the training data, which could lead to potential biases in the prediction in multilingual regions of Switzerland. Our results highlight the challenges and limitations of using {NLP} in the judicial field and the importance of addressing concerns about fairness, transparency, and potential bias in the development and use of {NLP} systems. The use of explainable artificial intelligence ({XAI}) techniques, such as occlusion and {LCI}, can help provide insight into the decision-making processes of {NLP} systems and identify areas for improvement. Finally, we identify areas for future research and development in this field in order to address the remaining limitations and challenges.},
author = {{Baumgartner, Nina}},
year = {2022},
langid = {english}
} | null | 0 | 7 | ---
annotations_creators:
- expert-generated
language:
- de
- fr
- it
- en
language_creators:
- expert-generated
- found
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
pretty_name: LowerCourtInsertionSwissJudgmentPrediction
size_categories:
- 1K<n<10K
source_datasets:
- extended|swiss_judgment_prediction
tags:
- explainability-judgment-prediction
task_categories:
- text-classification
- other
task_ids: []
---
# Dataset Card for "LowerCourtInsertionSwissJudgmentPrediction": An implementation of lower court insertion bias analysis for Swiss judgment prediction
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Summary](#dataset-summary)
- [Documents](#documents)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset **str**ucture](#dataset-**str**ucture)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Summary
This dataset contains an implementation of lower-court-insertion for the SwissJudgmentPrediction task.
Note that this dataset only provides a test set and should be used in comination with the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset.
### Documents
Lower-Court-Insertion-Swiss-Judgment-Prediction is a subset of the [Swiss-Judgment-Prediction](https://huggingface.co/datasets/swiss_judgment_prediction) dataset.
The Swiss-Judgment-Prediction dataset is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), the publication year, the legal area and the canton of origin per case. Lower-Court-Insertion-Swiss-Judgment-Prediction extends this dataset by adding lower court insertion.
### Supported Tasks and Leaderboards
LowerCourtInsertionSwissJudgmentPrediction can be used for performing the LowerCourtInsertion in the legal judgment prediction task.
### Languages
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
## Dataset structure
### Data Instances
#### Multilingual use of the dataset
When the dataset is used in a multilingual setting selecting the the 'all' flag:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/lower_court_insertion_swiss_judgment_prediction', 'all')
```
#### Monolingual use of the dataset
When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/lower-court-insertion_swiss_judgment_prediction', 'de')
```
### Data Fields
The following data fields are provided for documents (test):
id: (**int**) a unique identifier of the for the document<br/>
year: (**int**) the publication year<br/>
label: (**str**) the judgment outcome: dismissal or approval<br/>
language: (**str**) one of (de, fr, it)<br/>
region: (**str**) the region of the lower court<br/>
canton: (**str**) the canton of the lower court<br/>
legal area: (**str**) the legal area of the case<br/>
explainability_label: (**str**) the explainability label assigned to the occluded text: (Lower court, Baseline)<br/>
text: (**str**) the facts of the case w/o the occluded text except for cases w/ explainability label "Baseline" (contain entire facts)<br/>
lower_court: (**str**) the inserted lower_court (for Baseline there is no insertion)<br/>
### Data Splits (Including Swiss Judgment Prediction)
Language | Subset | Number of Rows (Test)
|-----|-----|------|
German| de| __378__
French | fr| __414__
Italian | it| __335__
All | all | __1127__
Language | Subset | Number of Documents (Test)
| ----------- | ----------- | ----------- |
German| de | __38__
French | fr | __36__
Italian | it | __34__
All | all | __108__
## Dataset Creation
### Curation Rationale
The dataset was curated by Niklaus et al. (2021) and Nina Baumgartner.
### Source Data
#### Initial Data Collection and Normalization
The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. In addition the a subset of the test set (27 cases in German, 24 in French and 23 in Italian spanning over the years 2017 an 20200) was annotated by legal experts with the lower court. These lower court annotations were then use the insert each lower court into each case once (instead of the original lower court). Allowing an analysis of the changes in the models performance for each inserted lower court, giving insight into a possible bias among them. The legal expert annotation were conducted from April 2020 to August 2020.
#### Who are the annotators?
Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch). The group of legal experts consists of Thomas Lüthi (lawyer), Lynn Grau (law student at master's level) and Angela Stefanelli (law student at master's level).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## Additional Information
### Dataset Curators
Niklaus et al. (2021) and Nina Baumgartner
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2000-2020
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
```
@misc{baumgartner_nina_occlusion_2019,
title = {From Occlusion to Transparancy – An Occlusion-Based Explainability Approach for Legal Judgment Prediction in Switzerland},
shorttitle = {From Occlusion to Transparancy},
abstract = {Natural Language Processing ({NLP}) models have been used for more and more complex tasks such as Legal Judgment Prediction ({LJP}). A {LJP} model predicts the outcome of a legal case by utilizing its facts. This increasing deployment of Artificial Intelligence ({AI}) in high-stakes domains such as law and the involvement of sensitive data has increased the need for understanding such systems. We propose a multilingual occlusion-based explainability approach for {LJP} in Switzerland and conduct a study on the bias using Lower Court Insertion ({LCI}). We evaluate our results using different explainability metrics introduced in this thesis and by comparing them to high-quality Legal Expert Annotations using Inter Annotator Agreement. Our findings show that the model has a varying understanding of the semantic meaning and context of the facts section, and struggles to distinguish between legally relevant and irrelevant sentences. We also found that the insertion of a different lower court can have an effect on the prediction, but observed no distinct effects based on legal areas, cantons, or regions. However, we did identify a language disparity with Italian performing worse than the other languages due to representation inequality in the training data, which could lead to potential biases in the prediction in multilingual regions of Switzerland. Our results highlight the challenges and limitations of using {NLP} in the judicial field and the importance of addressing concerns about fairness, transparency, and potential bias in the development and use of {NLP} systems. The use of explainable artificial intelligence ({XAI}) techniques, such as occlusion and {LCI}, can help provide insight into the decision-making processes of {NLP} systems and identify areas for improvement. Finally, we identify areas for future research and development in this field in order to address the remaining limitations and challenges.},
author = {{Baumgartner, Nina}},
year = {2022},
langid = {english}
}
```
### Contributions
Thanks to [@ninabaumgartner](https://github.com/ninabaumgartner) for adding this dataset.
|
domro11/lectures | 2023-03-11T19:41:06.000Z | [
"license:openrail",
"region:us"
] | domro11 | null | null | null | 0 | 7 | ---
license: openrail
---
|
LangChainDatasets/question-answering-state-of-the-union | 2023-03-12T21:39:00.000Z | [
"license:mit",
"region:us"
] | LangChainDatasets | null | null | null | 4 | 7 | ---
license: mit
---
|
trpakov/chest-xray-classification | 2023-03-13T07:23:48.000Z | [
"task_categories:image-classification",
"roboflow",
"roboflow2huggingface",
"Biology",
"region:us"
] | trpakov | null | \ | null | 1 | 7 | ---
task_categories:
- image-classification
tags:
- roboflow
- roboflow2huggingface
- Biology
---
<div align="center">
<img width="640" alt="trpakov/chest-xray-classification" src="https://huggingface.co/datasets/trpakov/chest-xray-classification/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['PNEUMONIA', 'NORMAL']
```
### Number of Images
```json
{'test': 582, 'valid': 1165, 'train': 12230}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("trpakov/chest-xray-classification", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/3](https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/3?ref=roboflow2huggingface)
### Citation
```
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.ai on December 8, 2021 at 12:45 AM GMT
It includes 13977 images.
Pneumonia are annotated in folder format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 640x640 (Stretch)
The following augmentation was applied to create 3 versions of each source image:
* Random shear of between -3° to +3° horizontally and -2° to +2° vertically
* Random brigthness adjustment of between -5 and +5 percent
* Random exposure adjustment of between -5 and +5 percent
|
kentsui/minimath | 2023-03-14T17:31:06.000Z | [
"region:us"
] | kentsui | null | null | null | 0 | 7 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: source
dtype: string
- name: Rationale
dtype: string
- name: annotated_formula
dtype: string
- name: linear_formula
dtype: string
splits:
- name: train
num_bytes: 1114848
num_examples: 2880
download_size: 543796
dataset_size: 1114848
---
# Dataset Card for "minimath"
The objective of `minimath` is to evaluate the mathematical capability of language model in a quick while diverse setting.
The dataset is composed of sampling from the below dataset:
https://huggingface.co/datasets/math_dataset
https://huggingface.co/datasets/math_qa
https://huggingface.co/datasets/competition_math
https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_math_jsonl
https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pcuenq/face_synthetics_spiga | 2023-03-20T08:53:26.000Z | [
"region:us"
] | pcuenq | null | null | null | 8 | 7 | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_seg
dtype: image
- name: landmarks
dtype: string
- name: spiga
sequence:
sequence: float64
- name: spiga_seg
dtype: image
splits:
- name: train
num_bytes: 31081737215.0
num_examples: 100000
download_size: 31009656222
dataset_size: 31081737215.0
---
# Dataset Card for "face_synthetics_spiga"
This is a copy of [Microsoft FaceSynthetics dataset](https://github.com/microsoft/FaceSynthetics) with [SPIGA](https://github.com/andresprados/SPIGA) landmark annotations. For a copy of the original FaceSynthetics dataset with no extra annotations, please refer to [pcuenq/face_synthetics](https://huggingface.co/pcuenq/face_synthetics).
Please, refer to the original [license](LICENSE.txt), which we replicate in this repo. The SPIGA annotations were created by Hugging Face Inc. and are distributed under the MIT license.
This dataset was prepared using the code below. It iterates through the dataset to perform landmark detection using SPIGA, and then to create visualizations of the features. Visualization is performed using Matplotlib to render to memory buffers.
```Python
import numpy as np
from datasets import load_dataset
from spiga.inference.config import ModelConfig
from spiga.inference.framework import SPIGAFramework
dataset_name = "pcuenq/face_synthetics"
faces = load_dataset(dataset_name)
faces = faces["train"]
# ## Obtain SPIGA features
processor = SPIGAFramework(ModelConfig("300wpublic"))
# We obtain the bbox from the existing landmarks in the dataset.
# We could use `dlib`, but this should be faster.
# Note that the `landmarks` are stored as strings.
def parse_landmarks(landmarks_str):
landmarks = landmarks_str.strip().split('\n')
landmarks = [k.split(' ') for k in landmarks]
landmarks = [(float(x), float(y)) for x, y in landmarks]
return landmarks
def bbox_from_landmarks(landmarks_str):
landmarks = parse_landmarks(landmarks_str)
landmarks_x, landmarks_y = zip(*landmarks)
x_min, x_max = min(landmarks_x), max(landmarks_x)
y_min, y_max = min(landmarks_y), max(landmarks_y)
width = x_max - x_min
height = y_max - y_min
# Give it a little room; I think it works anyway
x_min -= 5
y_min -= 5
width += 10
height += 10
bbox = (x_min, y_min, width, height)
return bbox
def spiga_process(example):
image = example["image"]
image = np.array(image)
# BGR
image = image[:, :, ::-1]
bbox = bbox_from_landmarks(example["landmarks"])
features = processor.inference(image, [bbox])
landmarks = features["landmarks"][0]
example["spiga"] = landmarks
return example
# For some reason this map doesn't work with num_proc > 1 :(
# TODO: run inference on GPU
faces = faces.map(spiga_process)
# ## "Segmentation"
# We use bezier paths to draw contours and areas.
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.path import Path
import PIL
def get_patch(landmarks, color='lime', closed=False):
contour = landmarks
ops = [Path.MOVETO] + [Path.LINETO]*(len(contour)-1)
facecolor = (0, 0, 0, 0) # Transparent fill color, if open
if closed:
contour.append(contour[0])
ops.append(Path.CLOSEPOLY)
facecolor = color
path = Path(contour, ops)
return patches.PathPatch(path, facecolor=facecolor, edgecolor=color, lw=4)
# Draw to a buffer.
def conditioning_from_landmarks(landmarks, size=512):
# Precisely control output image size
dpi = 72
fig, ax = plt.subplots(1, figsize=[size/dpi, size/dpi], tight_layout={'pad':0})
fig.set_dpi(dpi)
black = np.zeros((size, size, 3))
ax.imshow(black)
face_patch = get_patch(landmarks[0:17])
l_eyebrow = get_patch(landmarks[17:22], color='yellow')
r_eyebrow = get_patch(landmarks[22:27], color='yellow')
nose_v = get_patch(landmarks[27:31], color='orange')
nose_h = get_patch(landmarks[31:36], color='orange')
l_eye = get_patch(landmarks[36:42], color='magenta', closed=True)
r_eye = get_patch(landmarks[42:48], color='magenta', closed=True)
outer_lips = get_patch(landmarks[48:60], color='cyan', closed=True)
inner_lips = get_patch(landmarks[60:68], color='blue', closed=True)
ax.add_patch(face_patch)
ax.add_patch(l_eyebrow)
ax.add_patch(r_eyebrow)
ax.add_patch(nose_v)
ax.add_patch(nose_h)
ax.add_patch(l_eye)
ax.add_patch(r_eye)
ax.add_patch(outer_lips)
ax.add_patch(inner_lips)
plt.axis('off')
fig.canvas.draw()
buffer, (width, height) = fig.canvas.print_to_buffer()
assert width == height
assert width == size
buffer = np.frombuffer(buffer, np.uint8).reshape((height, width, 4))
buffer = buffer[:, :, 0:3]
plt.close(fig)
return PIL.Image.fromarray(buffer)
def spiga_segmentation(example):
landmarks = example["spiga"]
example['spiga_seg'] = conditioning_from_landmarks(landmarks)
return example
faces = faces.map(spiga_segmentation, num_proc=16)
faces.push_to_hub(f"{dataset_name}_spiga")
```
|
semeru/code-text-javascript | 2023-03-23T20:05:02.000Z | [
"license:mit",
"arxiv:1909.09436",
"region:us"
] | semeru | null | null | null | 3 | 7 | ---
license: mit
Programminglanguage: "JavaScript"
version: "N/A"
Date: "Codesearchnet(Jun 2020 - paper release date)"
Contaminated: "Very Likely"
Size: "Standar Tokenizer (TreeSitter)"
---
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-text/javascript in Semeru
# CodeXGLUE -- Code-To-Text
## Task Definition
The task is to generate natural language comments for a code, and evaluted by [smoothed bleu-4](https://www.aclweb.org/anthology/C04-1072.pdf) score.
## Dataset
The dataset we use comes from [CodeSearchNet](https://arxiv.org/pdf/1909.09436.pdf) and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- **repo:** the owner/repo
- **path:** the full path to the original file
- **func_name:** the function or method name
- **original_string:** the raw string before tokenization or parsing
- **language:** the programming language
- **code/function:** the part of the `original_string` that is code
- **code_tokens/function_tokens:** tokenized version of `code`
- **docstring:** the top-level comment or docstring, if it exists in the original string
- **docstring_tokens:** tokenized version of `docstring`
### Data Statistic
| Programming Language | Training | Dev | Test |
| :------------------- | :------: | :----: | :----: |
| JavaScript | 58,025 | 3,885 | 3,291 |
## Reference
<pre><code>@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}</code></pre>
|
mohammadjavadpirhadi/fake-news-detection-dataset-english | 2023-03-26T16:10:25.000Z | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] | mohammadjavadpirhadi | null | null | null | 0 | 7 | ---
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: subject
dtype: string
- name: date
dtype: string
- name: label
dtype:
class_label:
names:
'0': real
'1': fake
splits:
- name: train
num_bytes: 93521249
num_examples: 35918
- name: test
num_bytes: 23506751
num_examples: 8980
download_size: 71290190
dataset_size: 117028000
license: mit
task_categories:
- text-classification
language:
- en
pretty_name: Fake News Detection English
size_categories:
- 10K<n<100K
---
# Dataset Card for "fake-news-detection-dataset-english"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RussianNLP/rucola | 2023-03-27T18:47:12.000Z | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:ru",
"license:apache-2.0",
"arxiv:2210.12814",
"arxiv:2008.00401",
"region:us"
] | RussianNLP | Russian Corpus of Linguistic Acceptability (RuCoLA) is a novel benchmark of 13.4k sentences labeled as acceptable or not. RuCoLA combines in-domain sentences manually collected from linguistic literature and out-of-domain sentences produced by nine machine translation and paraphrase generation models. The motivation behind the out-of-domain set is to facilitate the practical use of acceptability judgments for improving language generation. Each unacceptable sentence is additionally labeled with four standard and machine-specific coarse-grained categories: morphology, syntax, semantics, and hallucinations. | @inproceedings{mikhailov-etal-2022-rucola,
title = "{R}u{C}o{LA}: {R}ussian Corpus of Linguistic Acceptability",
author = "Mikhailov, Vladislav and
Shamardina, Tatiana and
Ryabinin, Max and
Pestova, Alena and
Smurov, Ivan and
Artemova, Ekaterina",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.348",
pages = "5207--5227",
abstract = "Linguistic acceptability (LA) attracts the attention of the research community due to its many uses, such as testing the grammatical knowledge of language models and filtering implausible texts with acceptability classifiers.However, the application scope of LA in languages other than English is limited due to the lack of high-quality resources.To this end, we introduce the Russian Corpus of Linguistic Acceptability (RuCoLA), built from the ground up under the well-established binary LA approach. RuCoLA consists of 9.8k in-domain sentences from linguistic publications and 3.6k out-of-domain sentences produced by generative models. The out-of-domain set is created to facilitate the practical use of acceptability for improving language generation.Our paper describes the data collection protocol and presents a fine-grained analysis of acceptability classification experiments with a range of baseline approaches.In particular, we demonstrate that the most widely used language models still fall behind humans by a large margin, especially when detecting morphological and semantic errors. We release RuCoLA, the code of experiments, and a public leaderboard to assess the linguistic competence of language models for Russian.",
} | null | 1 | 7 | ---
license: apache-2.0
task_categories:
- text-classification
language:
- ru
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** https://rucola-benchmark.com
- **Repository:** https://github.com/RussianNLP/RuCoLA
- **Paper:** https://aclanthology.org/2022.emnlp-main.348/
- **ArXiv:** https://arxiv.org/abs/2210.12814
- **Leaderboard:** https://rucola-benchmark.com/leaderboard
- **Point of Contact:** vmikhailovhse@gmail.com
- **Language:** Russian
### Dataset Summary
Russian Corpus of Linguistic Acceptability (RuCoLA) is a novel benchmark of 13.4k sentences labeled as acceptable or not. RuCoLA combines in-domain sentences manually collected from linguistic literature and out-of-domain sentences produced by nine machine translation and paraphrase generation models.
The motivation behind the out-of-domain set is to facilitate the practical use of acceptability judgments for improving language generation.
Each unacceptable sentence is additionally labeled with four standard and machine-specific coarse-grained categories: morphology, syntax, semantics, and hallucinations.
## Dataset Structure
### Supported Tasks and Leaderboards
- **Task:** binary classification.
- **Metrics:** MCC/Acc.
- **Leaderboard:** https://rucola-benchmark.com/leaderboard
### Languages
Russian.
### Data Instances
```
{
"id": 19,
"sentence": "Люк останавливает удачу от этого.",
"label": 0,
"error_type": "Hallucination",
"detailed_source": "WikiMatrix"}
}
```
The example in English for illustration purposes:
```
{
"id": 19,
"sentence": "Luck stops luck from doing this.",
"label": 0,
"error_type": "Hallucination",
"detailed_source": "WikiMatrix"}
}
```
### Data Fields
- ```id (int64)```: the sentence's id.
- ```sentence (str)```: the sentence.
- ```label (str)```: the target class. "1" refers to "acceptable", while "0" corresponds to "unacceptable".
- ```error_type (str)```: the coarse-grained violation category (Morphology, Syntax, Semantics, or Hallucination); "0" if the sentence is acceptable.
- ```detailed_source```: the data source.
### Data Splits
RuCoLA consists of the training, development, and private test sets organised under two subsets: in-domain (linguistic publications) and out-of-domain (texts produced by natural language generation models).
- ```train```: 7869 in-domain samples (```"data/in_domain_train.csv"```).
- ```validation```: 2787 in-domain and out-of-domain samples. The in-domain (```"data/in_domain_dev.csv"```) and out-of-domain (```"data/out_of_domain_dev.csv"```) validation sets are merged into ```"data/dev.csv"``` for convenience.
- ```test```: 2789 in-domain and out-of-domain samples (```"data/test.csv"```).
## Dataset Creation
### Curation Rationale
- **In-domain Subset:** The in-domain sentences and the corresponding authors’ acceptability judgments are *manually* drawn from fundamental linguistic textbooks, academic publications, and methodological materials.
- **Out-of-domain Subset:** The out-of-domain sentences are produced by nine open-source MT and paraphrase generation models.
### Source Data
<details>
<summary>Linguistic publications and resources</summary>
|Original source |Transliterated source |Source id |
|---|---|---|
|[Проект корпусного описания русской грамматики](http://rusgram.ru) | [Proekt korpusnogo opisaniya russkoj grammatiki](http://rusgram.ru/)|Rusgram |
|Тестелец, Я.Г., 2001. *Введение в общий синтаксис*. Федеральное государственное бюджетное образовательное учреждение высшего образования Российский государственный гуманитарный университет.|Yakov Testelets. 2001. Vvedeniye v obschiy sintaksis. Russian State University for the Humanities. |Testelets |
|Лютикова, Е.А., 2010. *К вопросу о категориальном статусе именных групп в русском языке*. Вестник Московского университета. Серия 9. Филология, (6), pp.36-76. |Ekaterina Lutikova. 2010. K voprosu o kategorial’nom statuse imennykh grup v russkom yazyke. Moscow University Philology Bulletin. |Lutikova |
|Митренина, О.В., Романова, Е.Е. and Слюсарь, Н.А., 2017. *Введение в генеративную грамматику*. Общество с ограниченной ответственностью "Книжный дом ЛИБРОКОМ". |Olga Mitrenina et al. 2017. Vvedeniye v generativnuyu grammatiku. Limited Liability Company “LIBROCOM”. |Mitrenina |
|Падучева, Е.В., 2004. *Динамические модели в семантике лексики*. М.: Языки славянской культуры.| Elena Paducheva. 2004. Dinamicheskiye modeli v semantike leksiki. Languages of Slavonic culture. |Paducheva2004 |
|Падучева, Е.В., 2010. *Семантические исследования: Семантика времени и вида в русском языке; Семантика нарратива*. М.: Языки славянской культуры. | Elena Paducheva. 2010. Semanticheskiye issledovaniya: Semantika vremeni i vida v russkom yazyke; Semantika narrativa. Languages of Slavonic culture.|Paducheva2010 |
|Падучева, Е.В., 2013. *Русское отрицательное предложение*. М.: Языки славянской культуры |Elena Paducheva. 2013. Russkoye otritsatel’noye predlozheniye. Languages of Slavonic culture. |Paducheva2013 |
|Селиверстова, О.Н., 2004. *Труды по семантике*. М.: Языки славянской культуры | Olga Seliverstova. 2004. Trudy po semantike. Languages of Slavonic culture.|Seliverstova |
| Набор данных ЕГЭ по русскому языку | Shavrina et al. 2020. [Humans Keep It One Hundred: an Overview of AI Journey](https://aclanthology.org/2020.lrec-1.277/) |USE5, USE7, USE8 |
</details>
<details>
<summary>Machine-generated sentences</summary>
<br>
**Datasets**
|Original source |Source id|
|---|---|
|Mikel Artetxe and Holger Schwenk. 2019. [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00288/43523/Massively-Multilingual-Sentence-Embeddings-for)|Tatoeba |
|Holger Schwenk et al. 2021. [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://aclanthology.org/2021.eacl-main.115/)|WikiMatrix |
|Ye Qi et al. 2018. [When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?](https://aclanthology.org/N18-2084/)|TED |
|Alexandra Antonova and Alexey Misyurev. 2011. [Building a Web-Based Parallel Corpus and Filtering Out Machine-Translated Text](https://aclanthology.org/W11-1218/)|YandexCorpus |
**Models**
[EasyNMT models](https://github.com/UKPLab/EasyNMT):
1. OPUS-MT. Jörg Tiedemann and Santhosh Thottingal. 2020. [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/)
2. M-BART50. Yuqing Tang et al. 2020. [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401)
3. M2M-100. Angela Fan et al. 2021. [Beyond English-Centric Multilingual Machine Translation](https://jmlr.org/papers/volume22/20-1307/20-1307.pdf)
[Paraphrase generation models](https://github.com/RussianNLP/russian_paraphrasers):
1. [ruGPT2-Large](https://huggingface.co/sberbank-ai/rugpt2large)
2. [ruT5](https://huggingface.co/cointegrated/rut5-base-paraphraser)
3. mT5. Linting Xue et al. 2021. [mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer](https://aclanthology.org/2021.naacl-main.41/)
</details>
### Annotations
#### Annotation process
The out-of-domain sentences undergo a two-stage annotation procedure on [Toloka](https://toloka.ai), a crowd-sourcing platform for data labeling.
Each stage includes an unpaid training phase with explanations, control tasks for tracking annotation quality, and the main annotation task. Before starting, the worker is given detailed instructions describing the task, explaining the labels, and showing plenty of examples.
The instruction is available at any time during both the training and main annotation phases. To get access to the main phase, the worker should first complete the training phase by labeling more than 70% of its examples correctly. Each trained worker receives a page with five sentences, one of which is a control one.
We collect the majority vote labels via a dy- namic overlap from three to five workers after filtering them by response time and performance on control tasks.
- **Stage 1: Acceptability Judgments**
The first annotation stage defines whether a given sentence is acceptable or not. Access to the project is granted to workers certified as native speakers of Russian by Toloka and ranked top-60% workers according to the Toloka rating system.
Each worker answers 30 examples in the training phase. Each training example is accompanied by an explanation that appears in an incorrect answer.
The main annotation phase counts 3.6k machine-generated sentences. The pay rate is on average $2.55/hr, which is twice the amount of the hourly minimum wage in Russia. Each of 1.3k trained workers get paid, but we keep votes from only 960 workers whose annotation quality rate on the control sentences is more than 50%.
- **Stage 2: Violation Categories**
The second stage includes validation and annotation of sentences labeled unacceptable on Stage 1 according to five answer options: “Morphology”, “Syntax”, “Semantics”, “Hallucinations” and “Other”. The task is framed as a multi-label classification, i.e., the sentence may contain more than one violation in some rare cases or be re-labeled as acceptable.
We create a team of 30 annotators who are undergraduate BA and MA in philology and linguistics from several Russian universities. The students are asked to study the works on CoLA, TGEA, and hallucinations. We also hold an online seminar to discuss the works and clarify the task specifics. Each student undergoes platform-based training on 15 examples before moving onto the main phase of 1.3k sentences.
The students are paid on average $5.42/hr and are eligible to get credits for an academic course or an internship. This stage provides direct interaction between authors and students in a group chat. We keep submissions with more than 30 seconds of response time per page and collect the majority vote labels for each answer independently.
Sentences having more than one violation category or labeled as “Other” by the majority are filtered out.
### Personal and Sensitive Information
The annotators are warned about potentially sensitive topics in data (e.g., politics, culture, and religion).
## Considerations for Using the Data
### Social Impact of Dataset
RuCoLA may serve as training data for acceptability classifiers, which may benefit the quality of generated texts.
We recognize that such improvements in text generation may lead to misuse of LMs for malicious purposes. However, our corpus can be used to train adversarial defense and artificial text detection models.
We introduce a novel dataset for **research and development needs**, and the potential negative uses are not lost on us.
### Discussion of Biases
Although we aim to control the number of high-frequency tokens in the RuCoLA’s sentences, we assume that potential word frequency distribution shift between LMs’ pretraining corpora and our corpus can introduce bias in the evaluation.
Furthermore, linguistic publications represent a specific domain as the primary source of acceptability judgments. On the one hand, it can lead to a domain shift when using RuCoLA for practical purposes.
On the other hand, we observe moderate acceptability classification performance on the out-of-domain test, which spans multiple domains, ranging from subtitles to Wikipedia.
### Other Known Limitations
- **Data Collection**
Acceptability judgments datasets require a source of unacceptable sentences.
Collecting judgments from linguistic literature has become a standard practice replicated in multiple languages. However, this approach has several limitations. First, many studies raise concerns about the reliability and reproducibility of acceptability judgments. Second, the linguists’ judgments may limit data representativeness, as they may not reflect the errors that speakers tend to produce. Third, enriching acceptability judgments datasets is time-consuming, while creating new ones can be challenging due to limited resources, e.g., in low-resource languages.
- **Expert vs. Non-expert**
One of the open methodological questions on acceptability judgments is whether they should be collected from expert or non-expert speakers.
On the one hand, prior linguistic knowledge can introduce bias in reporting judgments. On the other hand, expertise may increase the quality of the linguists’ judgments over the ones of non-linguists. At the same time, the latter tend to be influenced by an individual’s exposure to ungrammatical language use.
The objective of involving students with a linguistic background is to maximize the annotation quality.
- **Fine-grained Annotation**
The coarse-grained annotation scheme of the RuCoLA’s unacceptable sentences relies on four major categories. While the annotation can be helpful for model error analysis, it limits the scope of LMs’ diagnostic evaluation concerning linguistic and machine-specific phenomena.
## Additional Information
### Dataset Curators
Correspondence: ```vmikhailovhse@gmail.com```
### Licensing Information
Our baseline code and acceptability labels are available under the Apache 2.0 license. The copyright (where applicable) of texts from the linguistic publications and resources remains with the original authors or publishers.
### Citation Information
```
@inproceedings{mikhailov-etal-2022-rucola,
title = "{R}u{C}o{LA}: {R}ussian Corpus of Linguistic Acceptability",
author = "Mikhailov, Vladislav and
Shamardina, Tatiana and
Ryabinin, Max and
Pestova, Alena and
Smurov, Ivan and
Artemova, Ekaterina",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.348",
pages = "5207--5227",
abstract = "Linguistic acceptability (LA) attracts the attention of the research community due to its many uses, such as testing the grammatical knowledge of language models and filtering implausible texts with acceptability classifiers.However, the application scope of LA in languages other than English is limited due to the lack of high-quality resources.To this end, we introduce the Russian Corpus of Linguistic Acceptability (RuCoLA), built from the ground up under the well-established binary LA approach. RuCoLA consists of 9.8k in-domain sentences from linguistic publications and 3.6k out-of-domain sentences produced by generative models. The out-of-domain set is created to facilitate the practical use of acceptability for improving language generation.Our paper describes the data collection protocol and presents a fine-grained analysis of acceptability classification experiments with a range of baseline approaches.In particular, we demonstrate that the most widely used language models still fall behind humans by a large margin, especially when detecting morphological and semantic errors. We release RuCoLA, the code of experiments, and a public leaderboard to assess the linguistic competence of language models for Russian.",
}
```
### Other
Please refer to our [paper](https://aclanthology.org/2022.emnlp-main.348/) for more details. |
koutch/JuICe | 2023-03-29T07:34:03.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"code",
"arxiv:1910.02216",
"region:us"
] | koutch | JuICe, a corpus of 1.5 million examples with a curated test set of 3.7K instances based on online programming assignments. | @article{DBLP:journals/corr/abs-1910-02216,
author = {Rajas Agashe and
Srinivasan Iyer and
Luke Zettlemoyer},
title = {JuICe: {A} Large Scale Distantly Supervised Dataset for Open Domain
Context-based Code Generation},
journal = {CoRR},
volume = {abs/1910.02216},
year = {2019},
url = {http://arxiv.org/abs/1910.02216},
eprinttype = {arXiv},
eprint = {1910.02216},
timestamp = {Wed, 09 Oct 2019 14:07:58 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-02216.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | null | 0 | 7 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: notebook
sequence:
- name: text
dtype: string
- name: cell_type
dtype: string
splits:
- name: validation
num_bytes: 19578995
num_examples: 1831
- name: test
num_bytes: 21651420
num_examples: 2115
download_size: 155457826
dataset_size: 41230415
license: cc-by-4.0
task_categories:
- question-answering
language:
- en
tags:
- code
pretty_name: juice
size_categories:
- 1K<n<10K
---
# Dataset Card for JuICe (A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation)
## Dataset Description
- **Homepage: [GitHub](https://github.com/rajasagashe/juice)**
- **Paper: [JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation](https://arxiv.org/abs/1910.02216)**
### Dataset Summary
The JuICe dataset was developed to study code generation conditioned on a long context history. For that purpose, the authors collected data from interactive coding environements (ICE) in Jupyter notebooks (JuICE). Since these notebooks contain interleaved code snippet cells and natural language markdown they are particularly useful for this task.
While the original [dataset](https://github.com/rajasagashe/juice) also contains a corpus of 1.5 million jupyter notebook examples, this version (redistributed on the hub for easier access), contains only the curated test set of 3.7K instances based on online programming assignments.
### Supported Tasks and Leaderboards
This dataset can be used for Natural Language to Code Generation tasks.
### Languages
Python, English
### Data Instances
```python
dataset = load_dataset("koutch/JuICe")
DatasetDict({
validation: Dataset({
features: ['question', 'answer', 'notebook'],
num_rows: 1831
})
test: Dataset({
features: ['question', 'answer', 'notebook'],
num_rows: 2115
})
})
```
### Data Fields
In short, each data row contains a programming `question` and an code `answer` to that programming question, answer which might require contextualized information in previous cells in the `notebook`
- `question`: Contextualized programming exercise/question to be answerred in the last cell of the jupyter notebook
- `notebook`: The ordered sequence of jupyter notebook cells which forms the full exercise context
- `text`: the raw content of the cell
- `cell_type`: code, markdown, or raw
- `answer`: The code implementation which answers to the question
### Data Splits
* validation: the dev split in the original paper
* test: the test split in the original paper
## Additional Information
### Citation Information
If you use the dataset or the code in your research, please cite the following paper:
```
@article{DBLP:journals/corr/abs-1910-02216,
author = {Rajas Agashe and
Srinivasan Iyer and
Luke Zettlemoyer},
title = {JuICe: {A} Large Scale Distantly Supervised Dataset for Open Domain
Context-based Code Generation},
journal = {CoRR},
volume = {abs/1910.02216},
year = {2019},
url = {http://arxiv.org/abs/1910.02216},
eprinttype = {arXiv},
eprint = {1910.02216},
timestamp = {Wed, 09 Oct 2019 14:07:58 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-02216.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |
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