text-classification bool 2 classes | text stringlengths 0 664k |
|---|---|
false |
# Dataset Card for People's Daily NER
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/OYE93/Chinese-NLP-Corpus/tree/master/NER/People's%20Daily)
- **Repository:** [Github](https://github.com/OYE93/Chinese-NLP-Corpus/)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
No citation available for this dataset.
### Contributions
Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset. |
true |
# Dataset Card for ASSET
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [ASSET Github repository](https://github.com/facebookresearch/asset)
- **Paper:** [ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations](https://www.aclweb.org/anthology/2020.acl-main.424/)
- **Point of Contact:** [Louis Martin](louismartincs@gmail.com)
### Dataset Summary
[ASSET](https://github.com/facebookresearch/asset) [(Alva-Manchego et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.424.pdf) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from [TurkCorpus]( https://github.com/cocoxu/simplification/) [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence
splitting in [HSplit](https://www.aclweb.org/anthology/D18-1081.pdf)), the simplifications in ASSET encompass a variety of rewriting transformations.
### Supported Tasks and Leaderboards
The dataset supports the evaluation of `text-simplification` systems. Success in this tasks is typically measured using the [SARI](https://huggingface.co/metrics/sari) and [FKBLEU](https://huggingface.co/metrics/fkbleu) metrics described in the paper [Optimizing Statistical Machine Translation for Text Simplification](https://www.aclweb.org/anthology/Q16-1029.pdf).
### Languages
The text in this dataset is in English (`en`).
## Dataset Structure
### Data Instances
- `simplification` configuration: an instance consists in an original sentence and 10 possible reference simplifications.
- `ratings` configuration: a data instance consists in an original sentence, a simplification obtained by an automated system, and a judgment of quality along one of three axes by a crowd worker.
### Data Fields
- `original`: an original sentence from the source datasets
- `simplifications`: in the `simplification` config, a set of reference simplifications produced by crowd workers.
- `simplification`: in the `ratings` config, a simplification of the original obtained by an automated system
- `aspect`: in the `ratings` config, the aspect on which the simplification is evaluated, one of `meaning`, `fluency`, `simplicity`
- `rating`: a quality rating between 0 and 100
### Data Splits
ASSET does not contain a training set; many models use [WikiLarge](https://github.com/XingxingZhang/dress) (Zhang and Lapata, 2017) for training.
Each input sentence has 10 associated reference simplified sentences. The statistics of ASSET are given below.
| | Dev | Test | Total |
| ----- | ------ | ---- | ----- |
| Input Sentences | 2000 | 359 | 2359 |
| Reference Simplifications | 20000 | 3590 | 23590 |
The test and validation sets are the same as those of TurkCorpus. The split was random.
There are 19.04 tokens per reference on average (lower than 21.29 and 25.49 for TurkCorpus and HSplit, respectively). Most (17,245) of the referece sentences do not involve sentence splitting.
## Dataset Creation
### Curation Rationale
ASSET was created in order to improve the evaluation of sentence simplification. It uses the same input sentences as the [TurkCorpus]( https://github.com/cocoxu/simplification/) dataset from [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). The 2,359 input sentences of TurkCorpus are a sample of "standard" (not simple) sentences from the [Parallel Wikipedia Simplification (PWKP)](https://www.informatik.tu-darmstadt.de/ukp/research_6/data/sentence_simplification/simple_complex_sentence_pairs/index.en.jsp) dataset [(Zhu et al., 2010)](https://www.aclweb.org/anthology/C10-1152.pdf), which come from the August 22, 2009 version of Wikipedia. The sentences of TurkCorpus were chosen to be of similar length [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). No further information is provided on the sampling strategy.
The TurkCorpus dataset was developed in order to overcome some of the problems with sentence pairs from Standard and Simple Wikipedia: a large fraction of sentences were misaligned, or not actually simpler [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). However, TurkCorpus mainly focused on *lexical paraphrasing*, and so cannot be used to evaluate simplifications involving *compression* (deletion) or *sentence splitting*. HSplit [(Sulem et al., 2018)](https://www.aclweb.org/anthology/D18-1081.pdf), on the other hand, can only be used to evaluate sentence splitting. The reference sentences in ASSET include a wider variety of sentence rewriting strategies, combining splitting, compression and paraphrasing. Annotators were given examples of each kind of transformation individually, as well as all three transformations used at once, but were allowed to decide which transformations to use for any given sentence.
An example illustrating the differences between TurkCorpus, HSplit and ASSET is given below:
> **Original:** He settled in London, devoting himself chiefly to practical teaching.
>
> **TurkCorpus:** He rooted in London, devoting himself mainly to practical teaching.
>
> **HSplit:** He settled in London. He devoted himself chiefly to practical teaching.
>
> **ASSET:** He lived in London. He was a teacher.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The input sentences are from English Wikipedia (August 22, 2009 version). No demographic information is available for the writers of these sentences. However, most Wikipedia editors are male (Lam, 2011; Graells-Garrido, 2015), which has an impact on the topics covered (see also [the Wikipedia page on Wikipedia gender bias](https://en.wikipedia.org/wiki/Gender_bias_on_Wikipedia)). In addition, Wikipedia editors are mostly white, young, and from the Northern Hemisphere [(Wikipedia: Systemic bias)](https://en.wikipedia.org/wiki/Wikipedia:Systemic_bias).
Reference sentences were written by 42 workers on Amazon Mechanical Turk (AMT). The requirements for being an annotator were:
- Passing a Qualification Test (appropriately simplifying sentences). Out of 100 workers, 42 passed the test.
- Being a resident of the United States, United Kingdom or Canada.
- Having a HIT approval rate over 95%, and over 1000 HITs approved.
No other demographic or compensation information is provided in the ASSET paper.
### Annotations
#### Annotation process
The instructions given to the annotators are available [here](https://github.com/facebookresearch/asset/blob/master/crowdsourcing/AMT_AnnotationInstructions.pdf).
#### 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
The dataset may contain some social biases, as the input sentences are based on Wikipedia. Studies have shown that the English Wikipedia contains both gender biases (Schmahl et al., 2020) and racial biases (Adams et al., 2019).
> Adams, Julia, Hannah Brückner, and Cambria Naslund. "Who Counts as a Notable Sociologist on Wikipedia? Gender, Race, and the “Professor Test”." Socius 5 (2019): 2378023118823946.
> Schmahl, Katja Geertruida, et al. "Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings." Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science. 2020.
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
ASSET was developed by researchers at the University of Sheffield, Inria,
Facebook AI Research, and Imperial College London. The work was partly supported by Benoît Sagot's chair in the PRAIRIE institute, funded by the French National Research Agency (ANR) as part of the "Investissements d’avenir" program (reference ANR-19-P3IA-0001).
### Licensing Information
[Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)
### Citation Information
```
@inproceedings{alva-manchego-etal-2020-asset,
title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations",
author = "Alva-Manchego, Fernando and
Martin, Louis and
Bordes, Antoine and
Scarton, Carolina and
Sagot, Beno{\^\i}t and
Specia, Lucia",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.424",
pages = "4668--4679",
}
```
This dataset card uses material written by [Juan Diego Rodriguez](https://github.com/juand-r).
### Contributions
Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset. |
false |
# Dataset Card for Igbo Monolingual Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_monoling
- **Repository:** https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_monoling
- **Paper:** https://arxiv.org/abs/2004.00648
### Dataset Summary
A dataset is a collection of Monolingual Igbo sentences.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Igbo (ig)
## Dataset Structure
### Data Instances
Here is an example from the bb-igbo config:
```
{'content': 'Ike Ekweremmadụ\n\nIke ịda jụụ otụ nkeji banyere oke ogbugbu na-eme n\'ala Naijiria agwụla Ekweremmadụ\n\nOsote onye-isi ndị ome-iwu Naịjirịa bụ Ike Ekweremadu ekwuola na ike agwụla ndị Sịnatị iji otu nkeji darajụụ akwanyere ndị egburu n\'ime oke ọgbaghara dị na Naịjirịa oge ọ bula.\n\nEkweremadu katọrọ mwakpọ na ogbugbu ndị Naịjirịa aka ha dị ọcha nke ndị Fulani na-achị ehi mere, kwuo na ike agwụla ndị ome- iwu ịkwanyere ha ugwu n\'otu nkeji\'\n\nCheta n\'otu ịzụka gara-aga ka emere akwam ozu mmadụ ruru iri asaa egburu na Local Gọọmenti Logo na Guma nke Benue Steeti, e be ihe kariri mmadụ iri ise ka akụkọ kwuru n\'egburu na Taraba Steeti.\n\nEkweremadu gosiri iwe gbasara ogbugbu ndị mmadụ na nzukọ ndị ome-iwu n\'ụbọchị taa, kwuo na Naịjirịa ga-ebu ụzọ nwe udo na nchekwa, tupu e kwuowa okwu iwulite obodo.\n\nỌ sịrị: "Ndị ome-iwu abụghị sọ ọsọ ndị ihe a metụtara, kama ndị Naịjirịa niile.\n\n\'Ike agwụla anyị iji otu nkeji dị jụụ maka nkwanye ugwu. Ihe anyị chọrọ bụ udo na nchekwa tupu echewa echịchị nwuli obodo."',
'date': '2018-01-19T17:07:38Z',
'description': "N'ihi oke ogbugbu ndị mmadụ na Naịjirịa gbagburu gburu, osota onyeisi ndị ome-iwu Naịjirịa bụ Ike Ekweremadu ekwuola na ihe Naịjiria chọrọ bụ nchekwa tara ọchịchị, tupu ekwuwa okwu ihe ọzọ.",
'headline': 'Ekweremadu: Ike agwụla ndị ụlọ ome iwu',
'source': 'https://www.bbc.com/igbo/42712250',
'tags': [],
'title': 'Ekweremadu: Ike agwụla ndị ụlọ ome iwu'}
```
### Data Fields
For config 'eze_goes_to_school':
- format, title, chapters
For config 'bbc-igbo' :
- source, title, description, date (Missing date values replaced with empty strings), headline, content, tags (Missing tags replaced with empty list)
For config 'igbo-radio':
- source, headline, author, date, description, content
For config 'jw-ot-igbo':
- format, title, chapters
For config 'jw-nt-igbo':
- format, title, chapters
For config 'jw-books':
- title, content, format, date (Missing date values replaced with empty strings)
For config 'jw-teta':
- title, content, format, date (Missing date values replaced with empty strings)
For config 'jw-ulo_nche':
- title, content, format, date (Missing date values replaced with empty strings)
For config 'jw-ulo_nche_naamu':
- title, content, format, date (Missing date values replaced with empty strings)
### Data Splits
| bbc-igbo | eze_goes_to_school |igbo-radio| jw-books|jw-nt-igbo| jw-ot-igbo | jw-teta |jw-ulo_nche |jw-ulo_nche_naamu
| ------------- |:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|
| 1297 | 1 | 440 | 48 | 27 | 39 | 37 | 55 | 88
## 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
@misc{ezeani2020igboenglish,
title={Igbo-English Machine Translation: An Evaluation Benchmark},
author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple},
year={2020},
eprint={2004.00648},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
### Contributions
Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset. |
false | # Dataset Card for MultiReQA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/google-research-datasets/MultiReQA
- **Repository:** https://github.com/google-research-datasets/MultiReQA
- **Paper:** https://arxiv.org/pdf/2005.02507.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data (also includes DuoRC but not specified in the official documentation)
### Supported Tasks and Leaderboards
- Question answering (QA)
- Retrieval question answering (ReQA)
### Languages
Sentence boundary annotation for SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, TextbookQA and DuoRC
## Dataset Structure
### Data Instances
The general format is:
`
{
"candidate_id": <candidate_id>,
"response_start": <response_start>,
"response_end": <response_end>
}
...
`
An example from SearchQA:
`{'candidate_id': 'SearchQA_000077f3912049dfb4511db271697bad/_0_1',
'response_end': 306,
'response_start': 243} `
### Data Fields
`
{
"candidate_id": <STRING>,
"response_start": <INT>,
"response_end": <INT>
}
...
`
- **candidate_id:** The candidate id of the candidate sentence. It consists of the original qid from the MRQA shared task.
- **response_start:** The start index of the sentence with respect to its original context.
- **response_end:** The end index of the sentence with respect to its original context
### Data Splits
Train and Dev splits are available only for the following datasets,
- SearchQA
- TriviaQA
- HotpotQA
- SQuAD
- NaturalQuestions
Test splits are available only for the following datasets,
- BioASQ
- RelationExtraction
- TextbookQA
The number of candidate sentences for each dataset in the table below.
| | MultiReQA | |
|--------------------|-----------|---------|
| | train | test |
| SearchQA | 629,160 | 454,836 |
| TriviaQA | 335,659 | 238,339 |
| HotpotQA | 104,973 | 52,191 |
| SQuAD | 87,133 | 10,642 |
| NaturalQuestions | 106,521 | 22,118 |
| BioASQ | - | 14,158 |
| RelationExtraction | - | 3,301 |
| TextbookQA | - | 3,701 |
## Dataset Creation
### Curation Rationale
MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the [MRQA shared task](https://mrqa.github.io/). The dataset was curated by converting existing QA datasets from [MRQA shared task](https://mrqa.github.io/) to the format of MultiReQA benchmark.
### Source Data
#### Initial Data Collection and Normalization
The Initial data collection was performed by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{m2020multireqa,
title={MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models},
author={Mandy Guo and Yinfei Yang and Daniel Cer and Qinlan Shen and Noah Constant},
year={2020},
eprint={2005.02507},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@Karthik-Bhaskar](https://github.com/Karthik-Bhaskar) for adding this dataset. |
false |
# Dataset Card for sidewalk-semantic
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Categories](#data-categories)
- [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:** [Dataset homepage on Segments.ai](https://segments.ai/segments/sidewalk-imagery/)
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Bert De Brabandere](mailto:bert@segments.ai)
### Dataset Summary
A dataset of sidewalk images gathered in Belgium in the summer of 2021. Label your own semantic segmentation datasets on [segments.ai](https://segments.ai/?utm_source=hf&utm_medium=hf-ds&utm_campaign=sidewalk)
### Supported Tasks and Leaderboards
- `semantic-segmentation`: The dataset can be used to train a semantic segmentation model, where each pixel is classified. The model performance is measured by how high its [mean IoU (intersection over union)](https://huggingface.co/metrics/mean_iou) to the reference is.
## Dataset Structure
### Data categories
| Id | Name | Description |
| --- | ---- | ----------- |
| 0 | unlabeled | - |
| 1 | flat-road | - |
| 2 | flat-sidewalk | - |
| 3 | flat-crosswalk | - |
| 4 | flat-cyclinglane | - |
| 5 | flat-parkingdriveway | - |
| 6 | flat-railtrack | - |
| 7 | flat-curb | - |
| 8 | human-person | - |
| 9 | human-rider | - |
| 10 | vehicle-car | - |
| 11 | vehicle-truck | - |
| 12 | vehicle-bus | - |
| 13 | vehicle-tramtrain | - |
| 14 | vehicle-motorcycle | - |
| 15 | vehicle-bicycle | - |
| 16 | vehicle-caravan | - |
| 17 | vehicle-cartrailer | - |
| 18 | construction-building | - |
| 19 | construction-door | - |
| 20 | construction-wall | - |
| 21 | construction-fenceguardrail | - |
| 22 | construction-bridge | - |
| 23 | construction-tunnel | - |
| 24 | construction-stairs | - |
| 25 | object-pole | - |
| 26 | object-trafficsign | - |
| 27 | object-trafficlight | - |
| 28 | nature-vegetation | - |
| 29 | nature-terrain | - |
| 30 | sky | - |
| 31 | void-ground | - |
| 32 | void-dynamic | - |
| 33 | void-static | - |
| 34 | void-unclear | - |
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
This dataset only contains one split.
## 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
[Needs More Information] |
false |
# Information Card for Brat
## Table of Contents
- [Description](#description)
- [Summary](#summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Usage](#usage)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Description
- **Homepage:** https://brat.nlplab.org
- **Paper:** https://aclanthology.org/E12-2021/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Summary
Brat is an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annota- tion for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. brat is designed in particular for structured annotation, where the notes are not free form text but have a fixed form that can be automatically processed and interpreted by a computer.
## Dataset Structure
Dataset annotated with brat format is processed using this script. Annotations created in brat are stored on disk in a standoff format: annotations are stored separately from the annotated document text, which is never modified by the tool. For each text document in the system, there is a corresponding annotation file. The two are associatied by the file naming convention that their base name (file name without suffix) is the same: for example, the file DOC-1000.ann contains annotations for the file DOC-1000.txt. More information can be found [here](https://brat.nlplab.org/standoff.html).
### Data Instances
[Needs More Information]
### Data Fields
```
-context: html content of data file as string
-file_name: a string name of file
-spans: a sequence containing id, type, location and text of a span
-relations: a sequence containing id, type and arguments of a relation
-equivalence_relations:
-events:
-attributions:
-normalizations:
-notes:
```
### Usage
brat script can be used by calling `load_dataset()` method and passing `kwargs` (arguments to the [BuilderConfig](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/builder_classes#datasets.BuilderConfig)) which should include at least `url` of the dataset prepared using brat. We provide an example of [SciArg](https://aclanthology.org/W18-5206.pdf) dataset below,
```python
from datasets import load_dataset
kwargs = {
"description" :
"""This dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing
fine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific
publications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of
scientific writing.""",
"citation" :
"""@inproceedings{lauscher2018b,
title = {An argument-annotated corpus of scientific publications},
booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},
publisher = {Association for Computational Linguistics},
author = {Lauscher, Anne and Glava\v{s}, Goran and Ponzetto, Simone Paolo},
address = {Brussels, Belgium},
year = {2018},
pages = {40–46}
}""",
"homepage": "https://github.com/anlausch/ArguminSci",
"url": "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip",
"file_name_blacklist": ['A28'],
}
dataset = load_dataset('dfki-nlp/brat', **kwargs)
```
## Additional Information
### Licensing Information
[Needs More Information]
### Citation Information
```
@inproceedings{stenetorp-etal-2012-brat,
title = "brat: a Web-based Tool for {NLP}-Assisted Text Annotation",
author = "Stenetorp, Pontus and
Pyysalo, Sampo and
Topi{\'c}, Goran and
Ohta, Tomoko and
Ananiadou, Sophia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the Demonstrations at the 13th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
month = apr,
year = "2012",
address = "Avignon, France",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E12-2021",
pages = "102--107",
}
``` |
true |
# Dataset Card for the ECtHR cases dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://archive.org/details/ECtHR-NAACL2021/
- **Repository:** http://archive.org/details/ECtHR-NAACL2021/
- **Paper:** https://arxiv.org/abs/2103.13084
- **Leaderboard:** TBA
- **Point of Contact:** [Ilias Chalkidis](mailto:ihalk@aueb.gr)
### Dataset Summary
The European Court of Human Rights (ECtHR) hears allegations regarding breaches in human rights provisions of the European Convention of Human Rights (ECHR) by European states. The Convention is available at https://www.echr.coe.int/Documents/Convention_ENG.pdf.
The court rules on a subset of all ECHR articles, which are predefined (alleged) by the applicants (*plaintiffs*).
Our dataset comprises 11k ECtHR cases and can be viewed as an enriched version of the ECtHR dataset of Chalkidis et al. (2019), which did not provide ground truth for alleged article violations (articles discussed) and rationales. The new dataset includes the following:
**Facts:** Each judgment includes a list of paragraphs that represent the facts of the case, i.e., they describe the main events that are relevant to the case, in numbered paragraphs. We hereafter call these paragraphs *facts* for simplicity. Note that the facts are presented in chronological order. Not all facts have the same impact or hold crucial information with respect to alleged article violations and the court's assessment; i.e., facts may refer to information that is trivial or otherwise irrelevant to the legally crucial allegations against *defendant* states.
**Allegedly violated articles:** Judges rule on specific accusations (allegations) made by the applicants (Harris, 2018). In ECtHR cases, the judges discuss and rule on the violation, or not, of specific articles of the Convention. The articles to be discussed (and ruled on) are put forward (as alleged article violations) by the applicants and are included in the dataset as ground truth; we identify 40 violable articles in total. The rest of the articles are procedural, i.e., the number of judges, criteria for office, election of judges, etc. In our experiments, however, the models are not aware of the allegations. They predict the Convention articles that will be discussed (the allegations) based on the case's facts, and they also produce rationales for their predictions. Models of this kind could be used by potential applicants to help them formulate future allegations (articles they could claim to have been violated), as already noted, but here we mainly use the task as a test-bed for rationale extraction.
**Violated articles:** The court decides which allegedly violated articles have indeed been violated. These decisions are also included in our dataset and could be used for full legal judgment prediction experiments (Chalkidis et al., 2019). However, they are not used in the experiments of this work.
**Silver allegation rationales:** Each decision of the ECtHR includes references to facts of the case (e.g., *"See paragraphs 2 and 4."*) and case law (e.g., *"See Draci vs. Russia (2010)"*.). We identified references to each case's facts and retrieved the corresponding paragraphs using regular expressions. These are included in the dataset as silver allegation rationales, on the grounds that the judges refer to these paragraphs when ruling on the allegations.
**Gold allegation rationales:** A legal expert with experience in ECtHR cases annotated a subset of 50 test cases to identify the relevant facts (paragraphs) of the case that support the allegations (alleged article violations). In other words, each identified fact justifies (hints) one or more alleged violations.
### Supported Tasks and Leaderboards
The dataset supports:
**Alleged violation prediction** (`alleged-violation-prediction`): A multi-label text classification task where, given the facts of a ECtHR case, a model predicts which of the 40 violable ECHR articles were allegedly violated according to the applicant(s). Consult Chalkidis et al. (2021), for details.
**Violation prediction** (`violation-prediction`): A multi-label text classification task where, given the facts of a ECtHR case, a model predicts which of the allegedly violated ECHR articles were violated, as decided (ruled) by the ECtHR court. Consult Chalkidis et al. (2019), for details.
**Rationale extraction:** A model can also predict the facts of the case that most prominently support its decision with respect to a classification task. Silver rationales can be used for both classification tasks, while gold rationales are only focused on the *alleged violation prediction* task.
### Languages
All documents are written in English.
## Dataset Structure
### Data Instances
This example was too long and was cropped:
```json
{
"facts": [
"8. In 1991 Mr Dusan Slobodnik, a research worker in the field of literature, ...",
"9. On 20 July 1992 the newspaper Telegraf published a poem by the applicant.",
"10. The poem was later published in another newspaper.",
"...",
"39. The City Court further dismissed the claim in respect of non-pecuniary damage ... ",
"40. The City Court ordered the plaintiff to pay SKK 56,780 to the applicant ...",
"41. On 25 November 1998 the Supreme Court upheld the decision of the Bratislava City Court ..."
],
"labels": ["14", "10", "9", "36"],
"silver_rationales": [27],
"gold_rationales": []
}
```
### Data Fields
`facts`: (**List[str]**) The paragraphs (facts) of the case.\
`labels`: (**List[str]**) The ECHR articles under discussion (*Allegedly violated articles*); or the allegedly violated ECHR articles that found to be violated by the court (judges).\
`silver_rationales`: (**List[int]**) Indices of the paragraphs (facts) that are present in the court's assessment.\
`gold_rationales`: (**List[int]**) Indices of the paragraphs (facts) that support alleged violations, according to a legal expert.
### Data Splits
| Split | No of ECtHR cases | Silver rationales ratio | Avg. allegations / case |
| ------------------- | ------------------------------------ | --- | --- |
| Train | 9,000 | 24% | 1.8 |
|Development | 1,000 | 30% | 1.7 |
|Test | 1,000 | 31% | 1.7 |
## Dataset Creation
### Curation Rationale
The dataset was curated by Chalkidis et al. (2021).\
The annotations for the gold rationales are available thanks to Dimitris Tsarapatsanis (Lecturer, York Law School).
### Source Data
#### Initial Data Collection and Normalization
The original data are available at HUDOC database (https://hudoc.echr.coe.int/eng) in an unprocessed format. The data were downloaded and all information was extracted from the HTML files and several JSON metadata files.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
* The original documents are available in HTML format at HUDOC database (https://hudoc.echr.coe.int/eng), except the gold rationales. The metadata are provided by additional JSON files, produced by REST services.
* The annotations for the gold rationales are available thanks to Dimitris Tsarapatsanis (Lecturer, York Law School).
#### Who are the annotators?
Dimitris Tsarapatsanis (Lecturer, York Law School).
### Personal and Sensitive Information
Privacy statement / Protection of personal data from HUDOC (https://www.echr.coe.int/Pages/home.aspx?p=privacy)
```
The Court complies with the Council of Europe's policy on protection of personal data, in so far as this is consistent with exercising its functions under the European Convention on Human Rights.
The Council of Europe is committed to respect for private life. Its policy on protection of personal data is founded on the Secretary General’s Regulation of 17 April 1989 outlining a data protection system for personal data files in the Council of Europe.
Most pages of the Council of Europe site require no personal information except in certain cases to allow requests for on-line services to be met. In such cases, the information is processed in accordance with the Confidentiality policy described below.
```
## Considerations for Using the Data
### Social Impact of Dataset
The publication of this dataset complies with the ECtHR data policy (https://www.echr.coe.int/Pages/home.aspx?p=privacy).
By no means do we aim to build a 'robot' lawyer or judge, and we acknowledge the possible harmful impact (Angwin et al., 2016, Dressel et al., 2018) of irresponsible deployment.
Instead, we aim to support fair and explainable AI-assisted judicial decision making and empirical legal studies.
For example, automated services can help applicants (plaintiffs) identify alleged violations that are supported by the facts of a case. They can help judges identify more quickly facts that support the alleged violations, contributing towards more informed judicial decision making (Zhong et al., 2020). They can also help legal experts identify previous cases related to particular allegations, helping analyze case law (Katz et al., 2012).
Also, consider ongoing critical research on responsible AI (Elish et al., 2021) that aims to provide explainable and fair systems to support human experts.
### Discussion of Biases
Consider the work of Chalkidis et al. (2019) for the identification of demographic bias by models.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
Ilias Chalkidis and Dimitris Tsarapatsanis
### Licensing Information
**CC BY-NC-SA (Creative Commons / Attribution-NonCommercial-ShareAlike)**
Read more: https://creativecommons.org/licenses/by-nc-sa/4.0/.
### Citation Information
*Ilias Chalkidis, Manos Fergadiotis, Dimitrios Tsarapatsanis, Nikolaos Aletras, Ion Androutsopoulos and Prodromos Malakasiotis. Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases.*
*Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021). Mexico City, Mexico. 2021.*
```
@InProceedings{chalkidis-et-al-2021-ecthr,
title = "Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases",
author = "Chalkidis, Ilias and Fergadiotis, Manos and Tsarapatsanis, Dimitrios and Aletras, Nikolaos and Androutsopoulos, Ion and Malakasiotis, Prodromos",
booktitle = "Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics",
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics"
}
```
*Ilias Chalkidis, Ion Androutsopoulos and Nikolaos Aletras. Neural Legal Judgment Prediction in English.*
*Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Florence, Italy. 2019.*
```
@InProceedings{chalkidis-etal-2019-neural,
title = "Neural Legal Judgment Prediction in {E}nglish",
author = "Chalkidis, Ilias and Androutsopoulos, Ion and Aletras, Nikolaos",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1424",
doi = "10.18653/v1/P19-1424",
pages = "4317--4323"
}
```
### Contributions
Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset. |
false |
# PROST: Physical Reasoning about Objects Through Space and Time
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/nala-cub/prost
- **Paper:** https://arxiv.org/abs/2106.03634
- **Leaderboard:**
- **Point of Contact:** [Stéphane Aroca-Ouellette](mailto:stephane.aroca-ouellette@colorado.edu)
### Dataset Summary
*Physical Reasoning about Objects Through Space and Time* (PROST) is a probing dataset to evaluate the ability of pretrained LMs to understand and reason about the physical world. PROST consists of 18,736 cloze-style multiple choice questions from 14 manually curated templates, covering 10 physical reasoning concepts: direction, mass, height, circumference, stackable, rollable, graspable, breakable, slideable, and bounceable.
### Supported Tasks and Leaderboards
The task is multiple choice question answering, but you can formulate it multiple ways. You can use `context` and `question` to form cloze style questions, or `context` and `ex_question` as multiple choice question answering. See the [GitHub](https://github.com/nala-cub/prost) repo for examples using GPT-1, GPT-2, BERT, RoBERTa, ALBERT, T5, and UnifiedQA.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en-US`.
## Dataset Structure
### Data Instances
An example looks like this:
```json
{
"A": "glass",
"B": "pillow",
"C": "coin",
"D": "ball",
"context": "A person drops a glass, a pillow, a coin, and a ball from a balcony.",
"ex_question": "Which object is the most likely to break?",
"group": "breaking",
"label": 0,
"name": "breaking_1",
"question": "The [MASK] is the most likely to break."
}
```
### Data Fields
- `A`: Option A (0)
- `B`: Option B (1)
- `C`: Option C (2)
- `D`: Option D (3)
- `context`: Context for the question
- `question`: A cloze style continuation of the context.
- `ex_question`: A multiple-choice style question.
- `group`: The question group, e.g. *bouncing*
- `label`: A ClassLabel indication the correct option
- `name':` The template identifier.
### Data Splits
The dataset contains 18,736 examples for testing.
## Dataset Creation
### Curation Rationale
PROST is designed to avoid models succeeding in unintended ways. First, PROST provides no training data, so as to probe models in a zero-shot fashion. This prevents models from succeeding through spurious correlations between testing and training, and encourages success through a true understanding of and reasoning about the concepts at hand. Second, we manually write templates for all questions in an effort to prevent models from having seen the exact same sentences in their training data. Finally, it focuses on a small set of well defined, objective concepts that only require a small vocabulary. This allows researchers to focus more on the quality of training data rather than on size of it.
### 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
PROST is licensed under the Apache 2.0 license.
### Citation Information
```
@inproceedings{aroca-ouellette-etal-2021-prost,
title = "{PROST}: {P}hysical Reasoning about Objects through Space and Time",
author = "Aroca-Ouellette, St{\'e}phane and
Paik, Cory and
Roncone, Alessandro and
Kann, Katharina",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.404",
pages = "4597--4608",
}
```
### Contributions
Thanks to [@corypaik](https://github.com/corypaik) for adding this dataset.
|
false |
# Dataset Card for MC-TACO
## 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:** [MC-TACO](https://cogcomp.seas.upenn.edu/page/resource_view/125)
- **Repository:** [Github repository](https://github.com/CogComp/MCTACO)
- **Paper:** ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://arxiv.org/abs/1909.03065)
- **Leaderboard:** [AI2 Leaderboard](https://leaderboard.allenai.org/mctaco)
### Dataset Summary
MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer pairs that require temporal commonsense comprehension. A system receives a sentence providing context information, a question designed to require temporal commonsense knowledge, and multiple candidate answers. More than one candidate answer can be plausible.
### Supported Tasks and Leaderboards
The task is framed as binary classification: givent he context, the question, and the candidate answer, the task is to determine whether the candidate answer is plausible ("yes") or not ("no").
Performance is measured using two metrics:
- Exact Match -- the average number of questions for which all the candidate answers are predicted correctly.
- F1 -- is slightly more relaxed than EM. It measures the overlap between one’s predictions and the ground truth, by computing the geometric mean of Precision and Recall.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
An example looks like this:
```
{
"sentence": "However, more recently, it has been suggested that it may date from earlier than Abdalonymus' death.",
"question": "How often did Abdalonymus die?",
"answer": "every two years",
"label": "no",
"category": "Frequency",
}
```
### Data Fields
All fields are strings:
- `sentence`: a sentence (or context) on which the question is based
- `question`: a question querying some temporal commonsense knowledge
- `answer`: a potential answer to the question (all lowercased)
- `label`: whether the answer is a correct. "yes" indicates the answer is correct/plaussible, "no" otherwise
- `category`: the temporal category the question belongs to (among "Event Ordering", "Event Duration", "Frequency", "Stationarity", and "Typical Time")
### Data Splits
The development set contains 561 questions and 3,783 candidate answers. The test set contains 1,332 questions and 9,442 candidate answers.
From the original repository:
*Note that there is no training data, and we provide the dev set as the only source of supervision. The rationale is that we believe a successful system has to bring in a huge amount of world knowledge and derive commonsense understandings prior to the current task evaluation. We therefore believe that it is not reasonable to expect a system to be trained solely on this data, and we think of the development data as only providing a definition of the task.*
## Dataset Creation
### Curation Rationale
MC-TACO is used as a testbed to study the temporal commonsense understanding on NLP systems.
### Source Data
From the original paper:
*The context sentences are randomly selected from [MultiRC](https://www.aclweb.org/anthology/N18-1023/) (from each of its 9 domains). For each sentence, we use crowdsourcing on Amazon Mechanical Turk to collect questions and candidate answers (both correct and wrong ones).*
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
From the original paper:
*To ensure the quality of the results, we limit the annotations to native speakers and use qualification tryouts.*
#### Annotation process
The crowdsourced construction/annotation of the dataset follows 4 steps described in Section 3 of the [paper](https://arxiv.org/abs/1909.03065): question generation, question verification, candidate answer expansion and answer labeling.
#### Who are the annotators?
Paid crowdsourcers.
### 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
Unknwon
### Citation Information
```
@inproceedings{ZKNR19,
author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},
title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding },
booktitle = {EMNLP},
year = {2019},
}
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. |
false |
# Dataset Card for BiblePara
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/bible-uedin.php
- **Repository:** None
- **Paper:** https://link.springer.com/article/10.1007/s10579-014-9287-y
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/bible-uedin.php
E.g.
`dataset = load_dataset("bible_para", lang1="fi", lang2="hi")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
false |
# Dataset Card for "definite_pronoun_resolution"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.hlt.utdallas.edu/~vince/data/emnlp12/](https://www.hlt.utdallas.edu/~vince/data/emnlp12/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 0.23 MB
- **Size of the generated dataset:** 0.24 MB
- **Total amount of disk used:** 0.47 MB
### Dataset Summary
Composed by 30 students from one of the author's undergraduate classes. These
sentence pairs cover topics ranging from real events (e.g., Iran's plan to
attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g.,
Batman) and purely imaginary situations, largely reflecting the pop culture as
perceived by the American kids born in the early 90s. Each annotated example
spans four lines: the first line contains the sentence, the second line contains
the target pronoun, the third line contains the two candidate antecedents, and
the fourth line contains the correct antecedent. If the target pronoun appears
more than once in the sentence, its first occurrence is the one to be resolved.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 0.23 MB
- **Size of the generated dataset:** 0.24 MB
- **Total amount of disk used:** 0.47 MB
An example of 'train' looks as follows.
```
{
"candidates": ["coreference resolution", "chunking"],
"label": 0,
"pronoun": "it",
"sentence": "There is currently more work on coreference resolution than on chunking because it is a problem that is still far from being solved."
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `sentence`: a `string` feature.
- `pronoun`: a `string` feature.
- `candidates`: a `list` of `string` features.
- `label`: a classification label, with possible values including `0` (0), `1` (1).
### Data Splits
| name |train|test|
|----------|----:|---:|
|plain_text| 1322| 564|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{rahman2012resolving,
title={Resolving complex cases of definite pronouns: the winograd schema challenge},
author={Rahman, Altaf and Ng, Vincent},
booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
pages={777--789},
year={2012},
organization={Association for Computational Linguistics}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
true |
# Multilingual Sentiments Dataset
A collection of multilingual sentiments datasets grouped into 3 classes -- positive, neutral, negative.
Most multilingual sentiment datasets are either 2-class positive or negative, 5-class ratings of products reviews (e.g. Amazon multilingual dataset) or multiple classes of emotions. However, to an average person, sometimes positive, negative and neutral classes suffice and are more straightforward to perceive and annotate. Also, a positive/negative classification is too naive, most of the text in the world is actually neutral in sentiment. Furthermore, most multilingual sentiment datasets don't include Asian languages (e.g. Malay, Indonesian) and are dominated by Western languages (e.g. English, German).
Git repo: https://github.com/tyqiangz/multilingual-sentiment-datasets
## Dataset Description
- **Webpage:** https://github.com/tyqiangz/multilingual-sentiment-datasets
|
true |
# Dataset Card for New Yorker Caption Contest Benchmarks
## 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:** [capcon.dev](https://www.capcon.dev)
- **Repository:** [https://github.com/jmhessel/caption_contest_corpus](https://github.com/jmhessel/caption_contest_corpus)
- **Paper:** [Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest](https://arxiv.org/abs/2209.06293)
- **Leaderboard:** No official leaderboard (yet).
- **Point of Contact:** jackh@allenai.org
### Dataset Summary
Data from:
[Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest](https://arxiv.org/abs/2209.06293)
```
@article{hessel2022androids,
title={Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest},
author={Hessel, Jack and Marasovi{\'c}, Ana and Hwang, Jena D and Lee, Lillian and Da, Jeff and Zellers, Rowan and Mankoff, Robert and Choi, Yejin},
journal={arXiv preprint arXiv:2209.06293},
year={2022}
}
```
If you use this dataset, we would appreciate you citing our work, but also -- several other papers that we build this corpus upon. See [Citation Information](#citation-information).
We challenge AI models to "demonstrate understanding" of the
sophisticated multimodal humor of The New Yorker Caption Contest.
Concretely, we develop three carefully circumscribed tasks for which
it suffices (but is not necessary) to grasp potentially complex and
unexpected relationships between image and caption, and similarly
complex and unexpected allusions to the wide varieties of human
experience.
### Supported Tasks and Leaderboards
Three tasks are supported:
- "Matching:" a model must recognize a caption written about a cartoon (vs. options that were not);
- "Quality ranking:" a model must evaluate the quality of a caption by scoring it more highly than a lower quality option from the same contest;
- "Explanation:" a model must explain why a given joke is funny.
There are no official leaderboards (yet).
### Languages
English
## Dataset Structure
Here's an example instance from Matching:
```
{'caption_choices': ['Tell me about your childhood very quickly.',
"Believe me . . . it's what's UNDER the ground that's "
'most interesting.',
"Stop me if you've heard this one.",
'I have trouble saying no.',
'Yes, I see the train but I think we can beat it.'],
'contest_number': 49,
'entities': ['https://en.wikipedia.org/wiki/Rule_of_three_(writing)',
'https://en.wikipedia.org/wiki/Bar_joke',
'https://en.wikipedia.org/wiki/Religious_institute'],
'from_description': 'scene: a bar description: Two priests and a rabbi are '
'walking into a bar, as the bartender and another patron '
'look on. The bartender talks on the phone while looking '
'skeptically at the incoming crew. uncanny: The scene '
'depicts a very stereotypical "bar joke" that would be '
'unlikely to be encountered in real life; the skepticism '
'of the bartender suggests that he is aware he is seeing '
'this trope, and is explaining it to someone on the '
'phone. entities: Rule_of_three_(writing), Bar_joke, '
'Religious_institute. choices A: Tell me about your '
"childhood very quickly. B: Believe me . . . it's what's "
"UNDER the ground that's most interesting. C: Stop me if "
"you've heard this one. D: I have trouble saying no. E: "
'Yes, I see the train but I think we can beat it.',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=323x231 at 0x7F34F283E9D0>,
'image_description': 'Two priests and a rabbi are walking into a bar, as the '
'bartender and another patron look on. The bartender '
'talks on the phone while looking skeptically at the '
'incoming crew.',
'image_location': 'a bar',
'image_uncanny_description': 'The scene depicts a very stereotypical "bar '
'joke" that would be unlikely to be encountered '
'in real life; the skepticism of the bartender '
'suggests that he is aware he is seeing this '
'trope, and is explaining it to someone on the '
'phone.',
'instance_id': '21125bb8787b4e7e82aa3b0a1cba1571',
'label': 'C',
'n_tokens_label': 1,
'questions': ['What is the bartender saying on the phone in response to the '
'living, breathing, stereotypical bar joke that is unfolding?']}
```
The label "C" indicates that the 3rd choice in the `caption_choices` is correct.
Here's an example instance from Ranking (in the from pixels setting --- though, this is also available in the from description setting)
```
{'caption_choices': ['I guess I misunderstood when you said long bike ride.',
'Does your divorce lawyer have any other cool ideas?'],
'contest_number': 582,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=600x414 at 0x7F8FF9F96610>,
'instance_id': 'dd1c214a1ca3404aa4e582c9ce50795a',
'label': 'A',
'n_tokens_label': 1,
'winner_source': 'official_winner'}
```
the label indicates that the first caption choice ("A", here) in the `caption_choices` list was more highly rated.
Here's an example instance from Explanation:
```
{'caption_choices': 'The classics can be so intimidating.',
'contest_number': 752,
'entities': ['https://en.wikipedia.org/wiki/Literature',
'https://en.wikipedia.org/wiki/Solicitor'],
'from_description': 'scene: a road description: Two people are walking down a '
'path. A number of giant books have surrounded them. '
'uncanny: There are book people in this world. entities: '
'Literature, Solicitor. caption: The classics can be so '
'intimidating.',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=800x706 at 0x7F90003D0BB0>,
'image_description': 'Two people are walking down a path. A number of giant '
'books have surrounded them.',
'image_location': 'a road',
'image_uncanny_description': 'There are book people in this world.',
'instance_id': 'eef9baf450e2fab19b96facc128adf80',
'label': 'A play on the word intimidating --- usually if the classics (i.e., '
'classic novels) were to be intimidating, this would mean that they '
'are intimidating to read due to their length, complexity, etc. But '
'here, they are surrounded by anthropomorphic books which look '
'physically intimidating, i.e., they are intimidating because they '
'may try to beat up these people.',
'n_tokens_label': 59,
'questions': ['What do the books want?']}
```
The label is an explanation of the joke, which serves as the autoregressive target.
### Data Instances
See above
### Data Fields
See above
### Data Splits
Data splits can be accessed as:
```
from datasets import load_dataset
dset = load_dataset("jmhessel/newyorker_caption_contest", "matching")
dset = load_dataset("jmhessel/newyorker_caption_contest", "ranking")
dset = load_dataset("jmhessel/newyorker_caption_contest", "explanation")
```
Or, in the from pixels setting, e.g.,
```
from datasets import load_dataset
dset = load_dataset("jmhessel/newyorker_caption_contest", "ranking_from_pixels")
```
Because the dataset is small, we reported in 5-fold cross-validation setting initially. The default splits are split 0. You can access the other splits, e.g.:
```
from datasets import load_dataset
# the 4th data split
dset = load_dataset("jmhessel/newyorker_caption_contest", "explanation_4")
```
## Dataset Creation
Full details are in the paper.
### Curation Rationale
See the paper for rationale/motivation.
### Source Data
See citation below. We combined 3 sources of data, and added significant annotations of our own.
#### Initial Data Collection and Normalization
Full details are in the paper.
#### Who are the source language producers?
We paid crowdworkers $15/hr to annotate the corpus.
In addition, significant annotation efforts were conducted by the authors of this work.
### Annotations
Full details are in the paper.
#### Annotation process
Full details are in the paper.
#### Who are the annotators?
A mix of crowdworks and authors of this paper.
### Personal and Sensitive Information
Has been redacted from the dataset. Images are published in the New Yorker already.
## Considerations for Using the Data
### Social Impact of Dataset
It's plausible that humor could perpetuate negative stereotypes. The jokes in this corpus are a mix of crowdsourced entries that are highly rated, and ones published in the new yorker.
### Discussion of Biases
Humor is subjective, and some of the jokes may be considered offensive. The images may contain adult themes and minor cartoon nudity.
### Other Known Limitations
More details are in the paper
## Additional Information
### Dataset Curators
The dataset was curated by researchers at AI2
### Licensing Information
The annotations we provide are CC-BY-4.0. See www.capcon.dev for more info.
### Citation Information
```
@article{hessel2022androids,
title={Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest},
author={Hessel, Jack and Marasovi{\'c}, Ana and Hwang, Jena D and Lee, Lillian and Da, Jeff and Zellers, Rowan and Mankoff, Robert and Choi, Yejin},
journal={arXiv preprint arXiv:2209.06293},
year={2022}
}
```
Our data contributions are:
- The cartoon-level annotations;
- The joke explanations;
- and the framing of the tasks
We release these data we contribute under CC-BY (see DATASET_LICENSE). If you find this data useful in your work, in addition to citing our contributions, please also cite the following, from which the cartoons/captions in our corpus are derived:
```
@misc{newyorkernextmldataset,
author={Jain, Lalit and Jamieson, Kevin and Mankoff, Robert and Nowak, Robert and Sievert, Scott},
title={The {N}ew {Y}orker Cartoon Caption Contest Dataset},
year={2020},
url={https://nextml.github.io/caption-contest-data/}
}
@inproceedings{radev-etal-2016-humor,
title = "Humor in Collective Discourse: Unsupervised Funniness Detection in The {New Yorker} Cartoon Caption Contest",
author = "Radev, Dragomir and
Stent, Amanda and
Tetreault, Joel and
Pappu, Aasish and
Iliakopoulou, Aikaterini and
Chanfreau, Agustin and
de Juan, Paloma and
Vallmitjana, Jordi and
Jaimes, Alejandro and
Jha, Rahul and
Mankoff, Robert",
booktitle = "LREC",
year = "2016",
}
@inproceedings{shahaf2015inside,
title={Inside jokes: Identifying humorous cartoon captions},
author={Shahaf, Dafna and Horvitz, Eric and Mankoff, Robert},
booktitle={KDD},
year={2015},
}
``` |
false |
# Dataset Card for M3IT-80
Project Page: [https://m3-it.github.io/](https://m3-it.github.io/)
## Dataset Description
- **Homepage: https://huggingface.co/datasets/MMInstruction/M3IT-80**
- **Repository: https://huggingface.co/datasets/MMInstruction/M3IT-80**
- **Paper: https://huggingface.co/papers/2306.04387**
- **Leaderboard:**
- **Point of Contact:**
### Languages
80 languages translated from English.
## Dataset Metainfo
[M3IT](https://huggingface.co/datasets/MMInstruction/M3IT) dataset
compiles diverse tasks of classical vision-language tasks, including captioning,
visual question answering~(VQA), visual conditioned generation, reasoning and classification.
**M3IT-80** is the 80-language translated version of M3IT.
### Languages
```python
_LAN_CODES = [
"af", "am", "ar", "as", "ast", "be", "bg", "bn", "bs", "ca",
"ceb", "cs", "cy", "da", "de", "el", "es", "et", "fi", "fr",
"fuv", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id",
"ig", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko",
"ky", "lb", "lg", "lij", "li", "ln", "lo", "lt", "lv", "mi",
"mk", "ml", "mr", "mt", "my", "nl", "ny", "oc", "pa", "pl",
"pt", "ro", "ru", "sd", "sk", "sn", "so", "sr", "sv", "ta",
"te", "tg", "th", "tl", "tr", "uk", "ur", "vi", "wo", "zh",
]
```
### Dataset Statistics
We report the number of the train/validation/test of each dataset per language.
| Task | Dataset | #Train | #Val | #Test |
|---------------------------|--------------|--------|------|-------|
| Classification | `imagenet` | 500 | 500 | 0 |
| Visual Question Answering | `vqa-v2` | 500 | 500 | 0 |
| Knowledgeable Visual QA | `okvqa` | 500 | 500 | 0 |
| Reasoning | `winoground` | 0 | 0 | 800 |
| Generation | `vist` | 500 | 500 | 500 |
| Video | `msrvtt` | 500 | 500 | 0 |
| | `msrvtt-qa` | 500 | 500 | 0 |
### Source Data
Source language: English
| Task | Dataset [Citation] | Source |
|---------------------------|--------------------|------------------------------------------------------------------------------------|
| Classification | `imagenet` [1] | [Source](https://www.image-net.org/) |
| Visual Question Answering | `vqa-v2` [2] | [Source](https://visualqa.org/) |
| Knowledgeable Visual QA | `okvqa` [3] | [Source](https://okvqa.allenai.org/) |
| Reasoning | `winoground` [4] | [Source](https://huggingface.co/datasets/facebook/winoground) |
| Generation | `vist` [5] | [Source](https://visionandlanguage.net/VIST/) |
| Video | `msrvtt` [6] | [Source](https://paperswithcode.com/dataset/msr-vtt) |
| | `msrvtt-qa` [7] | [Source](https://paperswithcode.com/sota/visual-question-answering-on-msrvtt-qa-1) |
### Translation
We use free [Alibaba Translate](https://www.alibabacloud.com/product/machine-translation),
a deep neural network translation (NMT) system, to perform the translation task.
## Dataset Structure
### HuggingFace Login (Optional)
```python
# OR run huggingface-cli login
from huggingface_hub import login
hf_token = "hf_xxx" # TODO: set a valid HuggingFace access token for loading datasets/models
login(token=hf_token)
```
### Data Loading
```python
from datasets import load_dataset
ds_name = "okvqa-zh" # change the dataset name here
dataset = load_dataset("MMInstruction/M3IT-80", ds_name)
```
### Data Splits
```python
from datasets import load_dataset
ds_name = "okvqa-zh" # change the dataset name here
dataset = load_dataset("MMInstruction/M3IT-80", ds_name)
train_set = dataset["train"]
validation_set = dataset["validation"]
test_set = dataset["test"]
```
### Data Instances
```python
from datasets import load_dataset
from io import BytesIO
from base64 import b64decode
from PIL import Image
ds_name = "okvqa-zh" # change the dataset name here
dataset = load_dataset("MMInstruction/M3IT-80", ds_name)
train_set = dataset["train"]
for train_instance in train_set:
instruction = train_instance["instruction"] # str
inputs = train_instance["inputs"] # str
outputs = train_instance["outputs"] # str
image_base64_str_list = train_instance["image_base64_str"] # str (base64)
image_0 = Image.open(BytesIO(b64decode(image_base64_str_list[0])))
```
### Data Fields
```python
import datasets
features = datasets.Features(
{
"instruction": datasets.Value("string"),
"inputs": datasets.Value("string"),
"image_base64_str": [datasets.Value("string")],
"outputs": datasets.Value("string"),
}
)
```
### Licensing Information
The content of original dataset follows their original license.
We suggest that for the task with Unknown/Custom license,
the user can check the original project or contact the dataset owner for detailed license information.
Our annotated instruction data is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```bibtex
@article{li2023m3it,
title={M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning},
author={Lei Li and Yuwei Yin and Shicheng Li and Liang Chen and Peiyi Wang and Shuhuai Ren and Mukai Li and Yazheng Yang and Jingjing Xu and Xu Sun and Lingpeng Kong and Qi Liu},
journal={arXiv preprint arXiv:2306.04387},
year={2023}
}
```
### Contributions
M3IT-80 is the translated version of M3IT,
an open-source, large-scale Multi-modal, Multilingual Instruction Tuning dataset,
designed to enable the development of general-purpose multi-modal agents.
## References
- [1] Imagenet large scale visual recognition challenge
- [2] Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
- [3] OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge
- [4] WinoGround: Probing vision and language models for visio-linguistic compositionality
- [5] Visual Storytelling
- [6] Video Question Answering via Gradually Refined Attention over Appearance and Motion
- [7] MSR-VTT: A large video description dataset for bridging video and language
|
false |
# Dataset Card for DocRED
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/thunlp/DocRED](https://github.com/thunlp/DocRED)
- **Paper:** [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://arxiv.org/abs/1906.06127)
- **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:** 21.00 MB
- **Size of the generated dataset:** 20.12 MB
- **Total amount of disk used:** 41.14 MB
### Dataset Summary
Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features:
- DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.
- DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.
- Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 21.00 MB
- **Size of the generated dataset:** 20.12 MB
- **Total amount of disk used:** 41.14 MB
An example of 'train_annotated' looks as follows.
```
{
"labels": {
"evidence": [[0]],
"head": [0],
"relation_id": ["P1"],
"relation_text": ["is_a"],
"tail": [0]
},
"sents": [["This", "is", "a", "sentence"], ["This", "is", "another", "sentence"]],
"title": "Title of the document",
"vertexSet": [[{
"name": "sentence",
"pos": [3],
"sent_id": 0,
"type": "NN"
}, {
"name": "sentence",
"pos": [3],
"sent_id": 1,
"type": "NN"
}], [{
"name": "This",
"pos": [0],
"sent_id": 0,
"type": "NN"
}]]
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `title`: a `string` feature.
- `sents`: a dictionary feature containing:
- `feature`: a `string` feature.
- `name`: a `string` feature.
- `sent_id`: a `int32` feature.
- `pos`: a `list` of `int32` features.
- `type`: a `string` feature.
- `labels`: a dictionary feature containing:
- `head`: a `int32` feature.
- `tail`: a `int32` feature.
- `relation_id`: a `string` feature.
- `relation_text`: a `string` feature.
- `evidence`: a `list` of `int32` features.
### Data Splits
| name |train_annotated|train_distant|validation|test|
|-------|--------------:|------------:|---------:|---:|
|default| 3053| 101873| 998|1000|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{yao-etal-2019-docred,
title = "{D}oc{RED}: A Large-Scale Document-Level Relation Extraction Dataset",
author = "Yao, Yuan and
Ye, Deming and
Li, Peng and
Han, Xu and
Lin, Yankai and
Liu, Zhenghao and
Liu, Zhiyuan and
Huang, Lixin and
Zhou, Jie and
Sun, Maosong",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1074",
doi = "10.18653/v1/P19-1074",
pages = "764--777",
}
```
### Contributions
Thanks to [@ghomasHudson](https://github.com/ghomasHudson), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
false |
# PubMed dataset for summarization
Dataset for summarization of long documents.\
Adapted from this [repo](https://github.com/armancohan/long-summarization).\
Note that original data are pre-tokenized so this dataset returns " ".join(text) and add "\n" for paragraphs. \
This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable:
```python
"ccdv/pubmed-summarization": ("article", "abstract")
```
### Data Fields
- `id`: paper id
- `article`: a string containing the body of the paper
- `abstract`: a string containing the abstract of the paper
### Data Splits
This dataset has 3 splits: _train_, _validation_, and _test_. \
Token counts are white space based.
| Dataset Split | Number of Instances | Avg. tokens |
| ------------- | --------------------|:----------------------|
| Train | 119,924 | 3043 / 215 |
| Validation | 6,633 | 3111 / 216 |
| Test | 6,658 | 3092 / 219 |
# Cite original article
```
@inproceedings{cohan-etal-2018-discourse,
title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
author = "Cohan, Arman and
Dernoncourt, Franck and
Kim, Doo Soon and
Bui, Trung and
Kim, Seokhwan and
Chang, Walter and
Goharian, Nazli",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2097",
doi = "10.18653/v1/N18-2097",
pages = "615--621",
abstract = "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.",
}
```
|
false |
# Dataset Card for SpeechCommands
## 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.tensorflow.org/datasets/catalog/speech_commands
- **Repository:** [More Information Needed]
- **Paper:** [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://arxiv.org/pdf/1804.03209.pdf)
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** Pete Warden, petewarden@google.com
### Dataset Summary
This is a set of one-second .wav audio files, each containing a single spoken
English word or background noise. These words are from a small set of commands, and are spoken by a
variety of different speakers. This data set is designed to help train simple
machine learning models. It is covered in more detail at [https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209).
Version 0.01 of the data set (configuration `"v0.01"`) was released on August 3rd 2017 and contains
64,727 audio files.
Version 0.02 of the data set (configuration `"v0.02"`) was released on April 11th 2018 and
contains 105,829 audio files.
### Supported Tasks and Leaderboards
* `keyword-spotting`: the dataset can be used to train and evaluate keyword
spotting systems. The task is to detect preregistered keywords by classifying utterances
into a predefined set of words. The task is usually performed on-device for the
fast response time. Thus, accuracy, model size, and inference time are all crucial.
### Languages
The language data in SpeechCommands is in English (BCP-47 `en`).
## Dataset Structure
### Data Instances
Example of a core word (`"label"` is a word, `"is_unknown"` is `False`):
```python
{
"file": "no/7846fd85_nohash_0.wav",
"audio": {
"path": "no/7846fd85_nohash_0.wav",
"array": array([ -0.00021362, -0.00027466, -0.00036621, ..., 0.00079346,
0.00091553, 0.00079346]),
"sampling_rate": 16000
},
"label": 1, # "no"
"is_unknown": False,
"speaker_id": "7846fd85",
"utterance_id": 0
}
```
Example of an auxiliary word (`"label"` is a word, `"is_unknown"` is `True`)
```python
{
"file": "tree/8b775397_nohash_0.wav",
"audio": {
"path": "tree/8b775397_nohash_0.wav",
"array": array([ -0.00854492, -0.01339722, -0.02026367, ..., 0.00274658,
0.00335693, 0.0005188]),
"sampling_rate": 16000
},
"label": 28, # "tree"
"is_unknown": True,
"speaker_id": "1b88bf70",
"utterance_id": 0
}
```
Example of background noise (`_silence_`) class:
```python
{
"file": "_silence_/doing_the_dishes.wav",
"audio": {
"path": "_silence_/doing_the_dishes.wav",
"array": array([ 0. , 0. , 0. , ..., -0.00592041,
-0.00405884, -0.00253296]),
"sampling_rate": 16000
},
"label": 30, # "_silence_"
"is_unknown": False,
"speaker_id": "None",
"utterance_id": 0 # doesn't make sense here
}
```
### Data Fields
* `file`: relative audio filename inside the original archive.
* `audio`: dictionary containing a relative audio filename,
a decoded audio array, and the sampling rate. Note that when accessing
the audio column: `dataset[0]["audio"]` the audio is automatically decoded
and resampled to `dataset.features["audio"].sampling_rate`.
Decoding and resampling of a large number of audios might take a significant
amount of time. Thus, it is important to first query the sample index before
the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred
over `dataset["audio"][0]`.
* `label`: either word pronounced in an audio sample or background noise (`_silence_`) class.
Note that it's an integer value corresponding to the class name.
* `is_unknown`: if a word is auxiliary. Equals to `False` if a word is a core word or `_silence_`,
`True` if a word is an auxiliary word.
* `speaker_id`: unique id of a speaker. Equals to `None` if label is `_silence_`.
* `utterance_id`: incremental id of a word utterance within the same speaker.
### Data Splits
The dataset has two versions (= configurations): `"v0.01"` and `"v0.02"`. `"v0.02"`
contains more words (see section [Source Data](#source-data) for more details).
| | train | validation | test |
|----- |------:|-----------:|-----:|
| v0.01 | 51093 | 6799 | 3081 |
| v0.02 | 84848 | 9982 | 4890 |
Note that in train and validation sets examples of `_silence_` class are longer than 1 second.
You can use the following code to sample 1-second examples from the longer ones:
```python
def sample_noise(example):
# Use this function to extract random 1 sec slices of each _silence_ utterance,
# e.g. inside `torch.utils.data.Dataset.__getitem__()`
from random import randint
if example["label"] == "_silence_":
random_offset = randint(0, len(example["speech"]) - example["sample_rate"] - 1)
example["speech"] = example["speech"][random_offset : random_offset + example["sample_rate"]]
return example
```
## Dataset Creation
### Curation Rationale
The primary goal of the dataset is to provide a way to build and test small
models that can detect a single word from a set of target words and differentiate it
from background noise or unrelated speech with as few false positives as possible.
### Source Data
#### Initial Data Collection and Normalization
The audio files were collected using crowdsourcing, see
[aiyprojects.withgoogle.com/open_speech_recording](https://github.com/petewarden/extract_loudest_section)
for some of the open source audio collection code that was used. The goal was to gather examples of
people speaking single-word commands, rather than conversational sentences, so
they were prompted for individual words over the course of a five minute
session.
In version 0.01 thirty different words were recoded: "Yes", "No", "Up", "Down", "Left",
"Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine",
"Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow".
In version 0.02 more words were added: "Backward", "Forward", "Follow", "Learn", "Visual".
In both versions, ten of them are used as commands by convention: "Yes", "No", "Up", "Down", "Left",
"Right", "On", "Off", "Stop", "Go". Other words are considered to be auxiliary (in current implementation
it is marked by `True` value of `"is_unknown"` feature). Their function is to teach a model to distinguish core words
from unrecognized ones.
The `_silence_` label contains a set of longer audio clips that are either recordings or
a mathematical simulation of noise.
#### Who are the source language producers?
The audio files were collected using crowdsourcing.
### Annotations
#### Annotation process
Labels are the list of words prepared in advances.
Speakers were prompted for individual words over the course of a five minute
session.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Creative Commons BY 4.0 License ((CC-BY-4.0)[https://creativecommons.org/licenses/by/4.0/legalcode]).
### Citation Information
```
@article{speechcommandsv2,
author = { {Warden}, P.},
title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1804.03209},
primaryClass = "cs.CL",
keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction},
year = 2018,
month = apr,
url = {https://arxiv.org/abs/1804.03209},
}
```
### Contributions
Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset. |
false |
# Dataset Card for KdConv
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Github](https://github.com/thu-coai/KdConv)
- **Paper:** [{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation](https://www.aclweb.org/anthology/2020.acl-main.635.pdf)
### Dataset Summary
KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn
conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel),
and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related
topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer
learning and domain adaptation.
### Supported Tasks and Leaderboards
This dataset can be leveraged for dialogue modelling tasks involving multi-turn and Knowledge base setup.
### Languages
This dataset has only Chinese Language.
## Dataset Structure
### Data Instances
Each data instance is a multi-turn conversation between 2 people with annotated knowledge base data used while talking
, e.g.:
```
{
"messages": [
{
"message": "对《我喜欢上你时的内心活动》这首歌有了解吗？"
},
{
"attrs": [
{
"attrname": "Information",
"attrvalue": "《我喜欢上你时的内心活动》是由韩寒填词，陈光荣作曲，陈绮贞演唱的歌曲，作为电影《喜欢你》的主题曲于2017年4月10日首发。2018年，该曲先后提名第37届香港电影金像奖最佳原创电影歌曲奖、第7届阿比鹿音乐奖流行单曲奖。",
"name": "我喜欢上你时的内心活动"
}
],
"message": "有些了解，是电影《喜欢你》的主题曲。"
},
...
{
"attrs": [
{
"attrname": "代表作品",
"attrvalue": "旅行的意义",
"name": "陈绮贞"
},
{
"attrname": "代表作品",
"attrvalue": "时间的歌",
"name": "陈绮贞"
}
],
"message": "我还知道《旅行的意义》与《时间的歌》，都算是她的代表作。"
},
{
"message": "好，有时间我找出来听听。"
}
],
"name": "我喜欢上你时的内心活动"
}
```
The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity
, relationship, tail entity), e.g.:
```
"忽然之间": [
[
"忽然之间",
"Information",
"《忽然之间》是歌手 莫文蔚演唱的歌曲，由 周耀辉， 李卓雄填词， 林健华谱曲，收录在莫文蔚1999年发行专辑《 就是莫文蔚》里。"
],
[
"忽然之间",
"谱曲",
"林健华"
]
...
]
```
### Data Fields
Conversation data fields:
- `name`: the starting topic (entity) of the conversation
- `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}`
- `messages`: list of all the turns in the dialogue. For each turn:
- `message`: the utterance
- `attrs`: list of knowledge graph triplets referred by the utterance. For each triplet:
- `name`: the head entity
- `attrname`: the relation
- `attrvalue`: the tail entity
Knowledge Base data fields:
- `head_entity`: the head entity
- `kb_triplets`: list of corresponding triplets
- `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}`
### Data Splits
The conversation dataset is split into a `train`, `validation`, and `test` split with the following sizes:
| | train | validation | test |
|--------|------:|-----------:|-----:|
| travel | 1200 | 1200 | 1200 |
| film | 1200 | 150 | 150 |
| music | 1200 | 150 | 150 |
| all | 3600 | 450 | 450 |
The Knowledge base dataset is having only train split with following sizes:
| | train |
|--------|------:|
| travel | 1154 |
| film | 8090 |
| music | 4441 |
| all | 13685 |
## 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
Apache License 2.0
### Citation Information
```
@inproceedings{zhou-etal-2020-kdconv,
title = "{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation",
author = "Zhou, Hao and
Zheng, Chujie and
Huang, Kaili and
Huang, Minlie and
Zhu, Xiaoyan",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.635",
doi = "10.18653/v1/2020.acl-main.635",
pages = "7098--7108",
}
```
### Contributions
Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset. |
true |
# Dataset Card for PAWS-X MT
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx)
- **Repository:** [PAWS-X](https://github.com/google-research-datasets/paws/tree/master/pawsx)
- **Paper:** [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://arxiv.org/abs/1908.11828)
- **Point of Contact:** [Yinfei Yang](yinfeiy@google.com)
### Dataset Summary
This dataset contains 23,659 **human** translated PAWS evaluation pairs and
296,406 **machine** translated training pairs in six typologically distinct
languages: French, Spanish, German, Chinese, Japanese, and Korean. All
translated pairs are sourced from examples in
[PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki).
For further details, see the accompanying paper:
[PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase
Identification](https://arxiv.org/abs/1908.11828)
This is a machine-translated version of the original dataset into English from each langauge.
### Supported Tasks and Leaderboards
It has been majorly used for paraphrase identification for English and other 6 languages namely French, Spanish, German, Chinese, Japanese, and Korean
### Languages
The dataset is in English, French, Spanish, German, Chinese, Japanese, and Korean
## Dataset Structure
### Data Instances
For en:
```
id : 1
sentence1 : In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland .
sentence2 : In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England .
label : 0
```
For fr:
```
id : 1
sentence1 : À Paris, en octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, lui demandant un passeport pour retourner en Angleterre en passant par l'Écosse.
sentence2 : En octobre 1560, il rencontra secrètement l'ambassadeur d'Angleterre, Nicolas Throckmorton, à Paris, et lui demanda un passeport pour retourner en Écosse par l'Angleterre.
label : 0
```
### Data Fields
All files are in tsv format with four columns:
Column Name | Data
:---------- | :--------------------------------------------------------
id | An ID that matches the ID of the source pair in PAWS-Wiki
sentence1 | The first sentence
sentence2 | The second sentence
label | Label for each pair
The source text of each translation can be retrieved by looking up the ID in the
corresponding file in PAWS-Wiki.
### Data Splits
The numbers of examples for each of the seven languages are shown below:
Language | Train | Dev | Test
:------- | ------: | -----: | -----:
en | 49,401 | 2,000 | 2,000
fr | 49,401 | 2,000 | 2,000
es | 49,401 | 2,000 | 2,000
de | 49,401 | 2,000 | 2,000
zh | 49,401 | 2,000 | 2,000
ja | 49,401 | 2,000 | 2,000
ko | 49,401 | 2,000 | 2,000
> **Caveat**: please note that the dev and test sets of PAWS-X are both sourced
> from the dev set of PAWS-Wiki. As a consequence, the same `sentence 1` may
> appear in both the dev and test sets. Nevertheless our data split guarantees
> that there is no overlap on sentence pairs (`sentence 1` + `sentence 2`)
> between dev and test.
## Dataset Creation
### Curation Rationale
Most existing work on adversarial data generation focuses on English. For example, PAWS (Paraphrase Adversaries from Word Scrambling) (Zhang et al., 2019) consists of challenging English paraphrase identification pairs from Wikipedia and Quora. They remedy this gap with PAWS-X, a new dataset of 23,659 human translated PAWS evaluation pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. They provide baseline numbers for three models with different capacity to capture non-local context and sentence structure, and using different multilingual training and evaluation regimes. Multilingual BERT (Devlin et al., 2019) fine-tuned on PAWS English plus machine-translated data performs the best, with a range of 83.1-90.8 accuracy across the non-English languages and an average accuracy gain of 23% over the next best model. PAWS-X shows the effectiveness of deep, multilingual pre-training while also leaving considerable headroom as a new challenge to drive multilingual research that better captures structure and contextual information.
### Source Data
PAWS (Paraphrase Adversaries from Word Scrambling)
#### Initial Data Collection and Normalization
All translated pairs are sourced from examples in [PAWS-Wiki](https://github.com/google-research-datasets/paws#paws-wiki)
#### Who are the source language producers?
This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean.
### Annotations
#### Annotation process
If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.
#### Who are the annotators?
The paper mentions the translate team, especially Mengmeng Niu, for the help with the annotations.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
### Licensing Information
The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
### Citation Information
```
@InProceedings{pawsx2019emnlp,
title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
booktitle = {Proc. of EMNLP},
year = {2019}
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@gowtham1997](https://github.com/gowtham1997) for adding this dataset. |
false |
# Dataset Card for GEM/wiki_cat_sum
## Dataset Description
- **Homepage:** https://github.com/lauhaide/WikiCatSum
- **Repository:** https://datashare.ed.ac.uk/handle/10283/3368
- **Paper:** https://arxiv.org/abs/1906.04687
- **Leaderboard:** N/A
- **Point of Contact:** Laura Perez-Beltrachini
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/wiki_cat_sum).
### Dataset Summary
WikiCatSum is an English summarization dataset in three domains: animals, companies, and film. It provides multiple paragraphs of text paired with a summary of the paragraphs.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/wiki_cat_sum')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/wiki_cat_sum).
#### website
[Github](https://github.com/lauhaide/WikiCatSum)
#### paper
[Arxiv](https://arxiv.org/abs/1906.04687)
#### authors
Laura Perez-Beltrachini, Yang Liu, Mirella Lapata (University of Edinburgh) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer (GoogleBrain)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/lauhaide/WikiCatSum)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Website](https://datashare.ed.ac.uk/handle/10283/3368)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[Arxiv](https://arxiv.org/abs/1906.04687)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{perez-beltrachini-etal-2019-generating,
title = "Generating Summaries with Topic Templates and Structured Convolutional Decoders",
author = "Perez-Beltrachini, Laura and
Liu, Yang and
Lapata, Mirella",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1504",
doi = "10.18653/v1/P19-1504",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Laura Perez-Beltrachini
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
lperez@ed.ac.uk
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-3.0: Creative Commons Attribution Share Alike 3.0 Unported
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Research on multi-document abstractive summarisation.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Summarization
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`, `academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Google Cloud Platform, University of Edinburgh
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Laura Perez-Beltrachini, Yang Liu, Mirella Lapata (University of Edinburgh) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer (GoogleBrain)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Google Cloud Platform, European Research Council
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Ronald Cardenas (University of Edinburgh) Laura Perez-Beltrachini (University of Edinburgh)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `id`: ID of the data example
- `title`: Is the Wikipedia article's title
- `paragraphs`: Is the ranked list of paragraphs from the set of crawled texts
- `summary`: Is constituted by a list of sentences together with their corresponding topic label
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
This is a truncated example from the animal setting:
```
{'gem_id': 'animal-train-1',
'gem_parent_id': 'animal-train-1',
'id': '2652',
'paragraphs': ["lytrosis (hulst) of louisiana vernon antoine brou jr. 2005. southern lepidopterists' news, 27: 7 ., ..."],
'references': ['lytrosis unitaria , the common lytrosis moth, is a species of moth of the geometridae family. it is found in north america, including arkansas, georgia, iowa , massachusetts, and wisconsin. the wingspan is about 50 mm. the larvae feed on rosa, crataegus, amelanchier, acer, quercus and viburnum species.'],
'summary': {'text': ['lytrosis unitaria , the common lytrosis moth , is a species of moth of the geometridae family .',
'it is found in north america , including arkansas , georgia , iowa , massachusetts , new hampshire , new jersey , new york , north carolina , ohio , oklahoma , ontario , pennsylvania , south carolina , tennessee , texas , virginia , west virginia and wisconsin .',
'the wingspan is about 50 mm .',
'the larvae feed on rosa , crataegus , amelanchier , acer , quercus and viburnum species . '],
'topic': [29, 20, 9, 8]},
'target': 'lytrosis unitaria , the common lytrosis moth, is a species of moth of the geometridae family. it is found in north america, including arkansas, georgia, iowa , massachusetts, and wisconsin. the wingspan is about 50 mm. the larvae feed on rosa, crataegus, amelanchier, acer, quercus and viburnum species.',
'title': 'lytrosis unitaria'}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
Nb of instances in train/valid/test are 50,938/2,855/2,831
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The data was split i.i.d., i.e. uniformly split into training, validation, and test datasets.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
Evaluation of models' performance on noisy (document, summary) pairs and long inputs.
Evaluate models' capabilities to generalise and mitigate biases.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Capabilities to generalise, mitigate biases, factual correctness.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`annotations added`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
We provide topic labels for summary sentences.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
- [Generating Wikipedia by Summarizing Long Sequences](https://arxiv.org/abs/1801.10198)
- [Generating Summaries with Topic Templates and Structured Convolutional Decoders](https://arxiv.org/abs/1906.04687)
- [Noisy Self-Knowledge Distillation for Text Summarization](https://arxiv.org/abs/2009.07032)
And all references in these papers.
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Capabilities to generalise, mitigate biases, factual correctness.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`ROUGE`, `BERT-Score`, `MoverScore`, `Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
- Abstract/Copy
- Factual accuracy based on the score of (Goodrich et al., 2019) and the relation extraction system of (Sorokin and Gurevych, 2017).
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
Human based are Question Answering and Ranking (Content, Fluency and Repetition)
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
Those listed above.
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
Generating Summaries with Topic Templates and Structured Convolutional Decoders
https://arxiv.org/abs/1906.04687
Noisy Self-Knowledge Distillation for Text Summarization
https://arxiv.org/abs/2009.07032
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The dataset is a subset of the WikiSum (Liu et al., 2018) dataset focusing on summaries of entities in three domains (Film, Company, and Animal). It is multi-document summarisation where input-output pairs for each example entity are created as follows. The input is a set of paragraphs collected from i) documents in the Reference section of the entity's Wikipedia page plus ii) documents collected from the top ten search results after querying Google search engine with the entity name. The output summary is the Wikipedia abstract for the entity.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Generate descriptive summaries with specific domains, where certain topics are discussed and generally in specific orders.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
WikiSum (Liu et al., 2018)
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Other`
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The dataset and task focuses on summaries for entities in three domains: Company, Film, and Animal.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Summary sentences are associated with a topic label. There is a topic model for each domain.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
automatically created
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
Each summary sentences was annotated with a topic label. There is a topic model for each of the three domains. This was used to guide a hierarchical decoder.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated by data curators
#### Quality Control Details
<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
Manual inspection of a sample of topics assigned to sentences. The number of topics was selected based on the performance of the summarisation model.
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
The dataset is base on Wikipedia and referenced and retrieved documents crawled from the Web.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
yes
#### Links and Summaries of Analysis Work
<!-- info: Provide links to and summaries of works analyzing these biases. -->
<!-- scope: microscope -->
This dataset is based on Wikipedia and thus biases analysis on other Wikipedia-based datasets are potentially true for WikiCatSum. For instance, see analysis for the ToTTo dataset here [1].
[1] Automatic Construction of Evaluation Suites for Natural Language Generation Datasets
https://openreview.net/forum?id=CSi1eu_2q96
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`public domain`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`
### Known Technical Limitations
|
false |
# Dataset Card for "cfq"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/google-research/google-research/tree/master/cfq](https://github.com/google-research/google-research/tree/master/cfq)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** https://arxiv.org/abs/1912.09713
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 2.14 GB
- **Size of the generated dataset:** 362.07 MB
- **Total amount of disk used:** 2.50 GB
### Dataset Summary
The Compositional Freebase Questions (CFQ) is a dataset that is specifically designed to measure compositional
generalization. CFQ is a simple yet realistic, large dataset of natural language questions and answers that also
provides for each question a corresponding SPARQL query against the Freebase knowledge base. This means that CFQ can
also be used for semantic parsing.
### 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 (`en`).
## Dataset Structure
### Data Instances
#### mcd1
- **Size of downloaded dataset files:** 267.60 MB
- **Size of the generated dataset:** 42.90 MB
- **Total amount of disk used:** 310.49 MB
An example of 'train' looks as follows.
```
{
'query': 'SELECT count(*) WHERE {\n?x0 a ns:people.person .\n?x0 ns:influence.influence_node.influenced M1 .\n?x0 ns:influence.influence_node.influenced M2 .\n?x0 ns:people.person.spouse_s/ns:people.marriage.spouse|ns:fictional_universe.fictional_character.married_to/ns:fictional_universe.marriage_of_fictional_characters.spouses ?x1 .\n?x1 a ns:film.cinematographer .\nFILTER ( ?x0 != ?x1 )\n}',
'question': 'Did a person marry a cinematographer , influence M1 , and influence M2'
}
```
#### mcd2
- **Size of downloaded dataset files:** 267.60 MB
- **Size of the generated dataset:** 44.77 MB
- **Total amount of disk used:** 312.38 MB
An example of 'train' looks as follows.
```
{
'query': 'SELECT count(*) WHERE {\n?x0 ns:people.person.parents|ns:fictional_universe.fictional_character.parents|ns:organization.organization.parent/ns:organization.organization_relationship.parent ?x1 .\n?x1 a ns:people.person .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M4 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M4\n}',
'question': "Did M1 and M5 employ M2 , M3 , and M4 and employ a person 's child"
}
```
#### mcd3
- **Size of downloaded dataset files:** 267.60 MB
- **Size of the generated dataset:** 43.60 MB
- **Total amount of disk used:** 311.20 MB
An example of 'train' looks as follows.
```
{
"query": "SELECT /producer M0 . /director M0 . ",
"question": "Who produced and directed M0?"
}
```
#### query_complexity_split
- **Size of downloaded dataset files:** 267.60 MB
- **Size of the generated dataset:** 45.95 MB
- **Total amount of disk used:** 313.55 MB
An example of 'train' looks as follows.
```
{
"query": "SELECT /producer M0 . /director M0 . ",
"question": "Who produced and directed M0?"
}
```
#### query_pattern_split
- **Size of downloaded dataset files:** 267.60 MB
- **Size of the generated dataset:** 46.12 MB
- **Total amount of disk used:** 313.72 MB
An example of 'train' looks as follows.
```
{
"query": "SELECT /producer M0 . /director M0 . ",
"question": "Who produced and directed M0?"
}
```
### Data Fields
The data fields are the same among all splits and configurations:
- `question`: a `string` feature.
- `query`: a `string` feature.
### Data Splits
| name | train | test |
|---------------------------|-------:|------:|
| mcd1 | 95743 | 11968 |
| mcd2 | 95743 | 11968 |
| mcd3 | 95743 | 11968 |
| query_complexity_split | 100654 | 9512 |
| query_pattern_split | 94600 | 12589 |
| question_complexity_split | 98999 | 10340 |
| question_pattern_split | 95654 | 11909 |
| random_split | 95744 | 11967 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{Keysers2020,
title={Measuring Compositional Generalization: A Comprehensive Method on
Realistic Data},
author={Daniel Keysers and Nathanael Sch"{a}rli and Nathan Scales and
Hylke Buisman and Daniel Furrer and Sergii Kashubin and
Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and
Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and
Olivier Bousquet},
booktitle={ICLR},
year={2020},
url={https://arxiv.org/abs/1912.09713.pdf},
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@brainshawn](https://github.com/brainshawn) for adding this dataset. |
false |
# Dataset Card for TweetQA
## 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:** [TweetQA homepage](https://tweetqa.github.io/)
- **Repository:**
- **Paper:** [TWEETQA: A Social Media Focused Question Answering Dataset](https://arxiv.org/abs/1907.06292)
- **Leaderboard:** [TweetQA Leaderboard](https://tweetqa.github.io/)
- **Point of Contact:** [Wenhan Xiong](xwhan@cs.ucsb.edu)
### Dataset Summary
With social media becoming increasingly popular on which lots of news and real-time events are reported, developing automated question answering systems is critical to the effectiveness of many applications that rely on real-time knowledge. While previous question answering (QA) datasets have concentrated on formal text like news and Wikipedia, the first large-scale dataset for QA over social media data is presented. To make sure the tweets are meaningful and contain interesting information, tweets used by journalists to write news articles are gathered. Then human annotators are asked to write questions and answers upon these tweets. Unlike other QA datasets like SQuAD in which the answers are extractive, the answer are allowed to be abstractive. The task requires model to read a short tweet and a question and outputs a text phrase (does not need to be in the tweet) as the answer.
### Supported Tasks and Leaderboards
- `question-answering`: The dataset can be used to train a model for Open-Domain Question Answering where the task is to answer the given questions for a tweet. The performance is measured by comparing the model answers to the the annoted groundtruth and calculating the BLEU-1/Meteor/ROUGE-L score. This task has an active leaderboard which can be found [here](https://tweetqa.github.io/) and ranks models based on [BLEU-1](https://huggingface.co/metrics/blue), [Meteor](https://huggingface.co/metrics/meteor) and [ROUGLE-L](https://huggingface.co/metrics/rouge).
### Languages
English.
## Dataset Structure
### Data Instances
Sample data:
```
{
"Question": "who is the tallest host?",
"Answer": ["sam bee","sam bee"],
"Tweet": "Don't believe @ConanOBrien's height lies. Sam Bee is the tallest host in late night. #alternativefacts\u2014 Full Frontal (@FullFrontalSamB) January 22, 2017",
"qid": "3554ee17d86b678be34c4dc2c04e334f"
}
```
The test split doesn't include answers so the Answer field is an empty list.
### Data Fields
- `Question`: a question based on information from a tweet
- `Answer`: list of possible answers from the tweet
- `Tweet`: source tweet
- `qid`: question id
### Data Splits
The dataset is split in train, validation and test set. The train set cointains 10692 examples, the validation set 1086 and the test set 1979 examples.
## Dataset Creation
### Curation Rationale
With social media becoming increasingly popular on which lots of news and real-time events are reported, developing automated question answering systems is critical to the effectiveness of many applications that rely on real-time knowledge. While previous question answering (QA) datasets have concentrated on formal text like news and Wikipedia, the first large-scale dataset for QA over social media data is presented. To make sure the tweets are meaningful and contain interesting information, tweets used by journalists to write news articles are gathered. Then human annotators are asked to write questions and answers upon these tweets. Unlike other QA datasets like SQuAD in which the answers are extractive, the answer are allowed to be abstractive. The task requires model to read a short tweet and a question and outputs a text phrase (does not need to be in the tweet) as the answer.
### Source Data
#### Initial Data Collection and Normalization
The authors look into the the archived snapshots of two major news websites (CNN, NBC), and then extract the tweet blocks that are embedded in the news articles. In order to get enough data, they first extract the URLs of all section pages (e.g. World, Politics, Money, Tech) from the snapshot of each home page and then crawl all articles with tweets from these section pages. Then, they filter out the tweets that heavily rely on attached media to convey information, for which they utilize a state-of-the-art semantic role labeling model trained on CoNLL-2005 (He et al., 2017) to analyze the predicate-argument structure of the tweets collected from news articles and keep
only the tweets with more than two labeled arguments. This filtering process also automatically
filters out most of the short tweets. For the tweets collected from CNN, 22.8% of them were filtered
via semantic role labeling. For tweets from NBC, 24.1% of the tweets were filtered.
#### Who are the source language producers?
Twitter users.
### Annotations
#### Annotation process
The Amazon Mechanical Turk workers were used to collect question-answer
pairs for the filtered tweets. For each Human Intelligence Task (HIT), the authors ask the worker to read three tweets and write two question-answer pairs for each tweet. To ensure the quality, they require the workers to be located in major English speaking countries (i.e. Canada, US, and UK) and have an acceptance rate larger than 95%. Since the authors use tweets as context, lots of important information are contained in hashtags or even emojis. Instead of only showing the text to the workers, they use javascript to directly embed the whole tweet into each HIT. This gives workers the same experience as reading tweets via web browsers and help them to better compose questions. To avoid trivial questions that can be simply answered by superficial text matching methods or too challenging questions that require background knowledge, the authors explicitly state the following items in the HIT instructions for question writing:
- No Yes-no questions should be asked.
- The question should have at least five words.
- Videos, images or inserted links should not
be considered.
- No background knowledge should be required to answer the question.
To help the workers better follow the instructions, they also include a representative example showing both good and bad questions or answers in the instructions. As for the answers, since the context they consider is relatively shorter than the context of previous datasets, they do not restrict the answers to be in the tweet, otherwise, the task may potentially be simplified as a classification problem. The workers are allowed to write their answers in their own words, but the authors require the answers to be brief and can be directly inferred from the tweets. After they retrieve the QA pairs from all HITs, they conduct further post-filtering to filter out the pairs from workers that obviously do not follow instructions. They remove QA pairs with yes/no answers. Questions with less than five words are also filtered out. This process filtered 13% of the QA pairs. The dataset now includes 10,898 articles, 17,794 tweets, and 13,757 crowdsourced question-answer pairs. All QA pairs were written by 492 individual workers.
#### Who are the annotators?
Amazon Mechanical Turk workers.
### 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:
> It is also worth noting that the data collected from social media can not only capture events and developments in real-time but also capture individual opinions and thus requires reasoning related to the authorship of the content as is illustrated in Table 1.
> Specifically, a significant amount of questions require certain reasoning skills that are specific to social media data:
- Understanding authorship: Since tweets are highly personal, it is critical to understand how questions/tweets related to the authors.
- Oral English & Tweet English: Tweets are often oral and informal. QA over tweets requires the understanding of common oral English. Our TWEETQA also requires understanding some tweet-specific English, like conversation-style English.
- Understanding of user IDs & hashtags: Tweets often contains user IDs and hashtags, which are single special tokens. Understanding these special tokens is important to answer person- or event-related questions.
### Other Known Limitations
[More Information Needed]
## Additional Information
The annotated answers are validated by the authors as follows:
For the purposes of human performance evaluation and inter-annotator agreement checking, the authors launch a different set of HITs to ask workers to answer questions in the test and development set. The workers are shown with the tweet blocks as well as the questions collected in the previous step. At this step, workers are allowed to label the questions as “NA” if they think the questions are not answerable. They find that 3.1% of the questions are labeled as unanswerable by the workers (for SQuAD, the ratio is 2.6%). Since the answers collected at this step and previous step are written by different workers, the answers can be written in different text forms even they are semantically equal to each other. For example, one answer can be “Hillary Clinton” while the other is “@HillaryClinton”. As it is not straightforward to automatically calculate the overall agreement, they manually check the agreement on a subset of 200 random samples from the development set and ask an independent human moderator to verify the result. It turns out that 90% of the answers pairs are semantically equivalent, 2% of them are partially equivalent (one of them is incomplete) and 8% are totally inconsistent. The answers collected at this step are also used to measure the human performance. 59 individual workers participated in this process.
### Dataset Curators
Xiong, Wenhan and Wu, Jiawei and Wang, Hong and Kulkarni, Vivek and Yu, Mo and Guo, Xiaoxiao and Chang, Shiyu and Wang, William Yang.
### Licensing Information
CC BY-SA 4.0.
### Citation Information
```
@inproceedings{xiong2019tweetqa,
title={TweetQA: A Social Media Focused Question Answering Dataset},
author={Xiong, Wenhan and Wu, Jiawei and Wang, Hong and Kulkarni, Vivek and Yu, Mo and Guo, Xiaoxiao and Chang, Shiyu and Wang, William Yang},
booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
year={2019}
}
```
### Contributions
Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset. |
false |
# Dataset Card for HEAD-QA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [HEAD-QA homepage](https://aghie.github.io/head-qa/)
- **Repository:** [HEAD-QA repository](https://github.com/aghie/head-qa)
- **Paper:** [HEAD-QA: A Healthcare Dataset for Complex Reasoning](https://www.aclweb.org/anthology/P19-1092/)
- **Leaderboard:** [HEAD-QA leaderboard](https://aghie.github.io/head-qa/#leaderboard-general)
- **Point of Contact:** [María Grandury](mailto:mariagrandury@gmail.com) (Dataset Submitter)
### Dataset Summary
HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the
Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the
[Ministerio de Sanidad, Consumo y Bienestar Social](https://www.mscbs.gob.es/), who also provides direct
[access](https://fse.mscbs.gob.es/fseweb/view/public/datosanteriores/cuadernosExamen/busquedaConvocatoria.xhtml)
to the exams of the last 5 years (in Spanish).
```
Date of the last update of the documents object of the reuse: January, 14th, 2019.
```
HEAD-QA tries to make these questions accesible for the Natural Language Processing community. We hope it is an useful resource towards achieving better QA systems. The dataset contains questions about the following topics:
- Medicine
- Nursing
- Psychology
- Chemistry
- Pharmacology
- Biology
### Supported Tasks and Leaderboards
- `multiple-choice-qa`: HEAD-QA is a multi-choice question answering testbed to encourage research on complex reasoning.
### Languages
The questions and answers are available in both Spanish (BCP-47 code: 'es-ES') and English (BCP-47 code: 'en').
The language by default is Spanish:
```
from datasets import load_dataset
data_es = load_dataset('head_qa')
data_en = load_dataset('head_qa', 'en')
```
## Dataset Structure
### Data Instances
A typical data point comprises a question `qtext`, multiple possible answers `atext` and the right answer `ra`.
An example from the HEAD-QA dataset looks as follows:
```
{
'qid': '1',
'category': 'biology',
'qtext': 'Los potenciales postsinápticos excitadores:',
'answers': [
{
'aid': 1,
'atext': 'Son de tipo todo o nada.'
},
{
'aid': 2,
'atext': 'Son hiperpolarizantes.'
},
{
'aid': 3,
'atext': 'Se pueden sumar.'
},
{
'aid': 4,
'atext': 'Se propagan a largas distancias.'
},
{
'aid': 5,
'atext': 'Presentan un periodo refractario.'
}],
'ra': '3',
'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=675x538 at 0x1B42B6A1668>,
'name': 'Cuaderno_2013_1_B',
'year': '2013'
}
```
### Data Fields
- `qid`: question identifier (int)
- `category`: category of the question: "medicine", "nursing", "psychology", "chemistry", "pharmacology", "biology"
- `qtext`: question text
- `answers`: list of possible answers. Each element of the list is a dictionary with 2 keys:
- `aid`: answer identifier (int)
- `atext`: answer text
- `ra`: `aid` of the right answer (int)
- `image`: (optional) 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]`
- `name`: name of the exam from which the question was extracted
- `year`: year in which the exam took place
### Data Splits
The data is split into train, validation and test set for each of the two languages. The split sizes are as follow:
| | Train | Val | Test |
| ----- | ------ | ----- | ---- |
| Spanish | 2657 | 1366 | 2742 |
| English | 2657 | 1366 | 2742 |
## Dataset Creation
### Curation Rationale
As motivation for the creation of this dataset, here is the abstract of the paper:
"We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions
come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly
specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information
retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well
behind human performance, demonstrating its usefulness as a benchmark for future work."
### Source Data
#### Initial Data Collection and Normalization
The questions come from exams to access a specialized position in the Spanish healthcare system, and are designed by the
[Ministerio de Sanidad, Consumo y Bienestar Social](https://www.mscbs.gob.es/), who also provides direct
[access](https://fse.mscbs.gob.es/fseweb/view/public/datosanteriores/cuadernosExamen/busquedaConvocatoria.xhtml)
to the exams of the last 5 years (in Spanish).
#### Who are the source language producers?
The dataset was created by David Vilares and Carlos Gómez-Rodríguez.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was created by David Vilares and Carlos Gómez-Rodríguez.
### Licensing Information
According to the [HEAD-QA homepage](https://aghie.github.io/head-qa/#legal-requirements):
The Ministerio de Sanidad, Consumo y Biniestar Social allows the redistribution of the exams and their content under [certain conditions:](https://www.mscbs.gob.es/avisoLegal/home.htm)
- The denaturalization of the content of the information is prohibited in any circumstance.
- The user is obliged to cite the source of the documents subject to reuse.
- The user is obliged to indicate the date of the last update of the documents object of the reuse.
According to the [HEAD-QA repository](https://github.com/aghie/head-qa/blob/master/LICENSE):
The dataset is licensed under the [MIT License](https://mit-license.org/).
### Citation Information
```
@inproceedings{vilares-gomez-rodriguez-2019-head,
title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
author = "Vilares, David and
G{\'o}mez-Rodr{\'i}guez, Carlos",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1092",
doi = "10.18653/v1/P19-1092",
pages = "960--966",
abstract = "We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.",
}
```
### Contributions
Thanks to [@mariagrandury](https://github.com/mariagrandury) for adding this dataset. |
true |
# Dataset Card for MIAM
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [N/A]
- **Repository:** [N/A]
- **Paper:** [N/A]
- **Leaderboard:** [N/A]
- **Point of Contact:** [N/A]
### Dataset Summary
Multilingual dIalogAct benchMark is a collection of resources for training, evaluating, and
analyzing natural language understanding systems specifically designed for spoken language. Datasets
are in English, French, German, Italian and Spanish. They cover a variety of domains including
spontaneous speech, scripted scenarios, and joint task completion. All datasets contain dialogue act
labels.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English, French, German, Italian, Spanish.
## Dataset Structure
### Data Instances
#### Dihana Corpus
For the `dihana` configuration one example from the dataset is:
```
{
'Speaker': 'U',
'Utterance': 'Hola , quería obtener el horario para ir a Valencia',
'Dialogue_Act': 9, # 'Pregunta' ('Request')
'Dialogue_ID': '0',
'File_ID': 'B209_BA5c3',
}
```
#### iLISTEN Corpus
For the `ilisten` configuration one example from the dataset is:
```
{
'Speaker': 'T_11_U11',
'Utterance': 'ok, grazie per le informazioni',
'Dialogue_Act': 6, # 'KIND-ATTITUDE_SMALL-TALK'
'Dialogue_ID': '0',
}
```
#### LORIA Corpus
For the `loria` configuration one example from the dataset is:
```
{
'Speaker': 'Samir',
'Utterance': 'Merci de votre visite, bonne chance, et à la prochaine !',
'Dialogue_Act': 21, # 'quit'
'Dialogue_ID': '5',
'File_ID': 'Dial_20111128_113927',
}
```
#### HCRC MapTask Corpus
For the `maptask` configuration one example from the dataset is:
```
{
'Speaker': 'f',
'Utterance': 'is it underneath the rope bridge or to the left',
'Dialogue_Act': 6, # 'query_w'
'Dialogue_ID': '0',
'File_ID': 'q4ec1',
}
```
#### VERBMOBIL
For the `vm2` configuration one example from the dataset is:
```
{
'Utterance': 'ja was sind viereinhalb Stunden Bahngerüttel gegen siebzig Minuten Turbulenzen im Flugzeug',
'Utterance': 'Utterance',
'Dialogue_Act': 'Dialogue_Act', # 'INFORM'
'Speaker': 'A',
'Dialogue_ID': '66',
}
```
### Data Fields
For the `dihana` configuration, the different fields are:
- `Speaker`: identifier of the speaker as a string.
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'Afirmacion' (0) [Feedback_positive], 'Apertura' (1) [Opening], 'Cierre' (2) [Closing], 'Confirmacion' (3) [Acknowledge], 'Espera' (4) [Hold], 'Indefinida' (5) [Undefined], 'Negacion' (6) [Feedback_negative], 'No_entendido' (7) [Request_clarify], 'Nueva_consulta' (8) [New_request], 'Pregunta' (9) [Request] or 'Respuesta' (10) [Reply].
- `Dialogue_ID`: identifier of the dialogue as a string.
- `File_ID`: identifier of the source file as a string.
For the `ilisten` configuration, the different fields are:
- `Speaker`: identifier of the speaker as a string.
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'AGREE' (0), 'ANSWER' (1), 'CLOSING' (2), 'ENCOURAGE-SORRY' (3), 'GENERIC-ANSWER' (4), 'INFO-REQUEST' (5), 'KIND-ATTITUDE_SMALL-TALK' (6), 'OFFER-GIVE-INFO' (7), 'OPENING' (8), 'PERSUASION-SUGGEST' (9), 'QUESTION' (10), 'REJECT' (11), 'SOLICITATION-REQ_CLARIFICATION' (12), 'STATEMENT' (13) or 'TALK-ABOUT-SELF' (14).
- `Dialogue_ID`: identifier of the dialogue as a string.
For the `loria` configuration, the different fields are:
- `Speaker`: identifier of the speaker as a string.
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'ack' (0), 'ask' (1), 'find_mold' (2), 'find_plans' (3), 'first_step' (4), 'greet' (5), 'help' (6), 'inform' (7), 'inform_engine' (8), 'inform_job' (9), 'inform_material_space' (10), 'informer_conditioner' (11), 'informer_decoration' (12), 'informer_elcomps' (13), 'informer_end_manufacturing' (14), 'kindAtt' (15), 'manufacturing_reqs' (16), 'next_step' (17), 'no' (18), 'other' (19), 'quality_control' (20), 'quit' (21), 'reqRep' (22), 'security_policies' (23), 'staff_enterprise' (24), 'staff_job' (25), 'studies_enterprise' (26), 'studies_job' (27), 'todo_failure' (28), 'todo_irreparable' (29), 'yes' (30)
- `Dialogue_ID`: identifier of the dialogue as a string.
- `File_ID`: identifier of the source file as a string.
For the `maptask` configuration, the different fields are:
- `Speaker`: identifier of the speaker as a string.
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'acknowledge' (0), 'align' (1), 'check' (2), 'clarify' (3), 'explain' (4), 'instruct' (5), 'query_w' (6), 'query_yn' (7), 'ready' (8), 'reply_n' (9), 'reply_w' (10) or 'reply_y' (11).
- `Dialogue_ID`: identifier of the dialogue as a string.
- `File_ID`: identifier of the source file as a string.
For the `vm2` configuration, the different fields are:
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialogue act label of the utterance. It can be one of 'ACCEPT' (0), 'BACKCHANNEL' (1), 'BYE' (2), 'CLARIFY' (3), 'CLOSE' (4), 'COMMIT' (5), 'CONFIRM' (6), 'DEFER' (7), 'DELIBERATE' (8), 'DEVIATE_SCENARIO' (9), 'EXCLUDE' (10), 'EXPLAINED_REJECT' (11), 'FEEDBACK' (12), 'FEEDBACK_NEGATIVE' (13), 'FEEDBACK_POSITIVE' (14), 'GIVE_REASON' (15), 'GREET' (16), 'INFORM' (17), 'INIT' (18), 'INTRODUCE' (19), 'NOT_CLASSIFIABLE' (20), 'OFFER' (21), 'POLITENESS_FORMULA' (22), 'REJECT' (23), 'REQUEST' (24), 'REQUEST_CLARIFY' (25), 'REQUEST_COMMENT' (26), 'REQUEST_COMMIT' (27), 'REQUEST_SUGGEST' (28), 'SUGGEST' (29), 'THANK' (30).
- `Speaker`: Speaker as a string.
- `Dialogue_ID`: identifier of the dialogue as a string.
### Data Splits
| Dataset name | Train | Valid | Test |
| ------------ | ----- | ----- | ---- |
| dihana | 19063 | 2123 | 2361 |
| ilisten | 1986 | 230 | 971 |
| loria | 8465 | 942 | 1047 |
| maptask | 25382 | 5221 | 5335 |
| vm2 | 25060 | 2860 | 2855 |
## 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
Anonymous.
### Licensing Information
This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
```
@inproceedings{colombo-etal-2021-code,
title = "Code-switched inspired losses for spoken dialog representations",
author = "Colombo, Pierre and
Chapuis, Emile and
Labeau, Matthieu and
Clavel, Chlo{\'e}",
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.656",
doi = "10.18653/v1/2021.emnlp-main.656",
pages = "8320--8337",
abstract = "Spoken dialogue systems need to be able to handle both multiple languages and multilinguality inside a conversation (\textit{e.g} in case of code-switching). In this work, we introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations. The goal of these losses is to expose the model to code-switched language. In order to scale up training, we automatically build a pretraining corpus composed of multilingual conversations in five different languages (French, Italian, English, German and Spanish) from OpenSubtitles, a huge multilingual corpus composed of 24.3G tokens. We test the generic representations on MIAM, a new benchmark composed of five dialogue act corpora on the same aforementioned languages as well as on two novel multilingual tasks (\textit{i.e} multilingual mask utterance retrieval and multilingual inconsistency identification). Our experiments show that our new losses achieve a better performance in both monolingual and multilingual settings.",
}
```
### Contributions
Thanks to [@eusip](https://github.com/eusip) and [@PierreColombo](https://github.com/PierreColombo) for adding this dataset. |
false | # T<sup>2</sup>Ranking
## Introduction
T<sup>2</sup>Ranking is a large-scale Chinese benchmark for passage ranking. The details about T<sup>2</sup>Ranking are elaborated in [this paper](https://arxiv.org/abs/2304.03679#).
Passage ranking are important and challenging topics for both academics and industries in the area of Information Retrieval (IR). The goal of passage ranking is to compile a search result list ordered in terms of relevance to the query from a large passage collection. Typically, Passage ranking involves two stages: passage retrieval and passage re-ranking.
To support the passage ranking research, various benchmark datasets are constructed. However, the commonly-used datasets for passage ranking usually focus on the English language. For non-English scenarios, such as Chinese, the existing datasets are limited in terms of data scale, fine-grained relevance annotation and false negative issues.
To address this problem, we introduce T<sup>2</sup>Ranking, a large-scale Chinese benchmark for passage ranking. T<sup>2</sup>Ranking comprises more than 300K queries and over 2M unique passages from real- world search engines. Specifically, we sample question-based search queries from user logs of the Sogou search engine, a popular search system in China. For each query, we extract the content of corresponding documents from different search engines. After model-based passage segmentation and clustering-based passage de-duplication, a large-scale passage corpus is obtained. For a given query and its corresponding passages, we hire expert annotators to provide 4-level relevance judgments of each query-passage pair.
<div align=center><img width="600" height="200" src="https://github.com/THUIR/T2Ranking/blob/main/pic/stat.png?raw=true"/></div>
<div align=center>Table 1: The data statistics of datasets commonly used in passage ranking. FR(SR): First (Second)- stage of passage ranking, i.e., passage Retrieval (Re-ranking).</div>
Compared with existing datasets, T<sup>2</sup>Ranking dataset has the following characteristics and advantages:
* The proposed dataset focus on the Chinese search scenario, and has advantages in data scale compared with existing Chinese passage ranking datasets, which can better support the design of deep learning algorithms
* The proposed dataset has a large number of fine-grained relevance annotations, which is helpful for mining fine-grained relationship between queries and passages and constructing more accurate ranking algorithms.
* By retrieving passage results from multiple commercial search engines and providing complete annotation, we ease the false negative problem to some extent, which is beneficial to providing more accurate evaluation.
* We design multiple strategies to ensure the high quality of our dataset, such as using a passage segment model and a passage clustering model to enhance the semantic integrity and diversity of passages and employing active learning for annotation method to improve the efficiency and quality of data annotation.
## Data Download
The whole dataset is placed in [huggingface](https://huggingface.co/datasets/THUIR/T2Ranking), and the data formats are presented in the following table.
<div class="center">
| Description| Filename|Num Records|Format|
|-------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|----------:|-----------------------------------:|
| Collection | collection.tsv | 2,303,643 | tsv: pid, passage |
| Queries Train | queries.train.tsv | 258,042 | tsv: qid, query |
| Queries Dev | queries.dev.tsv | 24,832 | tsv: qid, query |
| Queries Test | queries.test.tsv | 24,832 | tsv: qid, query |
| Qrels Train for re-ranking | qrels.train.tsv | 1,613,421 | TREC qrels format |
| Qrels Dev for re-ranking | qrels.dev.tsv | 400,536 | TREC qrels format |
| Qrels Retrieval Train | qrels.retrieval.train.tsv | 744,663 | tsv: qid, pid |
| Qrels Retrieval Dev | qrels.retrieval.dev.tsv | 118,933 | tsv: qid, pid |
| BM25 Negatives | train.bm25.tsv | 200,359,731 | tsv: qid, pid, index |
| Hard Negatives | train.mined.tsv | 200,376,001 | tsv: qid, pid, index, score |
</div>
You can download the dataset by running the following command:
```bash
git lfs install
git clone https://huggingface.co/datasets/THUIR/T2Ranking
```
After downloading, you can find the following files in the folder:
```
├── data
│ ├── collection.tsv
│ ├── qrels.dev.tsv
│ ├── qrels.retrieval.dev.tsv
│ ├── qrels.retrieval.train.tsv
│ ├── qrels.train.tsv
│ ├── queries.dev.tsv
│ ├── queries.test.tsv
│ ├── queries.train.tsv
│ ├── train.bm25.tsv
│ └── train.mined.tsv
├── script
│ ├── train_cross_encoder.sh
│ └── train_dual_encoder.sh
└── src
├── convert2trec.py
├── dataset_factory.py
├── modeling.py
├── msmarco_eval.py
├── train_cross_encoder.py
├── train_dual_encoder.py
└── utils.py
```
## Training and Evaluation
The dual-encoder can be trained by running the following command:
```bash
sh script/train_dual_encoder.sh
```
After training the model, you can evaluate the model by running the following command:
```bash
python src/msmarco_eval.py data/qrels.retrieval.dev.tsv output/res.top1000.step20
```
The cross-encoder can be trained by running the following command:
```bash
sh script/train_cross_encoder.sh
```
After training the model, you can evaluate the model by running the following command:
```bash
python src/convert2trec.py output/res.step-20 && python src/msmarco_eval.py data/qrels.retrieval.dev.tsv output/res.step-20.trec && path_to/trec_eval -m ndcg_cut.5 data/qrels.dev.tsv res.step-20.trec
```
BM25 on DEV set
```bash
#####################
MRR @10: 0.35894801237316354
QueriesRanked: 24831
recall@1: 0.05098711868967141
recall@1000: 0.7464097131133757
recall@50: 0.4942572226146033
#####################
```
DPR w/o hard negatives on DEV set
```bash
#####################
MRR @10: 0.4856112079562753
QueriesRanked: 24831
recall@1: 0.07367235058688999
recall@1000: 0.9082753169878586
recall@50: 0.7099350889583964
#####################
```
DPR w/ hard negatives on DEV set
```bash
#####################
MRR @10: 0.5166915171959451
QueriesRanked: 24831
recall@1: 0.08047455688965123
recall@1000: 0.9135220125786163
recall@50: 0.7327044025157232
#####################
```
BM25 retrieved+CE reranked on DEV set
```bash
#####################
MRR @10: 0.5188107959009376
QueriesRanked: 24831
recall@1: 0.08545219116806242
recall@1000: 0.7464097131133757
recall@50: 0.595298153566744
#####################
ndcg_cut_20 all 0.4405
ndcg_cut_100 all 0.4705
#####################
```
DPR retrieved+CE reranked on DEV set
```bash
#####################
MRR @10: 0.5508822816845231
QueriesRanked: 24831
recall@1: 0.08903406988867588
recall@1000: 0.9135220125786163
recall@50: 0.7393720781623112
#####################
ndcg_cut_20 all 0.5131
ndcg_cut_100 all 0.5564
#####################
```
## License
The dataset is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).
## Citation
If you use this dataset in your research, please cite our paper:
```
@misc{xie2023t2ranking,
title={T2Ranking: A large-scale Chinese Benchmark for Passage Ranking},
author={Xiaohui Xie and Qian Dong and Bingning Wang and Feiyang Lv and Ting Yao and Weinan Gan and Zhijing Wu and Xiangsheng Li and Haitao Li and Yiqun Liu and Jin Ma},
year={2023},
eprint={2304.03679},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
|
true |
# Dataset Card for Slither Audited Smart Contracts
## 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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/mwritescode/slither-audited-smart-contracts
- **Repository:** https://github.com/mwritescode/slither-audited-smart-contracts
- **Point of Contact:** [Martina Rossini](mailto:martina.rossini704@gmail.com)
### Dataset Summary
This dataset contains source code and deployed bytecode for Solidity Smart Contracts that have been verified on Etherscan.io, along with a classification of their vulnerabilities according to the Slither static analysis framework.
### Supported Tasks and Leaderboards
- `text-classification`: The dataset can be used to train a model for both binary and multilabel text classification on smart contracts bytecode and source code. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset.
- `text-generation`: The dataset can also be used to train a language model for the Solidity programming language
- `image-classification`: By pre-processing the bytecode data to obtain RGB images, the dataset can also be used to train convolutional neural networks for code vulnerability detection and classification.
### Languages
The language annotations are in English, while all the source codes are in Solidity.
## Dataset Structure
### Data Instances
Each data instance contains the following features: `address`, `source_code` and `bytecode`. The label comes in two configuration, either a plain-text cleaned up version of the output given by the Slither tool or a multi-label version, which consists in a simple list of integers, each one representing a particular vulnerability class. Label 4 indicates that the contract is safe.
An example from a plain-text configuration looks as follows:
```
{
'address': '0x006699d34AA3013605d468d2755A2Fe59A16B12B'
'source_code': 'pragma solidity 0.5.4; interface IERC20 { function balanceOf(address account) external ...'
'bytecode': '0x608060405234801561001057600080fd5b5060043610610202576000357c0100000000000000000000000000000000000000000000000000000000900...'
'slither': '{"success": true, "error": null, "results": {"detectors": [{"check": "divide-before-multiply", "impact": "Medium", "confidence": "Medium"}]}}'
}
```
An example from a multi-label configuration looks as follows:
```
{
'address': '0x006699d34AA3013605d468d2755A2Fe59A16B12B'
'source_code': 'pragma solidity 0.5.4; interface IERC20 { function balanceOf(address account) external ...'
'bytecode': '0x608060405234801561001057600080fd5b5060043610610202576000357c0100000000000000000000000000000000000000000000000000000000900...'
'slither': [ 4 ]
}
```
### Data Fields
- `address`: a string representing the address of the smart contract deployed on the Ethereum main net
- `source_code`: a flattened version of the smart contract codebase in Solidity
- `bytecode`: a string representing the smart contract's bytecode, obtained when calling `web3.eth.getCode()`. Note that in some cases where this was not available, the string is simply '0x'.
- `slither`: either a cleaned up version of Slither's JSON output or a list of class labels
### Data Splits
The dataset comes in 6 configurations and train, test and validation splits are only provided for those configurations that do not include `all-` in their names. Test and Validation splits are both about 15% of the total.
## Dataset Creation
### Curation Rationale
slither-audited-smart-contracts was built to provide a freely available large scale dataset for vulnerability detection and classification on verified Solidity smart contracts. Indeed, the biggest open source dataset for this task at the moment of writing is [SmartBugs Wild](https://github.com/smartbugs/smartbugs-wild), containing 47,398 smart contracts that were labeled with 9 tools withing the SmartBugs framework.
### Source Data
#### Initial Data Collection and Normalization
The dataset was constructed started from the list of verified smart contracts provided at [Smart Contract Sanctuary](https://github.com/tintinweb/smart-contract-sanctuary-ethereum). Then, smart contract source code was either downloaded from the aforementioned repo or downloaded via [Etherscan](https://etherscan.io/apis) and flattened using the Slither contract flattener. The bytecode was downloaded using the Web3.py library, in particular the `web3.eth.getCode()` function and using [INFURA](https://infura.io/) as our endpoint.
Finally, every smart contract was analyzed using the [Slither](https://github.com/crytic/slither) static analysis framework. The tool found 38 different vulnerability classes in the collected contracts and they were then mapped to 9 labels according to what is shown in the file `label_mappings.json`. These mappings were derived by following the guidelines at [Decentralized Application Security Project (DASP)](https://www.dasp.co/) and at [Smart Contract Weakness Classification Registry](https://swcregistry.io/). They were also inspired by the mappings used for Slither's detection by the team that labeled the SmartBugs Wild dataset, which can be found [here](https://github.com/smartbugs/smartbugs-results/blob/master/metadata/vulnerabilities_mapping.cs).
## Additional Information
### Dataset Curators
The dataset was initially created by Martina Rossini during work done for the project of the course Blockchain and Cryptocurrencies of the University of Bologna (Italy).
### Licensing Information
The license in the file LICENSE applies to all the files in this repository, except for the Solidity source code of the contracts. These are still publicly available, were obtained using the Etherscan APIs, and retain their original licenses.
### Citation Information
If you are using this dataset in your research and paper, here's how you can cite it:
```
@misc{rossini2022slitherauditedcontracts,
title = {Slither Audited Smart Contracts Dataset},
author={Martina Rossini},
year={2022}
}
```
### Contributions
Thanks to [@mwritescode](https://github.com/mwritescode) for adding this dataset. |
false |
# Dataset Card for Text-based NP Enrichment
## 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://yanaiela.github.io/TNE/
- **Repository:** https://github.com/yanaiela/TNE
- **Paper:** https://arxiv.org/abs/2109.12085
- **Leaderboard:** [TNE OOD](https://leaderboard.allenai.org/tne-ood/submissions/public)
[TNE](https://leaderboard.allenai.org/tne/submissions/public)
- **Point of Contact:** [Yanai Elazar](mailto:yanaiela@gmail.com)
### Dataset Summary
Text-based NP Enrichment (TNE) is a natural language understanding (NLU) task, which focus on relations between noun phrases (NPs) that can be mediated via prepositions. The dataset contains 5,497 documents, annotated exhaustively with all possible links between the NPs in each document.
The main data comes from WikiNews, which is used for train/dev/test. We also collected an additional set of 509 documents to serve as out of distribution (OOD) data points, from the Book Corpus, IMDB reviews and Reddit.
### Supported Tasks and Leaderboards
The data contain both the main data for the TNE task, as well as coreference resolution data.
There are two leaderboards for the TNE data, one for the standard test set, and another one for the OOD test set:
- [TNE Leaderboard](https://leaderboard.allenai.org/tne/submissions/public)
- [TNE OOD Leaderboard](https://leaderboard.allenai.org/tne-ood/submissions/public)
### Languages
The text in the dataset is in English, as spoken in the different domains we include. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
The original files are in a jsonl format, containing a dictionary of a single document, in each line.
Each document contain a different amount of labels, due to the different amount of NPs.
The test and ood splits come without the annotated labels.
### Data Fields
A document consists of:
* `id`: a unique identifier of a document, beginning with `r` and followed by a number
* `text`: the text of the document. The title and subtitles (if exists) are separated with two new lines. The paragraphs
are separated by a single new line.
* `tokens`: a list of string, containing the tokenized tokens
* `nps`: a list of dictionaries, containing the following entries:
* `text`: the text of the np
* `start_index`: an integer indicating the starting index in the text
* `end_index`: an integer indicating the ending index in the text
* `start_token`: an integer indicating the first token of the np out of the tokenized tokens
* `end_token`: an integer indicating the last token of the np out of the tokenized tokens
* `id`: the id of the np
* `np_relations`: these are the relation labels of the document. It is a list of dictionaries, where each
dictionary contains:
* `anchor`: the id of the anchor np
* `complement`: the id of the complement np
* `preposition`: the preposition that links between the anchor and the complement. This can take one out of 24 pre-defined preposition (23 + member(s)-of)
* `complement_coref_cluster_id`: the coreference id, which the complement is part of.
* `coref`: the coreference labels. It contains a list of dictionaries, where each dictionary contains:
* `id`: the id of the coreference cluster
* `members`: the ids of the nps members of such cluster
* `np_type`: the type of cluster. It can be either
* `standard`: regular coreference cluster
* `time/date/measurement`: a time / date / measurement np. These will be singletons.
* `idiomatic`: an idiomatic expression
* `metadata`: metadata of the document. It contains the following:
* `annotators`: a dictionary with anonymized annotators id
* `coref_worker`: the coreference worker id
* `consolidator_worker`: the consolidator worker id
* `np-relations_worker`: the np relations worker id
* `url`: the url where the document was taken from (not always existing)
* `source`: the original file name where the document was taken from
### Data Splits
The dataset is spread across four files, for the four different splits: train, dev, test and test_ood.
Additional details on the data statistics can be found in the [paper](https://arxiv.org/abs/2109.12085)
## Dataset Creation
### Curation Rationale
TNE was build as a new task for language understanding, focusing on extracting relations between nouns, moderated by prepositions.
### 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
The dataset was created by Yanai Elazar, Victoria Basmov, Yoav Goldberg, Reut Tsarfaty, during work done at Bar-Ilan University, and AI2.
### Licensing Information
The data is released under the MIT license.
### Citation Information
```bibtex
@article{tne,
author = {Elazar, Yanai and Basmov, Victoria and Goldberg, Yoav and Tsarfaty, Reut},
title = "{Text-based NP Enrichment}",
journal = {Transactions of the Association for Computational Linguistics},
year = {2022},
}
```
### Contributions
Thanks to [@yanaiela](https://github.com/yanaiela), who is also the first author of the paper, for adding this dataset. |
false |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://inklab.usc.edu/NumerSense/
- **Repository:** https://github.com/INK-USC/NumerSense
- **Paper:** https://arxiv.org/abs/2005.00683
- **Leaderboard:** https://inklab.usc.edu/NumerSense/#exp
- **Point of Contact:** Author emails listed in [paper](https://arxiv.org/abs/2005.00683)
### Dataset Summary
NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145
masked-word-prediction probes. The general idea is to mask numbers between 0-10 in sentences mined from a commonsense
corpus and evaluate whether a language model can correctly predict the masked value.
### Supported Tasks and Leaderboards
The dataset supports the task of slot-filling, specifically as an evaluation of numerical common sense. A leaderboard
is included on the [dataset webpage](https://inklab.usc.edu/NumerSense/#exp) with included benchmarks for GPT-2,
RoBERTa, BERT, and human performance. Leaderboards are included for both the core set and the adversarial set
discussed below.
### Languages
This dataset is in English.
## Dataset Structure
### Data Instances
Each instance consists of a sentence with a masked numerical value between 0-10 and (in the train set) a target.
Example from the training set:
```
sentence: Black bears are about <mask> metres tall.
target: two
```
### Data Fields
Each value of the training set consists of:
- `sentence`: The sentence with a number masked out with the `<mask>` token.
- `target`: The ground truth target value. Since the test sets do not include the ground truth, the `target` field
values are empty strings in the `test_core` and `test_all` splits.
### Data Splits
The dataset includes the following pre-defined data splits:
- A train set with >10K labeled examples (i.e. containing a ground truth value)
- A core test set (`test_core`) with 1,132 examples (no ground truth provided)
- An expanded test set (`test_all`) encompassing `test_core` with the addition of adversarial examples for a total of
3,146 examples. See section 2.2 of [the paper] for a discussion of how these examples are constructed.
## Dataset Creation
### Curation Rationale
The purpose of this dataset is "to study whether PTLMs capture numerical commonsense knowledge, i.e., commonsense
knowledge that provides an understanding of the numeric relation between entities." This work is motivated by the
prior research exploring whether language models possess _commonsense knowledge_.
### Source Data
#### Initial Data Collection and Normalization
The dataset is an extension of the [Open Mind Common Sense](https://huggingface.co/datasets/open_mind_common_sense)
corpus. A query was performed to discover sentences containing numbers between 0-12, after which the resulting
sentences were manually evaluated for inaccuracies, typos, and the expression of commonsense knowledge. The numerical
values were then masked.
#### Who are the source language producers?
The [Open Mind Common Sense](https://huggingface.co/datasets/open_mind_common_sense) corpus, from which this dataset
is sourced, is a crowdsourced dataset maintained by the MIT Media Lab.
### Annotations
#### Annotation process
No annotations are present in this dataset beyond the `target` values automatically sourced from the masked
sentences, as discussed above.
#### Who are the annotators?
The curation and inspection was done in two rounds by graduate students.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
The motivation of measuring a model's ability to associate numerical values with real-world concepts appears
relatively innocuous. However, as discussed in the following section, the source dataset may well have biases encoded
from crowdworkers, particularly in terms of factoid coverage. A model's ability to perform well on this benchmark
should therefore not be considered evidence that it is more unbiased or objective than a human performing similar
tasks.
[More Information Needed]
### Discussion of Biases
This dataset is sourced from a crowdsourced commonsense knowledge base. While the information contained in the graph
is generally considered to be of high quality, the coverage is considered to very low as a representation of all
possible commonsense knowledge. The representation of certain factoids may also be skewed by the demographics of the
crowdworkers. As one possible example, the term "homophobia" is connected with "Islam" in the ConceptNet knowledge
base, but not with any other religion or group, possibly due to the biases of crowdworkers contributing to the
project.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset was collected by Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren, Computer Science researchers
at the at the University of Southern California.
### Licensing Information
The data is hosted in a GitHub repositor with the
[MIT License](https://github.com/INK-USC/NumerSense/blob/main/LICENSE).
### Citation Information
```
@inproceedings{lin2020numersense,
title={Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models},
author={Bill Yuchen Lin and Seyeon Lee and Rahul Khanna and Xiang Ren},
booktitle={Proceedings of EMNLP},
year={2020},
note={to appear}
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. |
false | # Dataset Card for H4 Stack Exchange Preferences Dataset
## Dataset Description
- **Homepage:** https://archive.org/details/stackexchange
- **Repository:** (private for now) https://github.com/huggingface/h4
- **Point of Contact:** Nathan Lambert, nathan@huggingface.co
- **Size of downloaded dataset:** 22.13 GB
- **Number of instructions:** 10,741,532
### Dataset Summary
This dataset contains questions and answers from the [Stack Overflow Data Dump](https://archive.org/details/stackexchange) for the purpose of **preference model training**.
Importantly, the questions have been filtered to fit the following criteria for preference models (following closely from [Askell et al. 2021](https://arxiv.org/abs/2112.00861)): *have >=2 answers*.
This data could also be used for instruction fine-tuning and language model training.
The questions are grouped with answers that are assigned a score corresponding to the Anthropic paper:
```
score = log2 (1 + upvotes) rounded to the nearest integer, plus 1 if the answer was accepted by the questioner (we assign a score of −1 if the number of upvotes is negative).
```
Some important notes when using this dataset for preference model pretraining (PMP), which can be ignored for other uses:
* the data will likely need to be filtered more due to matching scores.
* see section 4.1 of Askel et al 2021 for instructions on using each pair of samples twice via the following `binarization` (for better pre-training initialization):
```
Subsequently, we created a binary dataset by applying a ‘binarization’ procedure to the ranked dataset. That
is, for every ranked pair A > B, we transform it into two independent binary comparisons:
GOOD:A > BAD:A
BAD:B > GOOD:B
```
To see all the stackexchanges used in this data, please see [this file](https://huggingface.co/datasets/HuggingFaceH4/pmp-stack-exchange/blob/main/stack_exchanges.json).
Unfortunately, sharing the binarized data directly without metadata violates the license, so we have shared a script for binarization.
### Using the data
Here is a script from our internal tooling used to create a binarized dataset:
```
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
from argparse import ArgumentParser
from pathlib import Path
import numpy as np
from datasets import Dataset, concatenate_datasets, load_dataset
from h4.data.utils import save_dataset_shards
H4_DIR = Path(__file__).resolve().parents[3]
DATA_DIR = H4_DIR / "data"
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--debug", action="store_true", help="Added print statements / limit data size for debugging")
parser.add_argument(
"--output_dir",
default=f"{DATA_DIR}/pmp-binarized",
type=str,
help="Where to save the processed dataset",
)
parser.add_argument(
"--exchange_name",
type=str,
default=None,
help="Optional argument to specify a specific subsection of the dataset",
)
parser.add_argument(
"--binary_score", type=int, default=8, help="Score assigned to binarized pairs for preference data."
)
parser.add_argument(
"--stream_data", action="store_true", help="Optionally stream data, which can be useful with weaker computers"
)
parser.set_defaults(debug=False, stream_data=False) # default will process full dataset
args = parser.parse_args()
specific_exchange = args.exchange_name
stream_dataset = args.stream_data
binary_score = args.binary_score
if specific_exchange:
data_dir = "data/" + args.exchange_name
else:
data_dir = None
if args.debug:
data_len_limit = 10000
else:
data_len_limit = np.inf
dataset = load_dataset(
"HuggingFaceH4/pmp-stack-exchange",
data_dir=data_dir,
split="train",
streaming=stream_dataset,
)
pmp_data = []
for i, d in enumerate(iter(dataset)):
# check debug limit, quit if in debug mode (don't save)
if i > data_len_limit:
print("Early exit for debug mode!")
print(pmp_data)
break
question = d["question"]
answers = d["answers"]
num_answers = len(answers)
answer_scores = [a["pm_score"] for a in answers]
if len(np.unique(answer_scores)) < 2:
print(f"PM Scores are {answer_scores}, skipping this question {i}")
else:
# Sample 2 unique scores for binarization
dif_scores = False
while not dif_scores:
# print("infinite loop...?")
two_answers = random.sample(answers, 2)
if two_answers[0]["pm_score"] != two_answers[1]["pm_score"]:
dif_scores = True
answer_0 = two_answers[0]
answer_1 = two_answers[1]
text_0 = "Question: " + question + "\n" + "Answer: " + answer_0["text"]
text_1 = "Question: " + question + "\n" + "Answer: " + answer_1["text"]
score_0 = binary_score
score_1 = binary_score
pmp_data.append({"context": text_0, "score": score_0})
pmp_data.append({"context": text_1, "score": score_1})
# Save binarized data
sublist_len = 100000
print(f"Dataset length is {len(pmp_data)}")
# bypass known issue in arrow https://issues.apache.org/jira/browse/ARROW-17137
print(f"Processed dataset length > {sublist_len}, processing to HF dataset in chunks")
chunks = [pmp_data[x : x + sublist_len] for x in range(0, len(pmp_data), sublist_len)]
ds_chunks = [Dataset.from_list(ch) for ch in chunks]
ds = concatenate_datasets(ds_chunks)
save_dataset_shards(ds, args.output_dir, subset="stackexchange", shard_size="100MB")
```
### Languages
This is intended to be English only, thought other languages may be present. Some Stack Exchanges that are omitted include:
```
spanish: es.meta.stackoverflow.com, es.stackoverflow.com
japanese: ja.meta.stackoverflow.com, ja.stackoverflow.com
portugese: pt.stackoverflow.com, pt.meta.stackoverflow.com
russian: ru.stackoverflow, ru.meta.stackoverflow
```
### Licensing Information
License: https://creativecommons.org/licenses/by-sa/4.0/
The cc-by-sa 4.0 licensing, while intentionally permissive, does require attribution:
Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work).
Specifically the attribution requirements are as follows:
1. Visually display or otherwise indicate the source of the content as coming from the Stack Exchange Network. This requirement is satisfied with a discreet text blurb, or some other unobtrusive but clear visual indication.
2. Ensure that any Internet use of the content includes a hyperlink directly to the original question on the source site on the Network (e.g., http://stackoverflow.com/questions/12345)
3. Visually display or otherwise clearly indicate the author names for every question and answer used
4. Ensure that any Internet use of the content includes a hyperlink for each author name directly back to his or her user profile page on the source site on the Network (e.g., http://stackoverflow.com/users/12345/username), directly to the Stack Exchange domain, in standard HTML (i.e. not through a Tinyurl or other such indirect hyperlink, form of obfuscation or redirection), without any “nofollow” command or any other such means of avoiding detection by search engines, and visible even with JavaScript disabled.
For more information, see the Stack Exchange Terms of Service.
### Citation Information
```
@online{h4stackexchange,
author = {Lambert, Nathan and Tunstall, Lewis and Rajani, Nazneen and Thrush, Tristan},
title = {HuggingFace H4 Stack Exchange Preference Dataset},
year = 2023,
url = {https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences},
}
``` |
false |
# Dataset Card for "aeslc"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/ryanzhumich/AESLC
- **Paper:** [This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation](https://arxiv.org/abs/1906.03497)
- **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:** 11.64 MB
- **Size of the generated dataset:** 14.95 MB
- **Total amount of disk used:** 26.59 MB
### Dataset Summary
A collection of email messages of employees in the Enron Corporation.
There are two features:
- email_body: email body text.
- subject_line: email subject text.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
Monolingual English (mainly en-US) with some exceptions.
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 11.64 MB
- **Size of the generated dataset:** 14.95 MB
- **Total amount of disk used:** 26.59 MB
An example of 'train' looks as follows.
```
{
"email_body": "B/C\n<<some doc>>\n",
"subject_line": "Service Agreement"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `email_body`: a `string` feature.
- `subject_line`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|14436| 1960|1906|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{zhang-tetreault-2019-email,
title = "This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation",
author = "Zhang, Rui and
Tetreault, Joel",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1043",
doi = "10.18653/v1/P19-1043",
pages = "446--456",
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset. |
false |
# 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:** [homepage](https://github.com/masakhane-io/masakhane-ner)
- **Repository:** [github](https://github.com/masakhane-io/masakhane-ner)
- **Paper:** [paper](https://arxiv.org/abs/2103.11811)
- **Point of Contact:** [Masakhane](https://www.masakhane.io/) or didelani@lsv.uni-saarland.de
### Dataset Summary
MasakhaNER 2.0 is the largest publicly available high-quality dataset for named entity recognition (NER) in 20 African languages created by the Masakhane community.
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
MasakhaNER 2.0 is a named entity dataset consisting of PER, ORG, LOC, and DATE entities annotated by Masakhane for 20 African languages
The train/validation/test sets are available for all the 20 languages.
For more details see https://arxiv.org/abs/2210.12391
### Supported Tasks and Leaderboards
[More Information Needed]
- `named-entity-recognition`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data.
### Languages
There are 20 languages available :
- Bambara (bam)
- Ghomala (bbj)
- Ewe (ewe)
- Fon (fon)
- Hausa (hau)
- Igbo (ibo)
- Kinyarwanda (kin)
- Luganda (lug)
- Dholuo (luo)
- Mossi (mos)
- Chichewa (nya)
- Nigerian Pidgin
- chShona (sna)
- Kiswahili (swą)
- Setswana (tsn)
- Twi (twi)
- Wolof (wol)
- isiXhosa (xho)
- Yorùbá (yor)
- isiZulu (zul)
## Dataset Structure
### Data Instances
The examples look like this for Yorùbá:
```
from datasets import load_dataset
data = load_dataset('masakhane/masakhaner2', 'yor')
# Please, specify the language code
# A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [B-DATE, I-DATE, 0, 0, 0, 0, 0, B-PER, I-PER, I-PER, O, O, O, O],
'tokens': ['Wákàtí', 'méje', 'ti', 'ré', 'kọjá', 'lọ', 'tí', 'Luis', 'Carlos', 'Díaz', 'ti', 'di', 'awati', '.']
}
```
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE",
```
In the NER tags, a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & time (DATE).
It is assumed that named entities are non-recursive and non-overlapping. In case a named entity is embedded in another named entity usually, only the top level entity is marked.
### Data Splits
For all languages, there are three splits.
The original splits were named `train`, `dev` and `test` and they correspond to the `train`, `validation` and `test` splits.
The splits have the following sizes :
| Language | train | validation | test |
|-----------------|------:|-----------:|------:|
| Bambara | 4463 | 638 | 1274 |
| Ghomala | 3384 | 483 | 966 |
| Ewe | 3505 | 501 | 1001 |
| Fon. | 4343 | 621 | 1240 |
| Hausa | 5716 | 816 | 1633 |
| Igbo | 7634 | 1090 | 2181 |
| Kinyarwanda | 7825 | 1118 | 2235 |
| Luganda | 4942 | 706 | 1412 |
| Luo | 5161 | 737 | 1474 |
| Mossi | 4532 | 648 | 1613 |
| Nigerian-Pidgin | 5646 | 806 | 1294 |
| Chichewa | 6250 | 893 | 1785 |
| chiShona | 6207 | 887 | 1773 |
| Kiswahili | 6593 | 942 | 1883 |
| Setswana | 3289 | 499 | 996 |
| Akan/Twi | 4240 | 605 | 1211 |
| Wolof | 4593 | 656 | 1312 |
| isiXhosa | 5718 | 817 | 1633 |
| Yoruba | 6877 | 983 | 1964 |
| isiZulu | 5848 | 836 | 1670 |
## Dataset Creation
### Curation Rationale
The dataset was introduced to introduce new resources to 20 languages that were under-served for natural language processing.
[More Information Needed]
### Source Data
The source of the data is from the news domain, details can be found here https://arxiv.org/abs/2210.12391
#### Initial Data Collection and Normalization
The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable.
#### Who are the source language producers?
The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above.
### Annotations
#### Annotation process
Details can be found here https://arxiv.org/abs/2103.11811
#### Who are the annotators?
Annotators were recruited from [Masakhane](https://www.masakhane.io/)
### Personal and Sensitive Information
The data is sourced from newspaper source and only contains mentions of public figures or individuals
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains.
## Additional Information
### Dataset Curators
### Licensing Information
The licensing status of the data is CC 4.0 Non-Commercial
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@article{Adelani2022MasakhaNER2A,
title={MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition},
author={David Ifeoluwa Adelani and Graham Neubig and Sebastian Ruder and Shruti Rijhwani and Michael Beukman and Chester Palen-Michel and Constantine Lignos and Jesujoba Oluwadara Alabi and Shamsuddeen Hassan Muhammad and Peter Nabende and Cheikh M. Bamba Dione and Andiswa Bukula and Rooweither Mabuya and Bonaventure F. P. Dossou and Blessing K. Sibanda and Happy Buzaaba and Jonathan Mukiibi and Godson Kalipe and Derguene Mbaye and Amelia Taylor and Fatoumata Kabore and Chris C. Emezue and Anuoluwapo Aremu and Perez Ogayo and Catherine W. Gitau and Edwin Munkoh-Buabeng and Victoire Memdjokam Koagne and Allahsera Auguste Tapo and Tebogo Macucwa and Vukosi Marivate and Elvis Mboning and Tajuddeen R. Gwadabe and Tosin P. Adewumi and Orevaoghene Ahia and Joyce Nakatumba-Nabende and Neo L. Mokono and Ignatius M Ezeani and Chiamaka Ijeoma Chukwuneke and Mofetoluwa Adeyemi and Gilles Hacheme and Idris Abdulmumin and Odunayo Ogundepo and Oreen Yousuf and Tatiana Moteu Ngoli and Dietrich Klakow},
journal={ArXiv},
year={2022},
volume={abs/2210.12391}
}
```
### Contributions
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset. |
false |
# Dataset Card for AmbigQA: Answering Ambiguous Open-domain Questions
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- [**Homepage:**](https://nlp.cs.washington.edu/ambigqa/)
- [**Repository:**](https://github.com/shmsw25/AmbigQA)
- [**Paper:**](https://arxiv.org/pdf/2004.10645.pdf)
### Dataset Summary
AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with
14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.
We provide two distributions of our new dataset AmbigNQ: a `full` version with all annotation metadata and a `light` version with only inputs and outputs.
### Supported Tasks and Leaderboards
`question-answering`
### Languages
English
## Dataset Structure
### Data Instances
An example from the data set looks as follows:
```
{'annotations': {'answer': [[]],
'qaPairs': [{'answer': [['April 19, 1987'], ['December 17, 1989']],
'question': ['When did the Simpsons first air on television as an animated short on the Tracey Ullman Show?',
'When did the Simpsons first air as a half-hour prime time show?']}],
'type': ['multipleQAs']},
'id': '-4469503464110108318',
'nq_answer': ['December 17 , 1989'],
'nq_doc_title': 'The Simpsons',
'question': 'When did the simpsons first air on television?',
'used_queries': {'query': ['When did the simpsons first air on television?'],
'results': [{'snippet': ['The <b>Simpsons</b> is an American animated <b>television</b> sitcom starring the animated \nSimpson family, ... Since its <b>debut</b> on December 17, 1989, the show <b>has</b> \nbroadcast 673 episodes and its 30th season started ... The <b>Simpsons first</b> season \n<b>was</b> the Fox network's <b>first TV</b> series to rank among a season's top 30 highest-\nrated shows.',
'The <b>Simpsons</b> is an American animated sitcom created by Matt Groening for the \nFox ... Since its <b>debut</b> on December 17, 1989, 674 episodes of The <b>Simpsons</b> \nhave been broadcast. ... When producer James L. Brooks <b>was</b> working on the \n<b>television</b> variety show The Tracey Ullman Show, he decided to include small \nanimated ...',
'... in shorts from The Tracey Ullman Show as their <b>television debut</b> in 1987. The \n<b>Simpsons</b> shorts are a series of animated shorts that <b>aired</b> as a recurring \nsegment on Fox variety <b>television</b> series The Tracey ... The final short to <b>air was</b> "\n<b>TV Simpsons</b>", originally airing on May 14, 1989. The <b>Simpsons</b> later debuted on\n ...',
'The <b>first</b> season of the American animated <b>television</b> series The <b>Simpsons</b> \noriginally <b>aired</b> on the Fox network between December 17, 1989, and May 13, \n1990, beginning with the Christmas special "<b>Simpsons</b> Roasting on an Open Fire\n". The executive producers for the <b>first</b> production season <b>were</b> Matt Groening, ...',
'The <b>Simpsons</b> is an American animated <b>television</b> sitcom created by Matt \nGroening for the Fox ... Since its <b>debut</b> on December 17, 1989, The <b>Simpsons</b> \n<b>has</b> broadcast 674 episodes. The show holds several American <b>television</b> \nlongevity ...',
'The opening sequence of the American animated <b>television</b> series The <b>Simpsons</b> \nis among the most popular opening sequences in <b>television</b> and is accompanied \nby one of <b>television's</b> most recognizable theme songs. The <b>first</b> episode to use \nthis intro <b>was</b> the series' second episode "Bart the ... <b>was</b> the <b>first</b> episode of The \n<b>Simpsons</b> to <b>air</b> in 720p high-definition <b>television</b>, ...',
'"<b>Simpsons</b> Roasting on an Open Fire", titled onscreen as "The <b>Simpsons</b> \nChristmas Special", is the premiere episode of the American animated <b>TV</b> series \nThe <b>Simpsons</b>, ... The show <b>was</b> originally intended to <b>debut</b> earlier in 1989 with "\nSome Enchanted Evening", but due to animation problems with that episode, the \nshow ...',
'"Stark Raving Dad" is the <b>first</b> episode of the third season of the American \nanimated <b>television</b> series The <b>Simpsons</b>. It <b>first aired</b> on the Fox network in the \nUnited States on September 19, 1991. ... The <b>Simpsons was</b> the second highest \nrated show on Fox the week it <b>aired</b>, behind Married... with Children. "Stark \nRaving Dad," ...',
'The <b>Simpsons</b>' twentieth season <b>aired</b> on Fox from September 28, 2008 to May \n17, 2009. With this season, the show tied Gunsmoke as the longest-running \nAmerican primetime <b>television</b> series in terms of total number ... It <b>was</b> the <b>first</b>-\never episode of the show to <b>air</b> in Europe before being seen in the United States.',
'The animated <b>TV</b> show The <b>Simpsons</b> is an American English language \nanimated sitcom which ... The <b>Simpsons was</b> dubbed for the <b>first</b> time in Punjabi \nand <b>aired</b> on Geo <b>TV</b> in Pakistan. The name of the localised Punjabi version is \nTedi Sim ...'],
'title': ['History of The Simpsons',
'The Simpsons',
'The Simpsons shorts',
'The Simpsons (season 1)',
'List of The Simpsons episodes',
'The Simpsons opening sequence',
'Simpsons Roasting on an Open Fire',
'Stark Raving Dad',
'The Simpsons (season 20)',
'Non-English versions of The Simpsons']}]},
'viewed_doc_titles': ['The Simpsons']}
```
### Data Fields
Full
```
{'id': Value(dtype='string', id=None),
'question': Value(dtype='string', id=None),
'annotations': Sequence(feature={'type': Value(dtype='string', id=None), 'answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'qaPairs': Sequence(feature={'question': Value(dtype='string', id=None), 'answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, length=-1, id=None)}, length=-1, id=None),
'viewed_doc_titles': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),
'used_queries': Sequence(feature={'query': Value(dtype='string', id=None), 'results': Sequence(feature={'title': Value(dtype='string', id=None), 'snippet': Value(dtype='string', id=None)}, length=-1, id=None)}, length=-1, id=None),
'nq_answer': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),
'nq_doc_title': Value(dtype='string', id=None)}
```
In the original data format `annotations` have different keys depending on the `type` field = `singleAnswer` or `multipleQAs`. But this implementation uses an empty list `[]` for the unavailable keys
please refer to Dataset Contents(https://github.com/shmsw25/AmbigQA#dataset-contents) for more details.
```
for example in train_light_dataset:
for i,t in enumerate(example['annotations']['type']):
if t =='singleAnswer':
# use the example['annotations']['answer'][i]
# example['annotations']['qaPairs'][i] - > is []
print(example['annotations']['answer'][i])
else:
# use the example['annotations']['qaPairs'][i]
# example['annotations']['answer'][i] - > is []
print(example['annotations']['qaPairs'][i])
```
please refer to Dataset Contents(https://github.com/shmsw25/AmbigQA#dataset-contents) for more details.
Light version only has `id`, `question`, `annotations` fields
### Data Splits
- train: 10036
- validation: 2002
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
- Wikipedia
- NQ-open:
```
@article{ kwiatkowski2019natural,
title={ Natural questions: a benchmark for question answering research},
author={ Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and others },
journal={ Transactions of the Association for Computational Linguistics },
year={ 2019 }
}
```
#### 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
[CC BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/)
### Citation Information
```
@inproceedings{ min2020ambigqa,
title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },
author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },
booktitle={ EMNLP },
year={2020}
}
```
### Contributions
Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset. |
false |
# Dataset Card for Neural Code Search
## 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:**
[facebookresearch
/
Neural-Code-Search-Evaluation-Dataset](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset/tree/master/data)
- **Repository:**
[Github](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset.git)
- **Paper:**
[arXiv](https://arxiv.org/pdf/1908.09804.pdf)
### Dataset Summary
Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs, with the hope that future work in this area can use this dataset as a common benchmark. We also provide the results of two code search models (NCS, UNIF) from recent work.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
EN - English
## Dataset Structure
### Data Instances
#### Search Corpus
The search corpus is indexed using all method bodies parsed from the 24,549 GitHub repositories. In total, there are 4,716,814 methods in this corpus. The code search model will find relevant code snippets (i.e. method bodies) from this corpus given a natural language query. In this data release, we will provide the following information for each method in the corpus:
#### Evaluation Dataset
The evaluation dataset is composed of 287 Stack Overflow question and answer pairs
### Data Fields
#### Search Corpus
- id: Each method in the corpus has a unique numeric identifier. This ID number will also be referenced in our evaluation dataset.
- filepath: The file path is in the format of :owner/:repo/relative-file-path-to-the-repo
method_name
- start_line: Starting line number of the method in the file.
- end_line: Ending line number of the method in the file.
- url: GitHub link to the method body with commit ID and line numbers encoded.
#### Evaluation Dataset
- stackoverflow_id: Stack Overflow post ID.
- question: Title fo the Stack Overflow post.
- question_url: URL of the Stack Overflow post.
- answer: Code snippet answer to the question.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The most popular Android repositories on GitHub (ranked by the number of stars) is used to create the search corpus. For each repository that we indexed, we provide the link, specific to the commit that was used.5 In total, there are 24,549 repositories.
#### 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
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
Hongyu Li, Seohyun Kim and Satish Chandra
### Licensing Information
CC-BY-NC 4.0 (Attr Non-Commercial Inter.)
### Citation Information
arXiv:1908.09804 [cs.SE]
### Contributions
Thanks to [@vinaykudari](https://github.com/vinaykudari) for adding this dataset. |
false |
# Dataset Card for TextVQA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://textvqa.org
- **Repository:** https://github.com/facebookresearch/mmf
- **Paper:** https://arxiv.org/abs/1904.08920
- **Leaderboard:** https://eval.ai/web/challenges/challenge-page/874/overview
- **Point of Contact:** mailto:amanpreet@nyu.edu
### Dataset Summary
TextVQA requires models to read and reason about text in images to answer questions about them.
Specifically, models need to incorporate a new modality of text present in the images and reason
over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images
from the OpenImages dataset. The dataset uses [VQA accuracy](https://visualqa.org/evaluation.html) metric for evaluation.
### Supported Tasks and Leaderboards
- `visual-question-answering`: The dataset can be used for Visual Question Answering tasks where given an image, you have to answer a question based on the image. For the TextVQA dataset specifically, the questions require reading and reasoning about the scene text in the given image.
### Languages
The questions in the dataset are in English.
## Dataset Structure
### Data Instances
A typical sample mainly contains the question in `question` field, an image object in `image` field, OpenImage image id in `image_id` and lot of other useful metadata. 10 answers per questions are contained in the `answers` attribute. For test set, 10 empty strings are contained in the `answers` field as the answers are not available for it.
An example look like below:
```
{'question': 'who is this copyrighted by?',
'image_id': '00685bc495504d61',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
'image_classes': ['Vehicle', 'Tower', 'Airplane', 'Aircraft'],
'flickr_original_url': 'https://farm2.staticflickr.com/5067/5620759429_4ea686e643_o.jpg',
'flickr_300k_url': 'https://c5.staticflickr.com/6/5067/5620759429_f43a649fb5_z.jpg',
'image_width': 786,
'image_height': 1024,
'answers': ['simon clancy',
'simon ciancy',
'simon clancy',
'simon clancy',
'the brand is bayard',
'simon clancy',
'simon clancy',
'simon clancy',
'simon clancy',
'simon clancy'],
'question_tokens': ['who', 'is', 'this', 'copyrighted', 'by'],
'question_id': 3,
'set_name': 'train'
},
```
### Data Fields
- `question`: string, the question that is being asked about the image
- `image_id`: string, id of the image which is same as the OpenImages id
- `image`: A `PIL.Image.Image` object containing the image about which the question is being asked. 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]`.
- `image_classes`: List[str], The OpenImages classes to which the image belongs to.
- `flickr_original_url`: string, URL to original image on Flickr
- `flickr_300k_url`: string, URL to resized and low-resolution image on Flickr.
- `image_width`: int, Width of the original image.
- `image_height`: int, Height of the original image.
- `question_tokens`: List[str], A pre-tokenized list of question.
- `answers`: List[str], List of 10 human-annotated answers for the question. These 10 answers are collected from 10 different users. The list will contain empty strings for test set for which we don't have the answers.
- `question_id`: int, Unique id of the question.
- `set_name`: string, the set to which this question belongs.
### Data Splits
There are three splits. `train`, `validation` and `test`. The `train` and `validation` sets share images with OpenImages `train` set and have their answers available. For test set answers, we return a list of ten empty strings. To get inference results and numbers on `test` set, you need to go to the [EvalAI leaderboard](https://eval.ai/web/challenges/challenge-page/874/overview) and upload your predictions there. Please see instructions at [https://textvqa.org/challenge/](https://textvqa.org/challenge/).
## Dataset Creation
### Curation Rationale
From the paper:
> Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today’s VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new “TextVQA” dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer.
### Source Data
#### Initial Data Collection and Normalization
The initial images were sourced from [OpenImages](https://storage.googleapis.com/openimages/web/factsfigures_v4.html) v4 dataset. These were first filtered based on automatic heuristics using an OCR system where we only took images which had at least some text detected in them. See [annotation process](#annotation-process) section to understand the next stages.
#### Who are the source language producers?
English Crowdsource Annotators
### Annotations
#### Annotation process
After the automatic process of filter the images that contain text, the images were manually verified using human annotators making sure that they had text. In next stage, the annotators were asked to write questions involving scene text for the image. For some images, in this stage, two questions were collected whenever possible. Finally, in the last stage, ten different human annotators answer the questions asked in last stage.
#### Who are the annotators?
Annotators are from one of the major data collection platforms such as AMT. Exact details are not mentioned in the paper.
### Personal and Sensitive Information
The dataset does have similar PII issues as OpenImages and can at some times contain human faces, license plates, and documents. Using provided `image_classes` data field is one option to try to filter out some of this information.
## Considerations for Using the Data
### Social Impact of Dataset
The paper helped realize the importance of scene text recognition and reasoning in general purpose machine learning applications and has led to many follow-up works including [TextCaps](https://textvqa.org/textcaps) and [TextOCR](https://textvqa.org/textocr). Similar datasets were introduced over the time which specifically focus on sight-disabled users such as [VizWiz](https://vizwiz.org) or focusing specifically on the same problem as TextVQA like [STVQA](https://paperswithcode.com/dataset/st-vqa), [DocVQA](https://arxiv.org/abs/2007.00398v3) and [OCRVQA](https://ocr-vqa.github.io/). Currently, most methods train on combined dataset from TextVQA and STVQA to achieve state-of-the-art performance on both datasets.
### Discussion of Biases
Question-only bias where a model is able to answer the question without even looking at the image is discussed in the [paper](https://arxiv.org/abs/1904.08920) which was a major issue with original VQA dataset. The outlier bias in answers is prevented by collecting 10 different answers which are also taken in consideration by the evaluation metric.
### Other Known Limitations
- The dataset is english only but does involve images with non-English latin characters so can involve some multi-lingual understanding.
- The performance on the dataset is also dependent on the quality of OCR used as the OCR errors can directly lead to wrong answers.
- The metric used for calculating accuracy is same as [VQA accuracy](https://visualqa.org/evaluation.html). This involves one-to-one matching with the given answers and thus doesn't allow analyzing one-off errors through OCR.
## Additional Information
### Dataset Curators
- [Amanpreet Singh](https://github.com/apsdehal)
- Vivek Natarjan
- Meet Shah
- Yu Jiang
- Xinlei Chen
- Dhruv Batra
- Devi Parikh
- Marcus Rohrbach
### Licensing Information
CC by 4.0
### Citation Information
```bibtex
@inproceedings{singh2019towards,
title={Towards VQA Models That Can Read},
author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={8317-8326},
year={2019}
}
```
### Contributions
Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset. |
false |
# Dataset Card for Snacks
## Dataset Summary
This is a dataset of 20 different types of snack foods that accompanies the book [Machine Learning by Tutorials](https://www.raywenderlich.com/books/machine-learning-by-tutorials/v2.0).
The images were taken from the [Google Open Images dataset](https://storage.googleapis.com/openimages/web/index.html), release 2017_11.
## Dataset Structure
Number of images in the train/validation/test splits:
```nohighlight
train 4838
val 955
test 952
total 6745
```
Total images in each category:
```nohighlight
apple 350
banana 350
cake 349
candy 349
carrot 349
cookie 349
doughnut 350
grape 350
hot dog 350
ice cream 350
juice 350
muffin 348
orange 349
pineapple 340
popcorn 260
pretzel 204
salad 350
strawberry 348
waffle 350
watermelon 350
```
To save space in the download, the images were resized so that their smallest side is 256 pixels. All EXIF information was removed.
### Data Splits
Train, Test, Validation
## Licensing Information
Just like the images from Google Open Images, the snacks dataset is licensed under the terms of the Creative Commons license.
The images are listed as having a [CC BY 2.0](https://creativecommons.org/licenses/by/2.0/) license.
The annotations are licensed by Google Inc. under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
The **credits.csv** file contains the original URL, author information and license for each image.
|
true |
# Dataset Card for ASSIN
## 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:** [ASSIN homepage](http://nilc.icmc.usp.br/assin/)
- **Repository:** [ASSIN repository](http://nilc.icmc.usp.br/assin/)
- **Paper:** [ASSIN: Evaluation of Semantic Similarity and Textual Inference](http://propor2016.di.fc.ul.pt/wp-content/uploads/2015/10/assin-overview.pdf)
- **Point of Contact:** [Erick Rocha Fonseca](mailto:erickrf@icmc.usp.br)
### Dataset Summary
The ASSIN (Avaliação de Similaridade Semântica e INferência textual) corpus is a corpus annotated with pairs of sentences written in
Portuguese that is suitable for the exploration of textual entailment and paraphrasing classifiers. The corpus contains pairs of sentences
extracted from news articles written in European Portuguese (EP) and Brazilian Portuguese (BP), obtained from Google News Portugal
and Brazil, respectively. To create the corpus, the authors started by collecting a set of news articles describing the
same event (one news article from Google News Portugal and another from Google News Brazil) from Google News.
Then, they employed Latent Dirichlet Allocation (LDA) models to retrieve pairs of similar sentences between sets of news
articles that were grouped together around the same topic. For that, two LDA models were trained (for EP and for BP)
on external and large-scale collections of unannotated news articles from Portuguese and Brazilian news providers, respectively.
Then, the authors defined a lower and upper threshold for the sentence similarity score of the retrieved pairs of sentences,
taking into account that high similarity scores correspond to sentences that contain almost the same content (paraphrase candidates),
and low similarity scores correspond to sentences that are very different in content from each other (no-relation candidates).
From the collection of pairs of sentences obtained at this stage, the authors performed some manual grammatical corrections
and discarded some of the pairs wrongly retrieved. Furthermore, from a preliminary analysis made to the retrieved sentence pairs
the authors noticed that the number of contradictions retrieved during the previous stage was very low. Additionally, they also
noticed that event though paraphrases are not very frequent, they occur with some frequency in news articles. Consequently,
in contrast with the majority of the currently available corpora for other languages, which consider as labels “neutral”, “entailment”
and “contradiction” for the task of RTE, the authors of the ASSIN corpus decided to use as labels “none”, “entailment” and “paraphrase”.
Finally, the manual annotation of pairs of sentences was performed by human annotators. At least four annotators were randomly
selected to annotate each pair of sentences, which is done in two steps: (i) assigning a semantic similarity label (a score between 1 and 5,
from unrelated to very similar); and (ii) providing an entailment label (one sentence entails the other, sentences are paraphrases,
or no relation). Sentence pairs where at least three annotators do not agree on the entailment label were considered controversial
and thus discarded from the gold standard annotations. The full dataset has 10,000 sentence pairs, half of which in Brazilian Portuguese (ptbr)
and half in European Portuguese (ptpt). Either language variant has 2,500 pairs for training, 500 for validation and 2,000 for testing.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Portuguese.
## Dataset Structure
### Data Instances
An example from the ASSIN dataset looks as follows:
```
{
"entailment_judgment": 0,
"hypothesis": "André Gomes entra em campo quatro meses depois de uma lesão na perna esquerda o ter afastado dos relvados.",
"premise": "Relembre-se que o atleta estava afastado dos relvados desde maio, altura em que contraiu uma lesão na perna esquerda.",
"relatedness_score": 3.5,
"sentence_pair_id": 1
}
```
### Data Fields
- `sentence_pair_id`: a `int64` feature.
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `relatedness_score`: a `float32` feature.
- `entailment_judgment`: a classification label, with possible values including `NONE`, `ENTAILMENT`, `PARAPHRASE`.
### Data Splits
The data is split into train, validation and test set. The split sizes are as follow:
| | Train | Val | Test |
| ----- | ------ | ----- | ---- |
| full | 5000 | 1000 | 4000 |
| ptbr | 2500 | 500 | 2000 |
| ptpt | 2500 | 500 | 2000 |
## 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{fonseca2016assin,
title={ASSIN: Avaliacao de similaridade semantica e inferencia textual},
author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S},
booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal},
pages={13--15},
year={2016}
}
```
### Contributions
Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset. |
true |
# Dataset Card for conv_ai_2
## 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/DeepPavlov/convai/tree/master/2018
- **Repository:** https://github.com/DeepPavlov/convai/tree/master/2018
- **Paper:** https://arxiv.org/abs/1902.00098
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
ConvAI is a dataset of human-to-bot conversations labeled for quality. This data can be used to train a metric for evaluating dialogue systems. Moreover, it can be used in the development of chatbots themselves: it contains information on the quality of utterances and entire dialogues, that can guide a dialogue system in search of better answers.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
```
{
"dialog_id": "0x648cc5b7",
"dialog": [
{
"id": 0,
"sender": "participant2",
"text": "Hi! How is your day? \ud83d\ude09",
"sender_class": "Bot"
},
{
"id": 1,
"sender": "participant1",
"text": "Hi! Great!",
"sender_class": "Human"
},
{
"id": 2,
"sender": "participant2",
"text": "I am good thanks for asking are you currently in high school?",
"sender_class": "Bot"
}
],
"bot_profile": [
"my current goal is to run a k.",
"when i grow up i want to be a physical therapist.",
"i'm currently in high school.",
"i make straight as in school.",
"i won homecoming queen this year."
],
"user_profile": [
"my favorite color is red.",
"i enjoy listening to classical music.",
"i'm a christian.",
"i can drive a tractor."
],
"eval_score": 4,
"profile_match": 1
}
```
### Data Fields
- dialog_id : specifies the unique ID for the dialogs.
- dialog : Array of dialogs.
- bot_profile : Bot annotated response that will be used for evaluation.
- user_profile : user annoted response that will be used for evaluation.
- eval_score : (`1`,` 2`,` 3`,` 4`,` 5`) how does an user like a conversation. The missing values are replaced with` -1`
- profile_match : (`0`,` 1`) an user is given by two profile descriptions (4 sentences each), one of them is the one given to the bot it had been talking to, the other one is random; the user needs to choose one of them.The missing values are replaced with` -1`
### 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
@article{DBLP:journals/corr/abs-1902-00098,
author = {Emily Dinan and
Varvara Logacheva and
Valentin Malykh and
Alexander H. Miller and
Kurt Shuster and
Jack Urbanek and
Douwe Kiela and
Arthur Szlam and
Iulian Serban and
Ryan Lowe and
Shrimai Prabhumoye and
Alan W. Black and
Alexander I. Rudnicky and
Jason Williams and
Joelle Pineau and
Mikhail S. Burtsev and
Jason Weston},
title = {The Second Conversational Intelligence Challenge (ConvAI2)},
journal = {CoRR},
volume = {abs/1902.00098},
year = {2019},
url = {http://arxiv.org/abs/1902.00098},
archivePrefix = {arXiv},
eprint = {1902.00098},
timestamp = {Wed, 07 Oct 2020 11:09:41 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1902-00098.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. |
false |
# Dataset Card for MetaLWOz
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [MetaLWOz Project Website](https://www.microsoft.com/en-us/research/project/metalwoz/)
- **Paper:** [Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer](https://ieeexplore.ieee.org/abstract/document/9053599), and [Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation](https://arxiv.org/pdf/2003.01680.pdf)
- **Point of Contact:** [Hannes Schulz](https://www.microsoft.com/en-us/research/people/haschulz/)
### Dataset Summary
MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models.
We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for
conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to
quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas
of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two
human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human
user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a
particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total.
Dialogues are a minimum of 10 turns long.
### Supported Tasks and Leaderboards
This dataset supports a range of task.
- **Generative dialogue modeling** or `dialogue-modeling`: This data can be used to train task-oriented dialogue
models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast
-adaptation models fall into the research areas of transfer learning and meta learning. The text of the dialogues
can be used to train a sequence model on the utterances.
Example of sample input/output is given in section [Data Instances](#data-instances)
### Languages
The text in the dataset is in English (`en`).
## Dataset Structure
### Data Instances
A data instance is a full multi-turn dialogue between two crowd-workers, one had the role of being a `bot`, and the other one was the `user`. Both were
given a `domain` and a `task`. Each turn has a single utterance, e.g.:
```
Domain: Ski
User Task: You want to know if there are good ski hills an
hour’s drive from your current location.
Bot Task: Tell the user that there are no ski hills in their
immediate location.
Bot: Hello how may I help you?
User: Is there any good ski hills an hour’s drive from my
current location?
Bot: I’m sorry to inform you that there are no ski hills in your
immediate location
User: Can you help me find the nearest?
Bot: Absolutely! It looks like you’re about 3 hours away from
Bear Mountain. That seems to be the closest.
User: Hmm.. sounds good
Bot: Alright! I can help you get your lift tickets now!When
will you be going?
User: Awesome! please get me a ticket for 10pax
Bot: You’ve got it. Anything else I can help you with?
User: None. Thanks again!
Bot: No problem!
```
Example of input/output for this dialog:
```
Input: dialog history = Hello how may I help you?; Is there
any good ski hills an hour’s drive from my current location?;
I’m sorry to inform you that there are no ski hills in your
immediate location
Output: user response = Can you help me find the nearest?
```
### Data Fields
Each dialogue instance has the following fields:
- `id`: a unique ID identifying the dialog.
- `user_id`: a unique ID identifying the user.
- `bot_id`: a unique ID identifying the bot.
- `domain`: a unique ID identifying the domain. Provides a mapping to tasks dataset.
- `task_id`: a unique ID identifying the task. Provides a mapping to tasks dataset.
- `turns`: the sequence of utterances alternating between `bot` and `user`, starting with a prompt from `bot`.
Each task instance has following fields:
- `task_id`: a unique ID identifying the task.
- `domain`: a unique ID identifying the domain.
- `bot_prompt`: The task specification for bot.
- `bot_role`: The domain oriented role of bot.
- `user_prompt`: The task specification for user.
- `user_role`: The domain oriented role of user.
### Data Splits
The dataset is split into a `train` and `test` split with the following sizes:
| | Training MetaLWOz | Evaluation MetaLWOz | Combined |
| ----- | ------ | ----- | ---- |
| Total Domains | 47 | 4 | 51 |
| Total Tasks | 226 | 14 | 240 |
| Total Dialogs | 37884 | 2319 | 40203 |
Below are the various statistics of the dataset:
| Statistic | Mean | Minimum | Maximum |
| ----- | ------ | ----- | ---- |
| Number of tasks per domain | 4.8 | 3 | 11 |
| Number of dialogs per domain | 806.0 | 288 | 1990 |
| Number of dialogs per task | 167.6 | 32 | 285 |
| Number of turns per dialog | 11.4 | 10 | 46 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset v1 version is created by team of researchers from Microsoft Research (Montreal, Canada)
### Licensing Information
The dataset is released under [Microsoft Research Data License Agreement](https://msropendata-web-api.azurewebsites.net/licenses/2f933be3-284d-500b-7ea3-2aa2fd0f1bb2/view)
### Citation Information
You can cite the following for the various versions of MetaLWOz:
Version 1.0
```
@InProceedings{shalyminov2020fast,
author = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},
title = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},
booktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year = {2020},
month = {April},
url = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a
-hybrid-generative-retrieval-transformer/},
}
```
### Contributions
Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset. |
false |
# Dataset Card for "break_data"
## 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/allenai/Break](https://github.com/allenai/Break)
- **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:** 79.86 MB
- **Size of the generated dataset:** 155.55 MB
- **Total amount of disk used:** 235.39 MB
### Dataset Summary
Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations
(QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases.
This repository contains the Break dataset along with information on the exact data format.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### QDMR
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 15.93 MB
- **Total amount of disk used:** 31.90 MB
An example of 'validation' looks as follows.
```
{
"decomposition": "return flights ;return #1 from denver ;return #2 to philadelphia ;return #3 if available",
"operators": "['select', 'filter', 'filter', 'filter']",
"question_id": "ATIS_dev_0",
"question_text": "what flights are available tomorrow from denver to philadelphia ",
"split": "dev"
}
```
#### QDMR-high-level
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 6.54 MB
- **Total amount of disk used:** 22.51 MB
An example of 'train' looks as follows.
```
{
"decomposition": "return ground transportation ;return #1 which is available ;return #2 from the pittsburgh airport ;return #3 to downtown ;return the cost of #4",
"operators": "['select', 'filter', 'filter', 'filter', 'project']",
"question_id": "ATIS_dev_102",
"question_text": "what ground transportation is available from the pittsburgh airport to downtown and how much does it cost ",
"split": "dev"
}
```
#### QDMR-high-level-lexicon
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 31.64 MB
- **Total amount of disk used:** 47.61 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"allowed_tokens": "\"['higher than', 'same as', 'what ', 'and ', 'than ', 'at most', 'he', 'distinct', 'House', 'two', 'at least', 'or ', 'date', 'o...",
"source": "What office, also held by a member of the Maine House of Representatives, did James K. Polk hold before he was president?"
}
```
#### QDMR-lexicon
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 77.19 MB
- **Total amount of disk used:** 93.16 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"allowed_tokens": "\"['higher than', 'same as', 'what ', 'and ', 'than ', 'at most', 'distinct', 'two', 'at least', 'or ', 'date', 'on ', '@@14@@', ...",
"source": "what flights are available tomorrow from denver to philadelphia "
}
```
#### logical-forms
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 24.25 MB
- **Total amount of disk used:** 40.22 MB
An example of 'train' looks as follows.
```
{
"decomposition": "return ground transportation ;return #1 which is available ;return #2 from the pittsburgh airport ;return #3 to downtown ;return the cost of #4",
"operators": "['select', 'filter', 'filter', 'filter', 'project']",
"program": "some program",
"question_id": "ATIS_dev_102",
"question_text": "what ground transportation is available from the pittsburgh airport to downtown and how much does it cost ",
"split": "dev"
}
```
### Data Fields
The data fields are the same among all splits.
#### QDMR
- `question_id`: a `string` feature.
- `question_text`: a `string` feature.
- `decomposition`: a `string` feature.
- `operators`: a `string` feature.
- `split`: a `string` feature.
#### QDMR-high-level
- `question_id`: a `string` feature.
- `question_text`: a `string` feature.
- `decomposition`: a `string` feature.
- `operators`: a `string` feature.
- `split`: a `string` feature.
#### QDMR-high-level-lexicon
- `source`: a `string` feature.
- `allowed_tokens`: a `string` feature.
#### QDMR-lexicon
- `source`: a `string` feature.
- `allowed_tokens`: a `string` feature.
#### logical-forms
- `question_id`: a `string` feature.
- `question_text`: a `string` feature.
- `decomposition`: a `string` feature.
- `operators`: a `string` feature.
- `split`: a `string` feature.
- `program`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-----------------------|----:|---------:|---:|
|QDMR |44321| 7760|8069|
|QDMR-high-level |17503| 3130|3195|
|QDMR-high-level-lexicon|17503| 3130|3195|
|QDMR-lexicon |44321| 7760|8069|
|logical-forms |44098| 7719|8006|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{Wolfson2020Break,
title={Break It Down: A Question Understanding Benchmark},
author={Wolfson, Tomer and Geva, Mor and Gupta, Ankit and Gardner, Matt and Goldberg, Yoav and Deutch, Daniel and Berant, Jonathan},
journal={Transactions of the Association for Computational Linguistics},
year={2020},
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
false |
# Dataset Card for RiddleSense
## 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://inklab.usc.edu/RiddleSense/
- **Repository:** https://github.com/INK-USC/RiddleSense/
- **Paper:** https://inklab.usc.edu/RiddleSense/riddlesense_acl21_paper.pdf
- **Leaderboard:** https://inklab.usc.edu/RiddleSense/#leaderboard
- **Point of Contact:** [Yuchen Lin](yuchen.lin@usc.edu)
### Dataset Summary
Answering such a riddle-style question is a challenging cognitive process, in that it requires
complex commonsense reasoning abilities, an understanding of figurative language, and counterfactual reasoning
skills, which are all important abilities for advanced natural language understanding (NLU). However,
there is currently no dedicated datasets aiming to test these abilities. Herein, we present RiddleSense,
a new multiple-choice question answering task, which comes with the first large dataset (5.7k examples) for answering
riddle-style commonsense questions. We systematically evaluate a wide range of models over the challenge,
and point out that there is a large gap between the best-supervised model and human performance suggesting
intriguing future research in the direction of higher-order commonsense reasoning and linguistic creativity towards
building advanced NLU systems.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"answerKey": "E",
"choices": {
"label": ["A", "B", "C", "D", "E"],
"text": ["throw", "bit", "gallow", "mouse", "hole"]
},
"question": "A man is incarcerated in prison, and as his punishment he has to carry a one tonne bag of sand backwards and forwards across a field the size of a football pitch. What is the one thing he can put in it to make it lighter?"
}
```
### Data Fields
Data Fields
The data fields are the same among all splits.
default
- `answerKey`: a string feature.
- `question`: a string feature.
- `choices`: a dictionary feature containing:
- `label`: a string feature.
- `text`: a string feature.
### Data Splits
|name| train| validation| test|
|---|---|---|---|
|default| 3510| 1021| 1184|
## 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
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
The copyright of RiddleSense dataset is consistent with the terms of use of the fan websites and the intellectual property and privacy rights of the original sources. All of our riddles and answers are from fan websites that can be accessed freely. The website owners state that you may print and download material from the sites solely for non-commercial use provided that we agree not to change or delete any copyright or proprietary notices from the materials. The dataset users must agree that they will only use the dataset for research purposes before they can access the both the riddles and our annotations. We do not vouch for the potential bias or fairness issue that might exist within the riddles. You do not have the right to redistribute them. Again, you must not use this dataset for any commercial purposes.
### Citation Information
```
@InProceedings{lin-etal-2021-riddlesense,
title={RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge},
author={Lin, Bill Yuchen and Wu, Ziyi and Yang, Yichi and Lee, Dong-Ho and Ren, Xiang},
journal={Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL-IJCNLP 2021): Findings},
year={2021}
}
```
### Contributions
Thanks to [@ziyiwu9494](https://github.com/ziyiwu9494) for adding this dataset. |
true |
# Dataset Card for "movie_rationales"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/jayded/eraserbenchmark
- **Paper:** [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://aclanthology.org/2020.acl-main.408/)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3.90 MB
- **Size of the generated dataset:** 8.73 MB
- **Total amount of disk used:** 12.62 MB
### Dataset Summary
The movie rationale dataset contains human annotated rationales for movie
reviews.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 3.90 MB
- **Size of the generated dataset:** 8.73 MB
- **Total amount of disk used:** 12.62 MB
An example of 'validation' looks as follows.
```
{
"evidences": ["Fun movie"],
"label": 1,
"review": "Fun movie\n"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `review`: a `string` feature.
- `label`: a classification label, with possible values including `NEG` (0), `POS` (1).
- `evidences`: a `list` of `string` features.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 1600| 200| 199|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{deyoung-etal-2020-eraser,
title = "{ERASER}: {A} Benchmark to Evaluate Rationalized {NLP} Models",
author = "DeYoung, Jay and
Jain, Sarthak and
Rajani, Nazneen Fatema and
Lehman, Eric and
Xiong, Caiming and
Socher, Richard and
Wallace, Byron C.",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.408",
doi = "10.18653/v1/2020.acl-main.408",
pages = "4443--4458",
}
@InProceedings{zaidan-eisner-piatko-2008:nips,
author = {Omar F. Zaidan and Jason Eisner and Christine Piatko},
title = {Machine Learning with Annotator Rationales to Reduce Annotation Cost},
booktitle = {Proceedings of the NIPS*2008 Workshop on Cost Sensitive Learning},
month = {December},
year = {2008}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset. |
false |
# Dataset Card for Taskmaster-2
## 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:** [Taskmaster-1](https://research.google/tools/datasets/taskmaster-1/)
- **Repository:** [GitHub](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-2-2020)
- **Paper:** [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://arxiv.org/abs/1909.05358)
- **Leaderboard:** N/A
- **Point of Contact:** [Taskmaster Googlegroup](taskmaster-datasets@googlegroups.com)
### Dataset Summary
Taskmaster is dataset for goal oriented conversations. The Taskmaster-2 dataset consists of 17,289 dialogs
in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports.
Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs,
Taskmaster-2 consists entirely of spoken two-person dialogs. In addition, while Taskmaster-1 is
almost exclusively task-based, Taskmaster-2 contains a good number of search- and recommendation-oriented dialogs.
All dialogs in this release were created using a Wizard of Oz (WOz) methodology in which crowdsourced
workers played the role of a 'user' and trained call center operators played the role of the 'assistant'.
In this way, users were led to believe they were interacting with an automated system that “spoke”
using text-to-speech (TTS) even though it was in fact a human behind the scenes.
As a result, users could express themselves however they chose in the context of an automated interface.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English language.
## Dataset Structure
### Data Instances
A typical example looks like this
```
{
"conversation_id": "dlg-0047a087-6a3c-4f27-b0e6-268f53a2e013",
"instruction_id": "flight-6",
"utterances": [
{
"index": 0,
"segments": [],
"speaker": "USER",
"text": "Hi, I'm looking for a flight. I need to visit a friend."
},
{
"index": 1,
"segments": [],
"speaker": "ASSISTANT",
"text": "Hello, how can I help you?"
},
{
"index": 2,
"segments": [],
"speaker": "ASSISTANT",
"text": "Sure, I can help you with that."
},
{
"index": 3,
"segments": [],
"speaker": "ASSISTANT",
"text": "On what dates?"
},
{
"index": 4,
"segments": [
{
"annotations": [
{
"name": "flight_search.date.depart_origin"
}
],
"end_index": 37,
"start_index": 27,
"text": "March 20th"
},
{
"annotations": [
{
"name": "flight_search.date.return"
}
],
"end_index": 45,
"start_index": 41,
"text": "22nd"
}
],
"speaker": "USER",
"text": "I'm looking to travel from March 20th to 22nd."
}
]
}
```
### Data Fields
Each conversation in the data file has the following structure:
- `conversation_id`: A universally unique identifier with the prefix 'dlg-'. The ID has no meaning.
- `utterances`: A list of utterances that make up the conversation.
- `instruction_id`: A reference to the file(s) containing the user (and, if applicable, agent) instructions for this conversation.
Each utterance has the following fields:
- `index`: A 0-based index indicating the order of the utterances in the conversation.
- `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance.
- `text`: The raw text of the utterance. In case of self dialogs (one_person_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers.
- `segments`: A list of various text spans with semantic annotations.
Each segment has the following fields:
- `start_index`: The position of the start of the annotation in the utterance text.
- `end_index`: The position of the end of the annotation in the utterance text.
- `text`: The raw text that has been annotated.
- `annotations`: A list of annotation details for this segment.
Each annotation has a single field:
- `name`: The annotation name.
### Data Splits
There are no deafults splits for all the config. The below table lists the number of examples in each config.
| Config | Train |
|-------------------|--------|
| flights | 2481 |
| food-orderings | 1050 |
| hotels | 2355 |
| movies | 3047 |
| music | 1602 |
| restaurant-search | 3276 |
| sports | 3478 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is licensed under `Creative Commons Attribution 4.0 License`
### Citation Information
[More Information Needed]
```
@inproceedings{48484,
title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
year = {2019}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. |
false |
# Dataset Card for turkish_ner
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://arxiv.org/abs/1702.02363
- **Repository:** [Needs More Information]
- **Paper:** http://arxiv.org/abs/1702.02363
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** erayyildiz@ktu.edu.tr
### Dataset Summary
Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Turkish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
There's only the training set.
## 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
H. Bahadir Sahin, Caglar Tirkaz, Eray Yildiz, Mustafa Tolga Eren and Omer Ozan Sonmez
### Licensing Information
Creative Commons Attribution 4.0 International
### Citation Information
@InProceedings@article{DBLP:journals/corr/SahinTYES17,
author = {H. Bahadir Sahin and
Caglar Tirkaz and
Eray Yildiz and
Mustafa Tolga Eren and
Omer Ozan Sonmez},
title = {Automatically Annotated Turkish Corpus for Named Entity Recognition
and Text Categorization using Large-Scale Gazetteers},
journal = {CoRR},
volume = {abs/1702.02363},
year = {2017},
url = {http://arxiv.org/abs/1702.02363},
archivePrefix = {arXiv},
eprint = {1702.02363},
timestamp = {Mon, 13 Aug 2018 16:46:36 +0200},
biburl = {https://dblp.org/rec/journals/corr/SahinTYES17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
### Contributions
Thanks to [@merveenoyan](https://github.com/merveenoyan) for adding this dataset. |
false |
# Dataset Card for FreebaseQA
## 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:** [FreebaseQA repository](https://github.com/kelvin-jiang/FreebaseQA)
- **Paper:** [FreebaseQA ACL paper](https://www.aclweb.org/anthology/N19-1028.pdf)
- **Leaderboard:**
- **Point of Contact:** [Kelvin Jiang](https://github.com/kelvin-jiang)
### Dataset Summary
FreebaseQA is a dataset for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```
{'Parses': {'Answers': [{'AnswersMid': ['m.01npcx'], 'AnswersName': [['goldeneye']]}, {'AnswersMid': ['m.01npcx'], 'AnswersName': [['goldeneye']]}], 'InferentialChain': ['film.film_character.portrayed_in_films..film.performance.film', 'film.actor.film..film.performance.film'], 'Parse-Id': ['FreebaseQA-train-0.P0', 'FreebaseQA-train-0.P1'], 'PotentialTopicEntityMention': ['007', 'pierce brosnan'], 'TopicEntityMid': ['m.0clpml', 'm.018p4y'], 'TopicEntityName': ['james bond', 'pierce brosnan']}, 'ProcessedQuestion': "what was pierce brosnan's first outing as 007", 'Question-ID': 'FreebaseQA-train-0', 'RawQuestion': "What was Pierce Brosnan's first outing as 007?"}
```
### Data Fields
- `Question-ID`: a `string` feature representing ID of each question.
- `RawQuestion`: a `string` feature representing the original question collected from data sources.
- `ProcessedQuestion`: a `string` feature representing the question processed with some operations such as removal of trailing question mark and decapitalization.
- `Parses`: a dictionary feature representing the semantic parse(s) for the question containing:
- `Parse-Id`: a `string` feature representing the ID of each semantic parse.
- `PotentialTopicEntityMention`: a `string` feature representing the potential topic entity mention in the question.
- `TopicEntityName`: a `string` feature representing name or alias of the topic entity in the question from Freebase.
- `TopicEntityMid`: a `string` feature representing the Freebase MID of the topic entity in the question.
- `InferentialChain`: a `string` feature representing path from the topic entity node to the answer node in Freebase, labeled as a predicate.
- `Answers`: a dictionary feature representing the answer found from this parse containing:
- `AnswersMid`: a `string` feature representing the Freebase MID of the answer.
- `AnswersName`: a `list` of `string` features representing the answer string from the original question-answer pair.
### Data Splits
This data set contains 28,348 unique questions that are divided into three subsets: train (20,358), dev (3,994) and eval (3,996), formatted as JSON files: FreebaseQA-[train|dev|eval].json
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data set is generated by matching trivia-type question-answer pairs with subject-predicateobject triples in Freebase. For each collected question-answer pair, we first tag all entities in each question and search for relevant predicates that bridge a tagged entity with the answer in Freebase. Finally, human annotation is used to remove false positives in these matched triples.
#### 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
Kelvin Jiang - Currently at University of Waterloo. Work was done at
York University.
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{jiang-etal-2019-freebaseqa,
title = "{F}reebase{QA}: A New Factoid {QA} Data Set Matching Trivia-Style Question-Answer Pairs with {F}reebase",
author = "Jiang, Kelvin and
Wu, Dekun and
Jiang, Hui",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1028",
doi = "10.18653/v1/N19-1028",
pages = "318--323",
abstract = "In this paper, we present a new data set, named FreebaseQA, for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase. The data set is generated by matching trivia-type question-answer pairs with subject-predicate-object triples in Freebase. For each collected question-answer pair, we first tag all entities in each question and search for relevant predicates that bridge a tagged entity with the answer in Freebase. Finally, human annotation is used to remove any false positive in these matched triples. Using this method, we are able to efficiently generate over 54K matches from about 28K unique questions with minimal cost. Our analysis shows that this data set is suitable for model training in factoid QA tasks beyond simpler questions since FreebaseQA provides more linguistically sophisticated questions than other existing data sets.",
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchhablani) and [@anaerobeth](https://github.com/anaerobeth) for adding this dataset. |
false |
# Dataset Card for "Few-NERD"
## 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://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)
- **Repository:** [https://github.com/thunlp/Few-NERD](https://github.com/thunlp/Few-NERD)
- **Paper:** [https://aclanthology.org/2021.acl-long.248/](https://aclanthology.org/2021.acl-long.248/)
- **Point of Contact:** See [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)
### Dataset Summary
This script is for loading the Few-NERD dataset from https://ningding97.github.io/fewnerd/.
Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities, and 4,601,223 tokens. Three benchmark tasks are built, one is supervised (Few-NERD (SUP)) and the other two are few-shot (Few-NERD (INTRA) and Few-NERD (INTER)).
NER tags use the `IO` tagging scheme. The original data uses a 2-column CoNLL-style format, with empty lines to separate sentences. DOCSTART information is not provided since the sentences are randomly ordered.
For more details see https://ningding97.github.io/fewnerd/ and https://aclanthology.org/2021.acl-long.248/.
### Supported Tasks and Leaderboards
- **Tasks:** Named Entity Recognition, Few-shot NER
- **Leaderboards:**
- https://ningding97.github.io/fewnerd/
- named-entity-recognition:https://paperswithcode.com/sota/named-entity-recognition-on-few-nerd-sup
- other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-intra
- other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-inter
### Languages
English
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:**
- `super`: 14.6 MB
- `intra`: 11.4 MB
- `inter`: 11.5 MB
- **Size of the generated dataset:**
- `super`: 116.9 MB
- `intra`: 106.2 MB
- `inter`: 106.2 MB
- **Total amount of disk used:** 366.8 MB
An example of 'train' looks as follows.
```json
{
'id': '1',
'tokens': ['It', 'starred', 'Hicks', "'s", 'wife', ',', 'Ellaline', 'Terriss', 'and', 'Edmund', 'Payne', '.'],
'ner_tags': [0, 0, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0],
'fine_ner_tags': [0, 0, 51, 0, 0, 0, 50, 50, 0, 50, 50, 0]
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `art` (1), `building` (2), `event` (3), `location` (4), `organization` (5), `other`(6), `person` (7), `product` (8)
- `fine_ner_tags`: a `list` of fine-grained classification labels, with possible values including `O` (0), `art-broadcastprogram` (1), `art-film` (2), ...
### Data Splits
| Task | Train | Dev | Test |
| ----- | ------ | ----- | ---- |
| SUP | 131767 | 18824 | 37648 |
| INTRA | 99519 | 19358 | 44059 |
| INTER | 130112 | 18817 | 14007 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@inproceedings{ding-etal-2021-nerd,
title = "Few-{NERD}: A Few-shot Named Entity Recognition Dataset",
author = "Ding, Ning and
Xu, Guangwei and
Chen, Yulin and
Wang, Xiaobin and
Han, Xu and
Xie, Pengjun and
Zheng, Haitao and
Liu, Zhiyuan",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.248",
doi = "10.18653/v1/2021.acl-long.248",
pages = "3198--3213",
}
```
### Contributions |
false | # Dataset Card for Evidence Infer
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://evidence-inference.ebm-nlp.com/
- **Repository:** https://github.com/jayded/evidence-inference
- **Paper:** [Evidence Inference 2.0: More Data, Better Models](https://arxiv.org/abs/2005.04177)
- **Leaderboard:** http://evidence-inference.ebm-nlp.com/leaderboard/
- **Point of Contact:** []()
### Dataset Summary
Data and code from our "Inferring Which Medical Treatments Work from Reports of Clinical Trials", NAACL 2019. This work concerns inferring the results reported in clinical trials from text.
The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator.
The dataset could be used for automatic data extraction of the results of a given RCT. This would enable readers to discover the effectiveness of different treatments without needing to read the paper.
We have recently collected additional data for this task (https://arxiv.org/abs/2005.04177), which we will present at BioNLP 2020.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
- English (`en`).
## Dataset Structure
### Data Instances
```
{'Text': "TITLE: Liraglutide, a once-daily human GLP-1 analogue, added to a sulphonylurea over 26 weeks produces greater improvements in glycaemic and weight control compared with adding rosiglitazone or placebo in subjects with Type 2 diabetes (LEAD-1 SU)\n\n ABSTRACT.AIM:\nTo compare the effects of combining liraglutide (0.6, 1.2 or 1.8 mg/day) or rosiglitazone 4 mg/day (all n ≥ 228) or placebo (n = 114) with glimepiride (2–4 mg/day) on glycaemic control, body weight and safety in Type 2 diabetes.\n\nABSTRACT.METHODS:\nIn total, 1041 adults (mean ± sd), age 56 ± 10 years, weight 82 ± 17 kg and glycated haemoglobin (HbA1c) 8.4 ± 1.0% at 116 sites in 21 countries were stratified based on previous oral glucose-lowering mono : combination therapies (30 : 70%) to participate in a five-arm, 26-week, double-dummy, randomized study.\n\nABSTRACT.RESULTS:\nLiraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride. Liraglutide 0.6 mg was less effective (−0.6%, baseline 8.4%). Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l). Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). Changes in body weight with liraglutide 1.8 mg (−0.2 kg, baseline 83.0 kg), 1.2 mg (+0.3 kg, baseline 80.0 kg) or placebo (−0.1 kg, baseline 81.9 kg) were less than with rosiglitazone (+2.1 kg, P < 0.0001, baseline 80.6 kg). Main adverse events for all treatments were minor hypoglycaemia (< 10%), nausea (< 11%), vomiting (< 5%) and diarrhoea (< 8%).\n\nABSTRACT.CONCLUSIONS:\nLiraglutide added to glimepiride was well tolerated and provided improved glycaemic control and favourable weight profile.\n\nBODY.INTRODUCTION:\nMost drugs that target Type 2 diabetes (T2D) also cause weight gain or hypoglycaemia, or both, with the risk increasing with combination therapy. Glucagon-like peptide-1 (GLP-1)-based therapies stimulate insulin secretion and reduce glucagon secretion only during hyperglycaemia. GLP-1 also slows gastric emptying and reduces appetite [1]. Although American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD) guidelines recommend lifestyle and metformin as initial therapy for T2D [2], sulphonylureas are used widely, particularly when metformin or thiazolidinediones are not tolerated. Glycaemic control eventually deteriorates with sulphonylureas while hypoglycaemia and weight gain are common [3]. Incretin therapy improves glycaemic control with low hypoglycaemic risk, while delayed gastric emptying and reduced appetite can reduce weight [1,4]. Liraglutide is a once-daily human GLP-1 analogue with 97% linear amino-acid sequence homology to human GLP-1 [5] and half-life of 13 h after subcutaneous administration that produces 24-h blood glucose control [6]. Liraglutide monotherapy for 14 weeks reduced glycated haemoglobin (HbA1c) by 1.7% and fasting plasma glucose (FPG) by 3.4 mmol/l without causing hypoglycaemia, along with weight loss (∼3 kg) compared with placebo [7]. Improvements in pancreatic B-cell function [7–9] and blood pressure [7], along with decreased glucagon secretion [7,10], also occurred. As part of the phase 3 programme [the Liraglutide Effect and Action in Diabetes (LEAD) programme] with liraglutide in > 4000 subjects with T2D as monotherapy or in combination therapy, this 26-week trial examined liraglutide plus glimepiride compared with either placebo or rosiglitazone added to glimepiride on glycaemic control and body weight.\n\nBODY.SUBJECTS AND METHODS.STUDY PARTICIPANTS:\nInclusion criteria: T2D treated with oral glucose-lowering agents (OGLAs) for ≥ 3 months; 18–80 years of age; HbA1c 7.0–11.0% (previous OGLA monotherapy) or 7.0–10.0% (previous OGLA combination therapy); body mass index (BMI) ≤ 45.0 kg/m2. Exclusion criteria: used insulin within 3 months, impaired liver or renal function, uncontrolled hypertension (≥ 180/100 mmHg), cancer or used any drugs apart from OGLAs likely to affect glucose concentrations. Subjects provided written informed consent. The study was conducted in accordance with good clinical practice guidelines and approved by independent ethics committees.\n\nBODY.SUBJECTS AND METHODS.STUDY DESIGN:\nThe study was a 26-week, double-blind, double-dummy, randomized, active-control, five-armed parallel (116 sites in 21 countries, primarily Europe and Asia) trial enrolling 1041 subjects (1–37 subjects per centre), all receiving glimepiride (2–4 mg/day) in combination with (Fig. 1): FIGURE 1Overview of trial design and treatment arms. one of three liraglutide doses [0.6, 1.2 or 1.8 mg, injected subcutaneously (Novo Nordisk, Bagsvaerd, Denmark) and rosiglitazone placebo];liraglutide placebo and rosiglitazone placebo;liraglutide placebo and rosiglitazone 4 mg/day (rosiglitazone; AvandiaTM; GlaxoSmithKline, London, UK). The doses of rosiglitazone and glimepiride used were determined by the highest doses approved in all participating counties. After discontinuing previous OGLAs except glimepiride, separate 2-week titration and maintenance periods with glimepiride (open-label) preceded randomization (Fig. 1). Subjects were stratified according to previous treatment (monotherapy or combination therapy). After randomization, 2-week treatment titration and 24-week treatment (maintenance) phases (Fig. 1) were completed. Liraglutide was up-titrated weekly in 0.6-mg increments until allocated doses were reached. Glimepiride could be adjusted between 2 and 4 mg/day in case of hypoglycaemia or other adverse events (AEs), while other drug doses were fixed. Liraglutide (active and placebo) was supplied in 3-ml pre-filled pens with 31G needles (Novo Nordisk). Subjects were encouraged to inject liraglutide into the upper arm, thigh or abdomen at the same time each day. Rosiglitazone and glimepiride were taken in the morning or with the first meal.\n\nBODY.SUBJECTS AND METHODS.STUDY MEASUREMENTS.EFFICACY:\nThe primary endpoint was change from baseline HbA1c after 26 weeks of treatment. Secondary endpoints included: percentages of subjects reaching HbA1c (< 7.0%, ≤ 6.5%), FPG (5.0 to ≤ 7.2 mmol/l) and postprandial plasma glucose (PPG; 10.0 mmol/l) targets [11–13]; changes in body weight, FPG, mean PPG, indices of pancreatic B-cell function [pro-insulin : insulin ratio and homeostasis model assessment (HOMA)-B], HOMA-insulin resistance (HOMA-IR) and blood pressure (BP). HbA1c was measured centrally (MDS Pharma Services, King of Prussia, PA, USA) by high performance liquid chromatography while plasma glucose (PG) was self-measured using MediSense® glucose meters (Abbott Diagnostics Inc., Abbott Park, IL, USA). Insulin and C-peptide were measured by chemiluminescence, proinsulin by ELISA, while glucagon was measured in aprotinin-treated plasma by radioimmunoassay. The proinsulin : insulin ratio was calculated from fasting insulin and fasting proinsulin. HOMA-B and HOMA-IR were both calculated from FPG and fasting insulin. Samples measured centrally were collected and transported according to detailed procedures in the MDS Pharma Services manual. Samples stored at ambient temperature were shipped by courier to the central laboratory on the same day as collection, while frozen samples were shipped every 3 weeks.\n\nBODY.SUBJECTS AND METHODS.STUDY MEASUREMENTS.SAFETY:\nSafety variables included hypoglycaemic episodes based on PG levels (< 3.1 mmol/l), liraglutide antibodies including cross-reacting and neutralizing antibodies, tolerability (gastrointestinal complaints) and pulse. AEs, vital signs, electrocardiogram (ECG), biochemical and haematology measures including calcitonin were also monitored. Self-treated hypoglycaemic episodes were classified as minor, while those requiring third-party assistance were considered major. Serum antibodies against liraglutide were measured by radioimmunoprecipitation assay.\n\nBODY.SUBJECTS AND METHODS.STATISTICAL ANALYSES:\nAll efficacy and safety analyses were based on intent-to-treat criteria, defined as subjects who were exposed to ≥ 1 dose of trial product(s). Efficacy endpoints were analysed by ancova with treatment, country and previous glucose-lowering treatment as fixed effects and baseline values as covariates. Missing data were imputed by last observation carried forward (LOCF). Sample size calculations were based on predicted HbA1c and body weight after trial completion. As the three liraglutide + glimepiride groups were to be compared with both rosiglitazone + glimepiride and glimepiride monotherapy, two calculations were performed. These sample size calculations assumed a standard deviation of 1.2% of HbA1c, the non-inferiority/superiority margin vs. active control was set to 0.4% and the difference to detect (superiority vs. placebo) was set to 0.5%. For body weight, a coefficient of variation of 3% (based on phase 2a trials for liraglutide) and a difference to detect of 3% were assumed. A combined power (calculated as the product of the marginal powers for HbA1c and body weight) of at least 85% was required. These calculations indicated that at least 168 and 81 patients completing the study would be needed for the combination and glimepiride monotherapy groups, respectively. Assuming a drop-out rate of 25%, targets for randomization were 228 in each of the combination therapy groups and 114 in the placebo group (total n = 1026). To protect against Type 1 errors, HbA1c was analysed using hierarchical testing for descending doses of liraglutide. First, superiority of liraglutide 1.8 mg to placebo was tested and, only if superior to placebo, non-inferiority to rosiglitazone was tested. If non-inferiority was obtained, superiority to rosiglitazone for liraglutide 1.8 mg was tested and superiority to placebo for liraglutide 1.2 mg was tested. If superiority was confirmed, non-inferiority to rosiglitazone would be tested and so on (i.e. testing sequence was stopped when hypotheses could not be rejected). Superiority was concluded when upper limits of two-sided 95% confidence intervals (CIs) for treatment differences were below 0%; non-inferiority was concluded if these values were < 0.4%; for secondary endpoints, Type 1 errors were controlled by estimating simultaneous CIs using Dunnett's method. Proportions of subjects achieving HbA1c (HbA1c < 7.0%, and ≤ 6.5%) and FPG (5.0 ≤ FPG ≤ 7.2 mmol/l) targets [13] were compared between treatments using logistic regression with allocated treatment and baseline values as covariates. Chi-square analyses assessed differences in treatments for percentages of subjects achieving no, one, two or three PPG values < 10 mmol/l [13]. Hypoglycaemic episodes were analysed under the assumption that number per subject were negatively binomially distributed using a generalized linear model, including treatment and country as fixed effects. Other safety data were compared by descriptive statistics. Values for descriptive statistics are expressed as means ± sd, while ancova results are expressed as least square means ± SEM or with 95% CI unless otherwise noted. Significance levels were set to 5% for two-sided tests and 2.5% for one-sided tests.\n\nBODY.RESULTS.DISPOSITION AND DEMOGRAPHICS:\nThe treatment groups were well balanced (Table 1). Of 1712 subjects screened, 1041 were randomized and 1040 were exposed to trial drugs; 147 subjects (14.1%) withdrew (Fig. 2). Withdrawals were higher with placebo (27%) and rosiglitazone treatment (16%) compared with liraglutide 0.6 mg (11%), liraglutide 1.2 mg (14%) and liraglutide 1.8 mg (9%) treatment. Thirty-eight subjects (3.7%) withdrew as a result of AEs (Fig. 2). Table 1 Demographic characteristics of study participants Liraglutide 0.6 mg ( n = 233) Liraglutide 1.2 mg ( n = 228) Liraglutide 1.8 mg ( n = 234) Placebo ( n = 114) Rosiglitazone ( n = 232) Male : female (%) 54 : 46 45 : 55 53 : 47 47 : 53 47 : 53 Age (years) 55.7 ± 9.9 57.7 ± 9.0 55.6 ± 10.0 54.7 ± 10.0 56.0 ± 9.8 Duration of diabetes (years) 6.5 (4.0,10.2) 6.7 (4.0,10.7) 6.5 (3.7,10.5) 6.5 (4.5,10.6) 6.6 (4.3,10.7) Previous on mono : combi (%) 30 : 70 31 : 69 27 : 73 32 : 68 32 : 68 FPG (mmol/l) 10.0 ± 2.4 9.8 ± 2.7 9.7 ± 2.4 9.5 ± 2.0 9.9 ± 2.5 HbA 1c (%) 8.4 ± 1.0 8.5 ± 1.1 8.5 ± 0.9 8.4 ± 1.0 8.4 ± 1.0 Diabetic retinopathy (%) 17.2 14.9 12.0 13.2 16.4 Hypertension (%) 69.1 68.0 69.7 64.9 66.8 BMI (kg/m 2 ) 30.0 ± 5.0 29.8 ± 5.1 30.0 ± 5.1 30.3 ± 5.4 29.4 ± 4.8 Weight (kg) 82.6 ± 17.7 80.0 ± 17.1 83.0 ± 18.1 81.9 ± 17.1 80.6 ± 17.0 Systolic blood pressure (mmHg) 131 ± 16 133 ± 15 132 ± 16 131 ± 15.3 133 ± 15 Data are mean ± sd and percentages, except for duration of diabetes, where data are median, 25th and 75th percentile. BMI, body mass index; FPG, fasting plasma glucose; HbA 1c , glycated haemoglobin; mono : combi, previous treatment with either monotherapy or combination therapy; sd , standard deviation. FIGURE 2Flow of patients through the study.\n\nBODY.RESULTS.EFFICACY.HBA:\nHbA1c decreased rapidly with all doses of liraglutide when added to glimepiride compared with either rosiglitazone or placebo (i.e. glimepiride monotherapy), irrespective of previous therapy. The greatest decreases occurred with liraglutide 1.2 and 1.8 mg (Fig. 3a–c). After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone. Rosiglitazone also was superior to placebo (P < 0.0001). FIGURE 3Mean glycated haemoglobin (HbA1c) by treatment and week (intent-to-treat population with last observation carried forward): (a) overall population; (b) previously on monotherapy; or (c) previously on combination therapy; (d) mean changes in HbA1c from baseline after 26 weeks of treatment. Keys: (a–c) liraglutide 0.6 mg: grey dotted line with squares; liraglutide 1.2 mg: black solid line with triangles; liraglutide 1.8 mg: black dotted line with squares; rosiglitazone: grey solid line with circles; placebo: black solid line with circles. (d) liraglutide 0.6 mg: black stripes on white; liraglutide 1.2 mg: white stripes on black, liraglutide 1.8 mg: grey tint; rosiglitazone: white; placebo: black. ****P < 0.0001 compared with placebo; ††††P < 0.0001 compared with rosiglitazone. HbA1c decreases were greater for subjects who entered from monotherapy compared with combination therapy (Fig. 3d). However, because the increase with placebo was higher for individuals entering on combination therapy (0.7 vs. 0.23%), the differences between treatment groups in favour of liraglutide were similar irrespective of whether subjects were treated previously with monotherapy or combination therapy. Neither age, gender nor BMI affected these trends.\n\nBODY.RESULTS.EFFICACY.PERCENTAGE REACHING AN HBA:\nThe percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). FIGURE 4Subjects achieving specified glycated haemoglobin (HbA1c) levels: (a) percentage reaching HbA1c < 7.0% (American Diabetes Association/European Association for the Study of Diabetes target); (b) percentage reaching HbA1c < 6.5% (International Diabetes Federation/American Association of Clinical Endocrinologists targets); (c) cumulative distribution of HbA1c at 26 weeks for the intent-to-treat (ITT) population; and (d) for the ITT last observation carried forward (LOCF) population. Keys: (a, b) liraglutide 0.6 mg: black stripes on white; liraglutide 1.2 mg: white stripes on black, liraglutide 1.8 mg: grey tint; rosiglitazone: white; placebo: black. (c, d) liraglutide 0.6 mg: pale grey solid line; liraglutide 1.2 mg: grey solid line, liraglutide 1.8 mg: black solid line; rosiglitazone: dotted black line; placebo: dotted grey line; baseline visit: long dashed black line. ****P < 0.0001 or **P < 0.01 compared with placebo; ††††P < 0.0001 or †††P = 0.0005 compared with rosiglitazone.\n\nBODY.RESULTS.EFFICACY.FASTING PLASMA GLUCOSE:\nBy week 2, subjects treated with liraglutide had rapid and larger decreases in FPG vs. comparator treatment. At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone. FPG treatment differences to placebo were 1.7 mmol/l for liraglutide 0.6 mg and 2.6 mmol/l for both liraglutide 1.2 and 1.8 mg. An 0.7-mmol/l greater reduction in FPG was achieved with either liraglutide 1.2 or 1.8 mg compared with rosiglitazone (P ≤ 0.006) after 26 weeks. FIGURE 5Mean changes from baseline in fasting plasma glucose after 26 weeks of treatment. ****P < 0.0001 compared with placebo; ††P < 0.01 compared with rosiglitazone. The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).\n\nBODY.RESULTS.EFFICACY.POSTPRANDIAL PLASMA GLUCOSE:\nPPG was reduced similarly after each meal. The greatest reductions in mean PPG values from baseline (average of values obtained 90 min after breakfast, lunch and evening meal) occurred with liraglutide 1.2 mg (2.5 mmol/l) and liraglutide 1.8 mg (2.7 mmol/l). By comparison, the reduction from baseline in mean PPG values was 1.8 mmol/l for rosiglitazone and liraglutide 0.6 mg and 0.4 mmol/l for placebo. Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.\n\nBODY.RESULTS.EFFICACY.PPG MEASUREMENTS < 10.0 MMOL/L:\nThe percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.\n\nBODY.RESULTS.BODY WEIGHT:\nMean weight at baseline was 81.6 kg. Mean reductions in weight from baseline to end of treatment were 0.2 kg with liraglutide 1.8 mg and 0.1 kg with placebo treatment, while increases occurred with either liraglutide 0.6 mg (0.7 kg), liraglutide 1.2 mg (0.3 kg) or rosiglitazone (2.1 kg) (Fig. 6). Unlike rosiglitazone, weight did not increase substantially with liraglutide and the differences between rosiglitazone and liraglutide were statistically significant (−2.3 to −1.4 kg; P < 0.0001), although there were no significant differences compared with placebo. Gender appeared to have no influence on the results, as indicated when added as a fixed effect in the ancova model. FIGURE 6Mean changes in body weight from baseline after 26 weeks of treatment. *P < 0.05 compared with placebo; ††††P < 0.0001 compared with rosiglitazone.\n\nBODY.RESULTS.INDICES OF PANCREATIC B-CELL FUNCTION AND INSULIN RESISTANCE:\nReductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051). There were no significant differences between treatments for HOMA-IR. Table 2 Selected indices of pancreatic B-cell function Variable Treatment Baseline Week 26 (LOCF) Least square difference from placebo (95% CI) Least square difference from rosiglitazone (95% CI) Proinsulin : insulin ratio Liraglutide 0.6 mg 0.42 ± 0.22 0.38 ± 0.24 −0.05 (−0.11; 0.00) −0.02 (−0.06; 0.03) Liraglutide 1.2 mg 0.45 ± 0.31 0.33 ± 0.20 −0.10 (−0.16; −0.05) † −0.07 (−0.11; −0.02) * Liraglutide 1.8 mg 0.48 ± 0.33 0.36 ± 0.20 −0.09 (−0.15; −0.03) * −0.05 (−0.10; −0.01) * Placebo 0.44 ± 0.27 0.46 ± 0.29 Rosiglitazone 0.45 ± 0.29 0.40 ± 0.20 HOMA-B (%) Liraglutide 0.6 mg 51 ± 43.3 70 ± 88.6 15 (−19.10; 49.0) 11 (−16.7; 39.0) Liraglutide 1.2 mg 71 ± 254.3 99 ± 184.3 43 (8.10; 76.9) * 39 (10.3; 67.0) * Liraglutide 1.8 mg 56 ± 84.6 91 ± 108.2 34 (−0.23; 68.5) 30 (2.00; 58.6) * Placebo 56 ± 103.3 52 ± 107.3 Rosiglitazone 46 ± 36.2 59 ± 63.3 * P ≤ 0.05; † P < 0.0001. CI, confidence interval; HOMA, homeostatis model assessment; LOCF, last observation carried forward. \n\nBODY.RESULTS.BLOOD PRESSURE AND PULSE:\nAlthough decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. Pulse increases above baseline ranged from 2 to 4 beats/min with the three doses of liraglutide and 1 beat/min with rosiglitazone, while pulse decreased by 1 beat/min with placebo. Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).\n\nBODY.RESULTS.SAFETY:\nThe most common treatment-emergent AEs that were considered by investigators to be either possibly or probably related to liraglutide were gastrointestinal (diarrhoea, nausea, dyspepsia and constipation) and nervous system disorders (headache and dizziness), particularly during the first 4 weeks. Nausea was highest with liraglutide 1.2 mg (10.5%) and lowest with placebo (1.8%). Vomiting (4.4%) and diarrhoea (7.9%) were also higher with liraglutide 1.2 mg. Withdrawals because of nausea ranged from 0.9–2.2%, vomiting 0.4–0.9% and diarrhoea 0–1.3%. Nausea was more common with liraglutide compared with placebo and rosiglitazone, particularly during the first 4 weeks (Fig. 7). Frequency of nausea was less in the liraglutide 0.6 mg treatment group compared with the higher doses of liraglutide. Generally, the occurrence of nausea dissipated from 4 to 26 weeks of treatment in all groups using liraglutide (Fig. 7). FIGURE 7Percentage of subjects experiencing nausea over the course of the study. Key: liraglutide 0.6 mg with glimepiride: black line with filled circles; liraglutide 1.2 mg with glimepiride: black line with filled triangles; liraglutide 1.8 mg with glimepiride: grey line with hollow circles; glimepiride grey lines with filled squares; rosiglitazone and glimepiride: grey line with hollow triangles. The incidence of serious AEs ranged between 3 and 5%: placebo (3%), rosiglitazone (3%), liraglutide 0.6 mg (3%), liraglutide 1.2 mg (4%) and liraglutide 1.8 mg (5%). Most treatment-emergent serious AEs were judged by investigators to be unlikely to be related to trial products. No deaths were reported during the trial. One subject developed chronic pancreatitis whilst taking liraglutide 0.6 mg; the person had no reported previous history of pancreatitis. The subject continued on liraglutide therapy and completed the trial. At screening, five patients had been previously diagnosed with pancreatitis. As pancreatitis was not an exclusion criterion, these patients were randomized as follows: one to liraglutide 0.6 mg, one to liraglutide 1.2 mg, two to liraglutide 1.8 mg and one to rosiglitazone + glimepiride. All five patients completed the trial without reporting pancreatitis as an adverse event. Hypoglycaemia was infrequent with all treatments. One major hypoglycaemic episode (self-measured blood glucose = 3.0 mmol/l) occurred 9 days after treatment started in a subject receiving liraglutide 1.8 mg in combination with glimepiride. Although medical assistance was not needed, the subject required third-party assistance. The investigator judged the episode as likely to be related to glimepiride and reduced the dose from 4 to 3 mg after the incident. Minor hypoglycaemia occurred in < 10% of subjects for any treatment. The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values. Antibodies to liraglutide were found in 9–13% of subjects treated with liraglutide. No significant effects of these antibodies on HbA1c were found in pooled analyses of four trials including the current study. There were no clinically relevant changes in ophthalmoscopy, biochemistry, urinalysis, haematology or ECG assessments. No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.\n\nBODY.DISCUSSION:\nTreatment with liraglutide plus glimepiride was superior to glimepiride monotherapy at all doses of liraglutide and superior to rosiglitazone plus glimepiride for the two higher liraglutide doses for improving HbA1c. Similar findings for reductions in FPG and PPG highlight improved 24-h glucose control with once-daily liraglutide, with substantially more subjects reaching glycaemic targets, particularly with liraglutide 1.8 mg. Improvements in pancreatic B-cell function were larger with liraglutide 1.2 and 1.8 mg compared with rosiglitazone. Liraglutide was well tolerated and occurrence of gastrointestinal AEs was low overall, particularly after week 4. Although rates of hypoglycaemia were low in all treatment groups (< 10%), minor hypoglycaemic events occurred more often in patients treated with glimepiride plus liraglutide 1.2 or 1.8 mg than with glimepiride alone. It should be noted, however, that patients treated with liraglutide 1.2 or 1.8 mg achieved a lower HbA1c than those receiving glimepiride monotherapy. At lower HbA1c levels, sulphonylureas are known to elicit hypoglycaemia more readily than at higher levels. In clinical practice it may be possible to reduce the dose of sulphonylurea (when used with liraglutide) to minimize risk of hypoglycaemia and maintain HbA1cimprovements. Although weight effects were modest, liraglutide produced more favourable weight effects compared with rosiglitazone, which produced substantial weight gain. In other studies with liraglutide, subjects adding a 1.8-mg dose to metformin lost 2.8 kg [14], while those adding both metformin and glimepiride lost 1.8 kg compared with placebo [15] (both over 26 weeks) and those on liraglutide monotherapy (1.8 mg) lost 2.45 kg over 52 weeks [16]. In our study, because sulphonylureas usually cause weight gain, inclusion or optimization of glimepiride but not metformin may have mitigated the weight benefits typically associated with liraglutide. Lack of weight effects could be secondary to lower baseline body weight, withdrawal of previous metformin treatment or defensive snacking to minimize risk of hypoglycaemia. It might have been expected that the greater weight gain with rosiglitazone compared with liraglutide 1.8 mg would be associated with a concurrent increase in insulin resistance with rosiglitazone. The absence of this effect could reflect the insulin-sensitizing nature of rosiglitazone. Improvements in pancreatic B-cell function associated with liraglutide are consistent with other studies [7–9]. Study strengths include inclusion of both placebo and active (rosiglitazone) comparators and that OGLAs were optimized (not maximized) before randomization to minimize risk of hypoglycaemia. Limitations of the study include short duration of the trial and restriction on glimepiride and rosiglitazone in some countries that precluded maximal dosing. The impact of using other GLP-1-based treatments [such as exenatide, or the dipeptidyl peptidase-4 (DPP-4) inhibitor, sitagliptin] with sulphonylureas in subjects with T2D has been studied. In a 30-week American trial where exenatide twice a day was added to sulphonylureas, HbA1c was reduced by 0.46% from baseline with 5 μg and 0.86% with 10 μg [17] compared with 1.1% with liraglutide 1.8 or 1.2 mg. This reduction in HbA1c with liraglutide is consistent with other LEAD trials investigating liraglutide as monotherapy or in combination with various OGLA drugs. In these trials, HbA1c was reduced by 1–1.5%[14,16,18–20]. Reductions in FPG with exenatide were 0.3 and 0.6 mmol/l from baseline with 5 μg and 10 μg, respectively, compared with 1.4 mmol/l with liraglutide 1.8 mg; weight loss of 1.6 kg occurred with exenatide 10 μg compared with 0.2 kg for liraglutide 1.8 mg [17]. Differences in weight effects may be as a result of lower baseline weight in this trial (82 kg) compared with exenatide (96 kg) and discontinuation of previous metformin therapy, unlike the exenatide trial where exenatide was added to previous sulphonylurea monotherapy [17]. Other large-scale trials with liraglutide in combination with sulphonylureas have demonstrated weight loss of 2–3 kg [18,20]. Withdrawals from exenatide trials ranged from 24–30% compared with 9–14% with liraglutide in this study. Nausea with exenatide ranged from 39% with 5 μg to 51% with 10 μg [17] compared with 10.5% for liraglutide. Furthermore, 41% were positive for anti-exenatide antibodies compared with 9–13% with anti-liraglutide antibodies. With sitagliptin 100 mg once daily for 24 weeks, HbA1c decreased by 0.3% from baseline in subjects receiving glimepiride, with 11% achieving an HbA1c < 7.0%[21]. Reductions in FPG and PPG from baseline were 0.05 and 1.4 mmol/l, respectively, while weight increased by 0.8 kg and the prevalence of nausea was < 1%. Although head-to-head trials are required to test true differences between these agents, the marked effects of liraglutide on FPG may be as a result of consistent blood levels of liraglutide maintained over 24 h compared with exenatide which has to be administered 60 min before breakfast and dinner and has a half-life of 1.5–3.6 h [22]. In a recent 26-week head-to-head trial comparing liraglutide with exenatide, liraglutide produced a 0.3% greater decrease on HbA1c (P < 0.0001) [20]. Because DPP-4 inhibitors inhibit the degradation of GLP-1, the efficacy of sitagliptin is dependent on levels of endogenous GLP-1 which is physiologically low compared with the much higher pharmacological levels of liraglutide. Pharmacological levels may be needed to induce satiety, weight loss and possibly larger HbA1c reductions. Liraglutide is an effective and well-tolerated once-daily human GLP-1 analogue that improves overall glycaemic control and indices of pancreatic B-cell function with minimal weight gain and risk of hypoglycaemia when used in combination with a sulphonylurea for T2D.\n\nBODY.COMPETING INTERESTS:\nThe study was funded by Novo Nordisk, the manufacturer of liraglutide. In collaboration with the investigators, Novo Nordisk was responsible for the study design, protocol, statistical analysis plans, oversight, analysis and reporting of the results. Data were recorded at the clinical centres and maintained by the sponsor. The LEAD-1 SU study group had full access to the data. Final responsibility for the decision to submit the manuscript for publication was the authors. MM has received lecture fees from Novo Nordisk, Servier, MSD; JS has received honoraria, grants and lecture fees from Novo Nordisk; MB, WMWB and NAK have no conflicts to declare; JS has received lecture fees from Novo Nordisk; MZ is employed by, and holds stock in, Novo Nordisk; TLT is employed by Novo Nordisk; SC is a member of the international advisory board on liraglutide for Novo Nordisk and has received lecture fees from Novo Nordisk.",
'PMCID': 2871176,
'Prompts': {'PromptID': [150,
113,
140,
106,
142,
149,
148,
152,
154,
125,
121,
124,
107,
105,
133,
103,
126,
118,
132,
122,
141,
151,
112,
153,
102,
129,
104,
116,
136,
123,
135,
139,
101,
99,
144,
145,
147,
117,
143,
111,
137,
114,
108,
128,
134,
115,
127,
131,
109,
146,
110,
100,
138,
119,
130],
'PMCID': [2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176,
2871176],
'Outcome': ['Incidence of minor hypoglycaemia',
'Patients reaching HbA1c goals less than 7.0% and equal or less than 6.5%',
'HOMA-IR',
'HbA1c level at 26 weeks',
'Reductions in systolic blood pressure',
'Pulse variations',
'Pulse variations',
'Incidence of minor hypoglycaemia',
'Changes in calcitonin at week 26',
'Postprandial plasma glucose',
'ADA fasting plasma glucose goals between 5.0 mmol/l and less than 7.2 mmol/l',
'Postprandial plasma glucose',
'HbA1c level at 26 weeks',
'HbA1c level at 26 weeks',
'Proinsulin : insulin ratio',
'Postprandial plasma glucose',
'ADA postprandial plasma glucose goals less than 10.0 mmol/l',
'ADA fasting plasma glucose goals between 5.0 mmol/l and less than 7.2 mmol/l',
'Proinsulin : insulin ratio',
'ADA fasting plasma glucose goals between 5.0 mmol/l and less than 7.2 mmol/l',
'Reductions in systolic blood pressure',
'Incidence of minor hypoglycaemia',
'Patients reaching HbA1c goals less than 7.0% and equal or less than 6.5%',
'Changes in calcitonin at week 26',
'Fasting plasma glucose at week 26',
'ADA postprandial plasma glucose goals less than 10.0 mmol/l',
'Postprandial plasma glucose',
'Fasting plasma glucose at week 26',
'HOMA-B',
'Postprandial plasma glucose',
'HOMA-B',
'HOMA-IR',
'Fasting plasma glucose at week 26',
'HbA1c level at 26 weeks',
'Reductions in systolic blood pressure',
'Decreases in diastolic blood pressure',
'Pulse variations',
'Fasting plasma glucose at week 26',
'Reductions in systolic blood pressure',
'Patients reaching HbA1c goals less than 7.0% and equal or less than 6.5%',
'HOMA-B',
'Patients reaching HbA1c goals less than 7.0% ',
'HbA1c level at 26 weeks',
'ADA postprandial plasma glucose goals less than 10.0 mmol/l',
'Proinsulin : insulin ratio',
'Fasting plasma glucose at week 26',
'ADA postprandial plasma glucose goals less than 10.0 mmol/l',
'Proinsulin : insulin ratio',
'HbA1c level at 26 weeks',
'Decreases in diastolic blood pressure',
'Patients reaching HbA1c goals less than 7.0% and equal or less than 6.5%',
'HbA1c level at 26 weeks',
'HOMA-B',
'ADA fasting plasma glucose goals between 5.0 mmol/l and less than 7.2 mmol/l',
'Weight gain'],
'Intervention': ['Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (all doses) plus glimepiride',
'Liraglutide (0.6 mg) plus glimepiride',
'Liraglutide (1.8 mg) plus glimepiride ',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (all doses) plus glimepiride',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (0.6 mg) plus glimepiride',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (1.8 mg) plus glimepiride ',
'Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (0.6 mg) plus glimepiride',
'Liraglutide (0.6 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride ',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride ',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (all doses) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (all doses) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (0.6 mg) plus glimepiride',
'Liraglutide (1.8 mg) plus glimepiride ',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride ',
'Liraglutide (all doses) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (1.8 mg) plus glimepiride ',
'Liraglutide (all doses) plus glimepiride',
'Liraglutide (all doses) plus glimepiride',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride ',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride ',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (0.6 mg) plus glimepiride',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (1.8 mg) plus glimepiride ',
'Liraglutide (1.8 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride ',
'Rosiglitazone plus glimepiride',
'Liraglutide (all doses) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (1.2 mg) plus glimepiride',
'Liraglutide (1.8 mg) plus glimepiride ',
'Liraglutide (1.2 mg) plus glimepiride',
'Rosiglitazone plus glimepiride'],
'Comparator': ['Rosiglitazone plus glimepiride',
'Rosiglitazone plus glimepiride',
'Rosiglitazone plus glimepiride',
'Placebo plus glimepiride',
'Placebo plus glimepiride ',
'Rosiglitazone plus glimepiride',
'Rosiglitazone plus glimepiride',
'Placebo plus glimepiride',
'Placebo plus glimepiride',
'Rosiglitazone plus glimepiride',
'Rosiglitazone plus glimepiride',
'Placebo plus glimepiride',
'Rosiglitazone plus glimepiride',
'Placebo plus glimepiride',
'Rosiglitazone plus glimepiride ',
'Placebo plus glimepiride',
'Placebo plus glimepiride',
'Placebo plus glimepiride',
'Placebo plus glimepiride ',
'Rosiglitazone plus glimepiride',
'Placebo plus glimepiride ',
'Rosiglitazone plus glimepiride',
'Rosiglitazone plus glimepiride',
'Rosiglitazone plus glimepiride',
'Rosiglitazone plus glimepiride',
'Rosiglitazone plus glimepiride',
'Rosiglitazone plus glimepiride',
'Placebo plus glimepiride',
'Rosiglitazone plus glimepiride ',
'Placebo plus glimepiride',
'Rosiglitazone plus glimepiride ',
'Placebo plus glimepiride',
'Placebo plus glimepiride',
'Placebo plus glimepiride',
'Rosiglitazone plus glimepiride ',
'Placebo plus glimepiride',
'Placebo plus glimepiride',
'Rosiglitazone plus glimepiride',
'Rosiglitazone plus glimepiride ',
'Placebo plus glimepiride',
'Placebo plus glimepiride ',
'Liraglutide (1.2 mg) plus glimepiride',
'Rosiglitazone plus glimepiride',
'Placebo plus glimepiride',
'Placebo plus glimepiride ',
'Placebo plus glimepiride',
'Placebo plus glimepiride',
'Rosiglitazone plus glimepiride ',
'Placebo plus glimepiride',
'Rosiglitazone plus glimepiride',
'Placebo plus glimepiride',
'Rosiglitazone plus glimepiride',
'Placebo plus glimepiride ',
'Placebo plus glimepiride',
'Liraglutide plus glimepiride'],
'Annotations': [{'UserID': [0, 3, 2],
'PromptID': [150, 150, 150],
'PMCID': [2871176, 2871176, 2871176],
'Valid Label': [True, True, True],
'Valid Reasoning': [True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.',
'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone',
'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.'],
'Label Code': [1, 1, 1],
'In Abstract': [True, True, True],
'Evidence Start': [25524, 25964, 25964],
'Evidence End': [26184, 26073, 26184]},
{'UserID': [0, 1, 3, 2],
'PromptID': [113, 113, 113, 113],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003)',
'he estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ',
'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), ',
'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [16120, 16121, 16120, 16120],
'Evidence End': [16353, 16449, 16355, 16449]},
{'UserID': [0, 1, 3, 2],
'PromptID': [140, 140, 140, 140],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['There were no significant differences between treatments for HOMA-IR.',
'There were no significant differences between treatments for HOMA-IR.',
'There were no significant differences between treatments for HOMA-IR.',
'There were no significant differences between treatments for HOMA-IR.'],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [20943, 20943, 20943, 20943],
'Evidence End': [21012, 21012, 21012, 21012]},
{'UserID': [0, 1, 3, 2],
'PromptID': [106, 106, 106, 106],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['All liraglutide doses were superior to placebo (P < 0.0001)',
'Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). ',
'All liraglutide doses were superior to placebo (P < 0.0001),',
'All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001).'],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [14169, 13955, 14169, 14169],
'Evidence End': [14228, 14314, 14229, 14313]},
{'UserID': [0, 1, 3, 2],
'PromptID': [142, 142, 142, 142],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg)',
'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ',
'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg)',
'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [22039, 22039, 22039, 22039],
'Evidence End': [22230, 22232, 22230, 22232]},
{'UserID': [0, 1, 3, 2],
'PromptID': [149, 149, 149, 149],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).',
'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).',
'Pulse increases above baseline ranged from 2 to 4 beats/min with the three doses of liraglutide and 1 beat/min with rosiglitazone, while pulse decreased by 1 beat/min with placebo. Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002)',
'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).'],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [22554, 22554, 22373, 22554],
'Evidence End': [22738, 22738, 22640, 22738]},
{'UserID': [0, 1, 3, 2],
'PromptID': [148, 148, 148, 148],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).',
'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002)',
'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).',
'Pulse increases above baseline ranged from 2 to 4 beats/min with the three doses of liraglutide and 1 beat/min with rosiglitazone, while pulse decreased by 1 beat/min with placebo. Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).'],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [22554, 22554, 22554, 22373],
'Evidence End': [22738, 22640, 22738, 22738]},
{'UserID': [0, 1, 3, 2],
'PromptID': [152, 152, 152, 152],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.',
'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.',
'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048),',
'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.'],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [25524, 25964, 25964, 25964],
'Evidence End': [26184, 26184, 26131, 26184]},
{'UserID': [0, 1, 3, 2],
'PromptID': [154, 154, 154, 154],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.',
'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.',
'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.',
'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.'],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [26515, 26515, 26515, 26515],
'Evidence End': [26703, 26703, 26703, 26703]},
{'UserID': [0, 1, 3, 2],
'PromptID': [125, 125, 125, 125],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.',
'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ',
'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ',
'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [19128, 1469, 1469, 1469],
'Evidence End': [19377, 1756, 1756, 1756]},
{'UserID': [0, 3],
'PromptID': [121, 121],
'PMCID': [2871176, 2871176],
'Valid Label': [True, True],
'Valid Reasoning': [True, True],
'Label': ['significantly increased', 'significantly increased'],
'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).',
'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). '],
'Label Code': [1, 1],
'In Abstract': [True, True],
'Evidence Start': [18230, 18230],
'Evidence End': [18670, 18476]},
{'UserID': [0, 1, 3, 2],
'PromptID': [124, 124, 124, 124],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001)',
'reatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.',
'Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) ',
'Treatment differences for PPG were greater with all doses of liraglutide compared with placebo (1.5–2.4 mmol/l; P < 0.0001) and greater with liraglutide 1.2 mg (0.64 mmol/l; P = 0.043) and 1.8 mg (0.87 mmol/l;P = 0.0022) compared with rosiglitazone.'],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [19128, 19129, 19128, 19128],
'Evidence End': [19251, 19377, 19252, 19377]},
{'UserID': [0, 1, 3, 2],
'PromptID': [107, 107, 107, 107],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride.',
'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ',
'Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride. ',
'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone. Rosiglitazone also was superior to placebo (P < 0.0001). '],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [843, 13756, 843, 13756],
'Evidence End': [1081, 13955, 1082, 14426]},
{'UserID': [0, 1, 3, 2],
'PromptID': [105, 105, 105, 105],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) or rosiglitazone (−0.4%, P < 0.0001, baseline 8.4%) when added to glimepiride.',
'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ',
'All liraglutide doses were superior to placebo (P < 0.0001),',
'All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001).'],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [843, 13756, 14169, 14169],
'Evidence End': [1081, 13955, 14229, 14313]},
{'UserID': [0, 1, 3, 2],
'PromptID': [133, 133, 133, 133],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)',
'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ',
'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)',
'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). '],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [20566, 20566, 20566, 20566],
'Evidence End': [20726, 20728, 20726, 20728]},
{'UserID': [0, 1, 3, 2],
'PromptID': [103, 103, 103, 103],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l)',
'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ',
'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) ',
'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [1469, 1469, 1469, 1469],
'Evidence End': [1691, 1756, 1692, 1756]},
{'UserID': [0, 1, 3, 2],
'PromptID': [126, 126, 126, 126],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone',
'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.',
'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05)',
'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.'],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [19433, 19433, 19433, 19433],
'Evidence End': [19623, 19624, 19601, 19624]},
{'UserID': [0, 1, 3, 2],
'PromptID': [118, 118, 118, 118],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%).',
'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). ',
'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%)',
'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). '],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [18230, 18230, 18230, 18230],
'Evidence End': [18475, 18476, 18474, 18476]},
{'UserID': [0, 1, 2],
'PromptID': [132, 132, 132],
'PMCID': [2871176, 2871176, 2871176],
'Valid Label': [True, True, True],
'Valid Reasoning': [True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)',
'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ',
'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). '],
'Label Code': [-1, -1, -1],
'In Abstract': [True, True, True],
'Evidence Start': [20566, 20566, 20566],
'Evidence End': [20726, 20728, 20728]},
{'UserID': [0, 1, 1, 2],
'PromptID': [122, 122, 122, 122],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).',
'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). ',
'The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).',
'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). The liraglutide 1.2 and 1.8 mg treatment groups also had more subjects achieving the same FPG target at end of treatment compared with rosiglitazone (26%) (P = 0.007 and P = 0.01, respectively).'],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [18230, 18230, 18476, 18230],
'Evidence End': [18670, 18476, 18670, 18670]},
{'UserID': [0, 1, 3, 2],
'PromptID': [141, 141, 141, 141],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg)',
'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ',
'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo ',
'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [22039, 22039, 22039, 22039],
'Evidence End': [22230, 22232, 22199, 22232]},
{'UserID': [0, 1, 3, 2],
'PromptID': [151, 151, 151, 151],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['The proportion of subjects experiencing minor hypoglycaemia during the trial was lowest with placebo (i.e. glimepiride monotherapy 2.6%; 0.17 events/subject-year), comparable with liraglutide 0.6 mg (5.2%, 0.17 events/subject-year) and rosiglitazone (4.3%, 0.12 events/subject-year) groups and similar between the liraglutide 1.2 mg (9.2%, 0.51 events/subject-year) and liraglutide 1.8 mg (8.1%, 0.47 events/subject-year) treatment groups. Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.',
'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.',
'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone',
'Incidence was higher with liraglutide 1.2 mg (P = 0.0024) and 1.8 mg (P = 0.0065) compared with rosiglitazone and liraglutide 1.2 mg compared with placebo (P = 0.048), occurring in the setting of lower mean HbA1c values.'],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [25524, 25964, 25964, 25964],
'Evidence End': [26184, 26184, 26073, 26184]},
{'UserID': [0, 1, 3, 2],
'PromptID': [112, 112, 112, 112],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003)',
'At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ',
'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ',
'The percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [16120, 15956, 16120, 15735],
'Evidence End': [16353, 16449, 16449, 16449]},
{'UserID': [0, 1, 3, 2],
'PromptID': [153, 153, 153, 153],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.',
'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.',
'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.',
'No significant differences in calcitonin were found between the three groups treated with liraglutide when compared with either placebo or rosiglitazone at the end of the trial at week 26.'],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [26515, 26515, 26515, 26515],
'Evidence End': [26703, 26703, 26703, 26703]},
{'UserID': [0, 1, 3, 2],
'PromptID': [102, 102, 102, 102],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).',
'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).',
'An 0.7-mmol/l greater reduction in FPG was achieved with either liraglutide 1.2 or 1.8 mg compared with rosiglitazone (P ≤ 0.006) after 26 weeks. ',
'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).'],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [1144, 1144, 17914, 1144],
'Evidence End': [1468, 1468, 18061, 1468]},
{'UserID': [0, 1, 3, 2],
'PromptID': [129, 129, 129, 129],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.',
'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.',
'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.',
'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.'],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [19433, 19433, 19433, 19433],
'Evidence End': [19624, 19624, 19624, 19624]},
{'UserID': [1, 2],
'PromptID': [104, 104],
'PMCID': [2871176, 2871176],
'Valid Label': [True, True],
'Valid Reasoning': [True, True],
'Label': ['significantly decreased', 'significantly decreased'],
'Annotations': ['Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ',
'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '],
'Label Code': [-1, -1],
'In Abstract': [True, True],
'Evidence Start': [1469, 1469],
'Evidence End': [1756, 1756]},
{'UserID': [0, 1, 3, 2],
'PromptID': [116, 116, 116, 116],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001)',
'By week 2, subjects treated with liraglutide had rapid and larger decreases in FPG vs. comparator treatment. At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone. FPG treatment differences to placebo were 1.7 mmol/l for liraglutide 0.6 mg and 2.6 mmol/l for both liraglutide 1.2 and 1.8 mg.',
'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001),',
'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone.'],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [17606, 17497, 17606, 17606],
'Evidence End': [17699, 17913, 17700, 17785]},
{'UserID': [0, 1, 3, 2],
'PromptID': [136, 136, 136, 136],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05)',
'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).',
'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05),',
'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).'],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [20728, 20728, 20728, 20728],
'Evidence End': [20816, 20942, 20817, 20942]},
{'UserID': [0, 1, 3, 2],
'PromptID': [123, 123, 123, 123],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l)',
'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). ',
'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) ',
'Decreases in postprandial plasma glucose from baseline were greater with liraglutide 1.2 or 1.8 mg [−2.5 to −2.7 mmol/l (baseline 12.9 mmol/l for both)] compared with placebo (−0.4 mmol/l, P < 0.0001, baseline 12.7 mmol/l) or rosiglitazone (−1.8 mmol/l, P < 0.05, baseline 13.0 mmol/l). '],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [1469, 1469, 1469, 1469],
'Evidence End': [1691, 1756, 1692, 1756]},
{'UserID': [0, 1, 3, 2],
'PromptID': [135, 135, 135, 135],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05)',
'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).',
'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05),',
'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)'],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [20728, 20728, 20728, 20728],
'Evidence End': [20816, 20942, 20817, 20941]},
{'UserID': [0, 1, 3, 2],
'PromptID': [139, 139, 139, 139],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['There were no significant differences between treatments for HOMA-IR.',
'There were no significant differences between treatments for HOMA-IR.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable 2',
'There were no significant differences between treatments for HOMA-IR.',
'There were no significant differences between treatments for HOMA-IR.'],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [20943, -1, 20943, 20943],
'Evidence End': [21012, -1, 21012, 21012]},
{'UserID': [0, 1, 3, 2],
'PromptID': [101, 101, 101, 101],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l)',
'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).',
'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001)',
'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).'],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [1144, 1144, 17606, 1144],
'Evidence End': [1396, 1468, 17699, 1468]},
{'UserID': [0, 1, 3, 2],
'PromptID': [99, 99, 99, 99],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%)',
'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ',
'Liraglutide (1.2 or 1.8 mg) produced greater reductions in HbA1c from baseline, (−1.1%, baseline 8.5%) compared with placebo (+0.2%, P < 0.0001, baseline 8.4%) ',
'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001)'],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [843, 13756, 843, 13756],
'Evidence End': [1002, 13955, 1003, 14312]},
{'UserID': [0, 1, 3, 2],
'PromptID': [144, 144, 144, 144],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg).',
'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ',
'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ',
'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [22039, 22039, 22039, 22039],
'Evidence End': [22231, 22232, 22232, 22232]},
{'UserID': [0, 1, 3, 2],
'PromptID': [145, 145, 145, 145],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments.',
'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ',
'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ',
'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. '],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [22232, 22232, 22232, 22232],
'Evidence End': [22372, 22373, 22373, 22373]},
{'UserID': [0, 1, 2],
'PromptID': [147, 147, 147],
'PMCID': [2871176, 2871176, 2871176],
'Valid Label': [True, True, True],
'Valid Reasoning': [True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). This also was true with either liraglutide 1.8 or 1.2 mg compared with rosiglitazone (P < 0.01).',
'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). ',
'Changes in pulse for all doses of liraglutide were significant vs. placebo (P ≤ 0.002). '],
'Label Code': [1, 1, 1],
'In Abstract': [True, True, True],
'Evidence Start': [22554, 22554, 22554],
'Evidence End': [22738, 22642, 22642]},
{'UserID': [0, 1, 3, 2],
'PromptID': [117, 117, 117, 117],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).',
'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).',
'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).',
'By week 2, subjects treated with liraglutide had rapid and larger decreases in FPG vs. comparator treatment. At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001), while only liraglutide 1.2 or 1.8 mg produced greater reductions than rosiglitazone. FPG treatment differences to placebo were 1.7 mmol/l for liraglutide 0.6 mg and 2.6 mmol/l for both liraglutide 1.2 and 1.8 mg. An 0.7-mmol/l greater reduction in FPG was achieved with either liraglutide 1.2 or 1.8 mg compared with rosiglitazone (P ≤ 0.006) after 26 weeks. '],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [1144, 1144, 1144, 17497],
'Evidence End': [1468, 1468, 1468, 18061]},
{'UserID': [0, 1, 3, 2],
'PromptID': [143, 143, 143, 143],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg).',
'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ',
'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). ',
'Although decreases in systolic blood pressure occurred with either liraglutide 1.2 or 1.8 mg (2.6–2.8 mmHg), they were not significantly different from placebo or rosiglitazone (0.9–2.3 mmHg). '],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [22039, 22039, 22039, 22039],
'Evidence End': [22231, 22232, 22232, 22232]},
{'UserID': [0, 1, 3, 2],
'PromptID': [111, 111, 111, 111],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001)',
' The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). FIGURE 4',
'At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo ',
'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [16120, 16119, 15956, 16120],
'Evidence End': [16315, 16457, 16110, 16449]},
{'UserID': [0, 1, 3, 2],
'PromptID': [137, 137, 137, 137],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)',
'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).',
'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01)',
'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).'],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [20728, 20728, 20728, 20728],
'Evidence End': [20941, 20942, 20902, 20942]},
{'UserID': [0, 1],
'PromptID': [114, 114],
'PMCID': [2871176, 2871176],
'Valid Label': [True, True],
'Valid Reasoning': [True, True],
'Label': ['significantly increased', 'significantly increased'],
'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018).',
'At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '],
'Label Code': [1, 1],
'In Abstract': [True, True],
'Evidence Start': [16120, 15956],
'Evidence End': [16447, 16449]},
{'UserID': [0, 1, 3, 2],
'PromptID': [108, 108, 108, 108],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['Liraglutide 0.6 mg was non-inferior to rosiglitazone',
'All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone.',
'Liraglutide 0.6 mg was non-inferior to rosiglitazone',
'. All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone.'],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [14314, 14169, 14314, 14167],
'Evidence End': [14366, 14367, 14366, 14367]},
{'UserID': [0],
'PromptID': [128],
'PMCID': [2871176],
'Valid Label': [True],
'Valid Reasoning': [True],
'Label': ['significantly increased'],
'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone'],
'Label Code': [1],
'In Abstract': [True],
'Evidence Start': [19433],
'Evidence End': [19623]},
{'UserID': [0, 1, 2],
'PromptID': [134, 134, 134],
'PMCID': [2871176, 2871176, 2871176],
'Valid Label': [True, True, True],
'Valid Reasoning': [True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)',
'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ',
'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), '],
'Label Code': [-1, -1, -1],
'In Abstract': [True, True, True],
'Evidence Start': [20566, 20566, 20566],
'Evidence End': [20726, 20728, 20818]},
{'UserID': [0, 1, 3, 2],
'PromptID': [115, 115, 115, 115],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l)',
'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).',
'At week 26, all doses of liraglutide decreased FPG more than did placebo (Fig. 5; P < 0.0001)',
'Fasting plasma glucose decreased by week 2, with a 1.6 mmol/l decrease from baseline at week 26 with liraglutide 1.2 mg (baseline 9.8 mmol/l) or 1.8 mg (baseline 9.7 mmol/l) compared with a 0.9 mmol/l increase (placebo, P < 0.0001, baseline 9.5 mmol/l) or 1.0 mmol/l decrease (rosiglitazone, P < 0.006, baseline 9.9 mmol/l).'],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [1144, 1144, 17606, 1144],
'Evidence End': [1396, 1468, 17699, 1468]},
{'UserID': [0, 1, 2],
'PromptID': [127, 127, 127],
'PMCID': [2871176, 2871176, 2871176],
'Valid Label': [True, True, True],
'Valid Reasoning': [True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone',
'he percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.',
'The percentage of subjects with one, two or three PPG measurements < 10.0 mmol/l (ADA target) were greater for all doses of liraglutide compared with placebo (P < 0.05) but not rosiglitazone.'],
'Label Code': [1, 1, 1],
'In Abstract': [True, True, True],
'Evidence Start': [19433, 19434, 19433],
'Evidence End': [19623, 19624, 19624]},
{'UserID': [0, 1, 3, 2],
'PromptID': [131, 131, 131, 131],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)',
'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ',
'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02). ',
'Reductions in the proinsulin : insulin ratio were greater with both liraglutide 1.2 and 1.8 mg compared with either rosiglitazone or placebo (Table 2; P ≤ 0.02)'],
'Label Code': [-1, -1, -1, -1],
'In Abstract': [True, True, True, True],
'Evidence Start': [20566, 20566, 20566, 20566],
'Evidence End': [20726, 20728, 20728, 20726]},
{'UserID': [0, 1, 1, 3, 2],
'PromptID': [109, 109, 109, 109, 109],
'PMCID': [2871176, 2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True, True],
'Valid Reasoning': [True, True, True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['Rosiglitazone also was superior to placebo (P < 0.0001)',
'Rosiglitazone also was superior to placebo (P < 0.0001).',
' The greatest decreases occurred with liraglutide 1.2 and 1.8 mg (Fig. 3a–c). After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). Liraglutide 0.6 mg was non-inferior to rosiglitazone. ',
'Rosiglitazone also was superior to placebo (P < 0.0001).',
'Rosiglitazone also was superior to placebo (P < 0.0001).'],
'Label Code': [-1, -1, -1, -1, -1],
'In Abstract': [True, True, True, True, True],
'Evidence Start': [14368, 14368, 13678, 14368, 14368],
'Evidence End': [14423, 14424, 14368, 14424, 14424]},
{'UserID': [0, 1, 3, 2],
'PromptID': [146, 146, 146, 146],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments.',
'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ',
'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. ',
'Reductions in diastolic blood pressure also occurred with all treatments (0.7–1.4 mmHg), with no significant differences between treatments. '],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [22232, 22232, 22232, 22232],
'Evidence End': [22372, 22373, 22373, 22373]},
{'UserID': [0, 1, 3, 2],
'PromptID': [110, 110, 110, 110],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001)',
'The percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ',
'The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). ',
'The percentage of subjects reaching ADA [2] and International Diabetes Federation (IDF)/American Association of Clinical Endocrinologists (AACE) [11,12] treatment HbA1c goals with liraglutide was dose dependent (Fig. 4). At week 26, 42% and 21% of subjects treated with liraglutide 1.8 mg reached an HbA1c < 7.0% and ≤ 6.5%, respectively, compared with 8% and 4% for placebo (Fig. 4). The estimated proportion of subjects treated with either liraglutide 1.2 or 1.8 mg reaching ADA/EASD and IDF/AACE HbA1c targets was substantially greater compared with either placebo (P < 0.0001) or rosiglitazone (Fig. 4; P ≤ 0.0003), with more patients reaching < 7.0% with liraglutide 1.8 mg compared with 1.2 mg (P = 0.018). '],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [16120, 15735, 16120, 15735],
'Evidence End': [16315, 16449, 16449, 16449]},
{'UserID': [1, 3, 2],
'PromptID': [100, 100, 100],
'PMCID': [2871176, 2871176, 2871176],
'Valid Label': [True, True, True],
'Valid Reasoning': [True, True, True],
'Label': ['significantly decreased',
'significantly decreased',
'significantly decreased'],
'Annotations': ['After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). ',
'After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) ',
'HbA1c decreased rapidly with all doses of liraglutide when added to glimepiride compared with either rosiglitazone or placebo (i.e. glimepiride monotherapy), irrespective of previous therapy. The greatest decreases occurred with liraglutide 1.2 and 1.8 mg (Fig. 3a–c). After 26 weeks, HbA1c decreased by 1.1% from baseline (primary endpoint) with either liraglutide 1.2 or 1.8 mg, respectively, compared with either placebo (+0.2%) or rosiglitazone (−0.4%) (Fig. 3d). Estimated treatment differences and 95% CIs to placebo were: liraglutide 1.8 mg: −1.4% (1.6, −1.1); liraglutide 1.2 mg: −1.3% (1.5, −1.1); liraglutide 0.6 mg: −0.8% (−1.1, −0.6); rosiglitazone: −0.7% (−0.9, −0.4). All liraglutide doses were superior to placebo (P < 0.0001), while the two higher liraglutide doses were superior to rosiglitazone (P < 0.0001). '],
'Label Code': [-1, -1, -1],
'In Abstract': [True, True, True],
'Evidence Start': [13756, 13756, 13487],
'Evidence End': [13955, 13944, 14314]},
{'UserID': [0, 1, 3, 2],
'PromptID': [138, 138, 138, 138],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['no significant difference',
'no significant difference',
'no significant difference',
'no significant difference'],
'Annotations': ['HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)',
'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).',
'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051)',
'HOMA-B increased with liraglutide (1.8 or 1.2 mg) compared with rosiglitazone (P < 0.05), while this increase was only different to placebo with liraglutide 1.2 mg (P = 0.01) and not liraglutide 1.8 mg (P = 0.051).'],
'Label Code': [0, 0, 0, 0],
'In Abstract': [True, True, True, True],
'Evidence Start': [20728, 20728, 20728, 20728],
'Evidence End': [20941, 20942, 20941, 20942]},
{'UserID': [0, 1, 3, 2],
'PromptID': [119, 119, 119, 119],
'PMCID': [2871176, 2871176, 2871176, 2871176],
'Valid Label': [True, True, True, True],
'Valid Reasoning': [True, True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%).',
'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). ',
'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001)',
'The percentage of subjects achieving FPG values between 5.0 mmol/l and ≤ 7.2 mmol/l (ADA target) after 26 weeks was higher with liraglutide: 0.6 mg (19%; P = 0.002); 1.2 mg (37%; P < 0.001); and 1.8 mg (38%;P < 0.001) compared with placebo (7%). '],
'Label Code': [1, 1, 1, 1],
'In Abstract': [True, True, True, True],
'Evidence Start': [18230, 18230, 18230, 18230],
'Evidence End': [18475, 18476, 18419, 18476]},
{'UserID': [0, 3, 2],
'PromptID': [130, 130, 130],
'PMCID': [2871176, 2871176, 2871176],
'Valid Label': [True, True, True],
'Valid Reasoning': [True, True, True],
'Label': ['significantly increased',
'significantly increased',
'significantly increased'],
'Annotations': ['Unlike rosiglitazone, weight did not increase substantially with liraglutide and the differences between rosiglitazone and liraglutide were statistically significant (−2.3 to −1.4 kg; P < 0.0001)',
'Changes in body weight with liraglutide 1.8 mg (−0.2 kg, baseline 83.0 kg), 1.2 mg (+0.3 kg, baseline 80.0 kg) or placebo (−0.1 kg, baseline 81.9 kg) were less than with rosiglitazone (+2.1 kg, P < 0.0001, baseline 80.6 kg)',
'Unlike rosiglitazone, weight did not increase substantially with liraglutide and the differences between rosiglitazone and liraglutide were statistically significant (−2.3 to −1.4 kg; P < 0.0001), although there were no significant differences compared with placebo. '],
'Label Code': [1, 1, 1],
'In Abstract': [True, True, True],
'Evidence Start': [19950, 1756, 19950],
'Evidence End': [20145, 1979, 20217]}]}}
```
### Data Fields
- `PMCID` (`int`): ID to identify the articles.
- `Text` (`str`): Article text.
- `Prompts` (`dict`): Prompts and annotations with keys:
- 'PromptID': Which prompt the doctor is answering.
- 'PMCID'
- 'Outcome': Represent the fill-in-the-blank input for the following prompt formed "With respect to outcome, characterize the reported difference between intervention and those receiving comparator".
- 'Intervention': Represent the fill-in-the-blank input for the following prompt formed "With respect to outcome, characterize the reported difference between intervention and those receiving comparator".
- 'Comparator': Represent the fill-in-the-blank input for the following prompt formed "With respect to outcome, characterize the reported difference between intervention and those receiving comparator".
- 'Annotations': The annotation files consist of the following headings: UserID, PromptID, PMCID, Valid Label, Valid Reasoning, Label, Annotations, Label Code, In Abstract, Start Evidence, End Evidence.
### Data Splits
| name | train | validation | test |
|------|------:|-----------:|-----:|
| 1.1 | 1931 | 248 | 240 |
| 2.0 | 2690 | 340 | 334 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{lehman2019inferring,
title={Inferring Which Medical Treatments Work from Reports of Clinical Trials},
author={Lehman, Eric and DeYoung, Jay and Barzilay, Regina and Wallace, Byron C},
booktitle={Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL)},
pages={3705--3717},
year={2019}
}
@misc{deyoung2020evidence,
title={Evidence Inference 2.0: More Data, Better Models},
author={Jay DeYoung and Eric Lehman and Ben Nye and Iain J. Marshall and Byron C. Wallace},
year={2020},
eprint={2005.04177},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset. |
false |
# Dataset Card for MultiLingual LibriSpeech
## 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:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
- **Repository:** [Needs More Information]
- **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/dataset/multilingual-librispeech)
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Deprecated:</b> This legacy dataset doesn't support streaming and is not updated. Use "facebook/multilingual_librispeech" instead.</p>
</div>
Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
### Languages
The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'chapter_id': 141231,
'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'id': '1272-141231-0000',
'speaker_id': 1272,
'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'}
```
### Data Fields
- file: A path to the downloaded audio file in .flac format.
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- chapter_id: id of the audiobook chapter which includes the transcription.
### Data Splits
| | Train | Train.9h | Train.1h | Dev | Test |
| ----- | ------ | ----- | ---- | ---- | ---- |
| german | 469942 | 2194 | 241 | 3469 | 3394 |
| dutch | 374287 | 2153 | 234 | 3095 | 3075 |
| french | 258213 | 2167 | 241 | 2416 | 2426 |
| spanish | 220701 | 2110 | 233 | 2408 | 2385 |
| italian | 59623 | 2173 | 240 | 1248 | 1262 |
| portuguese | 37533 | 2116 | 236 | 826 | 871 |
| polish | 25043 | 2173 | 238 | 512 | 520 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
```
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
false |
# Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task1
## 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:** [WMT20 Quality Estimation Shared Task](http://www.statmt.org/wmt20/quality-estimation-task.html)
- **Repository:** [Github repository](https://github.com/facebookresearch/mlqe/)
- **Paper:** *Not available*
### Dataset Summary
From the homepage:
*This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.*
*Task 1 uses Wikipedia data for 6 language pairs that includes high-resource English--German (En-De) and English--Chinese (En-Zh), medium-resource Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a dataset with a combination of Wikipedia articles and Reddit articles for Russian-English (En-Ru). The datasets were collected by translating sentences sampled from source language articles using state-of-the-art NMT models built using the fairseq toolkit and annotated with Direct Assessment (DA) scores by professional translators. Each sentence was annotated following the FLORES setup, which presents a form of DA, where at least three professional translators rate each sentence from 0-100 according to the perceived translation quality. DA scores are standardised using the z-score by rater. Participating systems are required to score sentences according to z-standardised DA scores.*
### Supported Tasks and Leaderboards
From the homepage:
*Sentence-level submissions will be evaluated in terms of the Pearson's correlation metric for the DA prediction agains human DA (z-standardised mean DA score, i.e. z_mean). These are the [official evaluation scripts](https://github.com/sheffieldnlp/qe-eval-scripts). The evaluation will focus on multilingual systems, i.e. systems that are able to provide predictions for all languages in the Wikipedia domain. Therefore, average Pearson correlation across all these languages will be used to rank QE systems. We will also evaluate QE systems on a per-language basis for those interested in particular languages.*
### Languages
Eight languages are represented in this dataset:
- English (`en`)
- German (`de`)
- Romanian (`ro`)
- Estonian (`et`)
- Nepalese (`ne`)
- Sinhala (`si`)
- Russian (`ru`)
## Dataset Structure
### Data Instances
An example looks like this:
```
{
'segid': 123,
'translation': {
'en': 'José Ortega y Gasset visited Husserl at Freiburg in 1934.',
'de': '1934 besuchte José Ortega y Gasset Husserl in Freiburg.',
},
'scores': [100.0, 100.0, 100.0],
'mean': 100.0,
'z_scores': [0.9553316831588745, 1.552362322807312, 0.850531816482544],
'z_mean': 1.1194086074829102,
'model_score': -0.10244649648666382,
'doc_id': 'Edmund Husserl',
'nmt_output': '1934 besuchte José Ort@@ ega y G@@ asset Hus@@ ser@@ l in Freiburg .',
'word_probas': [-0.4458000063896179, -0.2745000123977661, -0.07199999690055847, -0.002300000051036477, -0.005900000222027302, -0.14579999446868896, -0.07500000298023224, -0.012400000356137753, -0.026900000870227814, -0.036400001496076584, -0.05299999937415123, -0.14990000426769257, -0.012400000356137753, -0.1145000010728836, -0.10999999940395355],
}
```
### Data Fields
- `segid`: segment id.
- `original`: original sentence.
- `translation`: Dictionary with pairs (source,target).
- src_lg: sequence of text in source language.
- tgt_lg: sequence of text in target language.
- `scores`: list of DA scores by all annotators - the number of annotators may vary. [] if N/A (only for `ru-en/test`).
- `mean`: average of DA scores. -10_000 if N/A (only for `ru-en/test`).
- `z_scores`: list of z-standardized DA scores. [] if N/A (only for `ru-en/test`).
- `z_mean`: average of z-standardized DA scores. -10_000 if N/A (only for `ru-en/test`).
- `model_score`: NMT model score for sentence. -10_000 if N/A (only for `ru-en/test`).
- `doc_id`: the name of the article where each original segment came from.
- `nmt_output`: the actual output of the NMT model before any post-processing, corresponding to the log-probas in `word_probas` (the token is not printed, so the number of log-probabilities equals the number of tokens plus 1).
- `word_probas`: log-probabilities from the NMT model for each decoded token including the token.
### Data Splits
There are 7 configurations in this dataset (one for each available language pair). Each configuration is composed of 7K examples for training, 1K for validation and 1K for test.
## Dataset Creation
### Curation Rationale
The original text is extracted from Wikipedia, Russian Reddit and Russian WikiQuotes. Translations are obtained using state-of-the-art NMT models built using the [fairseq toolkit](https://github.com/pytorch/fairseq) and annotated with Direct Assesment scores by professional translators.
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Unknown
### Citation Information
```
Not available.
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. |
true |
# Dataset Card for xed_english_finnish
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [Github](https://github.com/Helsinki-NLP/XED)
- **Paper:** [Arxiv](https://arxiv.org/abs/2011.01612)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is the XED dataset. The dataset consists of emotion annotated movie subtitles from OPUS. We use Plutchik's 8 core emotions to annotate. The data is multilabel. The original annotations have been sourced for mainly English and Finnish.
For the English data we used Stanford NER (named entity recognition) (Finkel et al., 2005) to replace names and locations with the tags: [PERSON] and [LOCATION] respectively.
For the Finnish data, we replaced names and locations using the Turku NER corpus (Luoma et al., 2020).
### Supported Tasks and Leaderboards
Sentiment Classification, Multilabel Classification, Multilabel Classification, Intent Classification
### Languages
English, Finnish
## Dataset Structure
### Data Instances
```
{ "sentence": "A confession that you hired [PERSON] ... and are responsible for my father's murder."
"labels": [1, 6] # anger, sadness
}
```
### Data Fields
- sentence: a line from the dataset
- labels: labels corresponding to the emotion as an integer
Where the number indicates the emotion in ascending alphabetical order: anger:1, anticipation:2, disgust:3, fear:4, joy:5, sadness:6, surprise:7, trust:8, with neutral:0 where applicable.
### Data Splits
For English:
Number of unique data points: 17528 ('en_annotated' config) + 9675 ('en_neutral' config)
Number of emotions: 8 (+neutral)
For Finnish:
Number of unique data points: 14449 ('fi_annotated' config) + 10794 ('fi_neutral' config)
Number of emotions: 8 (+neutral)
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
License: Creative Commons Attribution 4.0 International License (CC-BY)
### Citation Information
@inproceedings{ohman2020xed,
title={XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection},
author={{\"O}hman, Emily and P{\`a}mies, Marc and Kajava, Kaisla and Tiedemann, J{\"o}rg},
booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},
year={2020}
}
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq), [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset. |
false |
# Dataset Card for "discofuse"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/google-research-datasets/discofuse
- **Paper:** [DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion](https://arxiv.org/abs/1902.10526)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 6.04 GB
- **Size of the generated dataset:** 21.55 GB
- **Total amount of disk used:** 27.59 GB
### Dataset Summary
DiscoFuse is a large scale dataset for discourse-based sentence fusion.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### discofuse-sport
- **Size of downloaded dataset files:** 4.33 GB
- **Size of the generated dataset:** 15.04 GB
- **Total amount of disk used:** 19.36 GB
An example of 'train' looks as follows.
```
{
"coherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .",
"coherent_second_sentence": "Finally , an HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner .",
"connective_string": "finally ,",
"discourse_type": "PAIR_CONN",
"has_coref_type_nominal": 0.0,
"has_coref_type_pronoun": 0.0,
"incoherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .",
"incoherent_second_sentence": "An HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner ."
}
```
#### discofuse-wikipedia
- **Size of downloaded dataset files:** 1.72 GB
- **Size of the generated dataset:** 6.51 GB
- **Total amount of disk used:** 8.23 GB
An example of 'validation' looks as follows.
```
{
"coherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .",
"coherent_second_sentence": "Finally , an HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner .",
"connective_string": "finally ,",
"discourse_type": "PAIR_CONN",
"has_coref_type_nominal": 0.0,
"has_coref_type_pronoun": 0.0,
"incoherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .",
"incoherent_second_sentence": "An HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner ."
}
```
### Data Fields
The data fields are the same among all splits.
#### discofuse-sport
- `connective_string`: a `string` feature.
- `discourse_type`: a `string` feature.
- `coherent_second_sentence`: a `string` feature.
- `has_coref_type_pronoun`: a `float32` feature.
- `incoherent_first_sentence`: a `string` feature.
- `incoherent_second_sentence`: a `string` feature.
- `has_coref_type_nominal`: a `float32` feature.
- `coherent_first_sentence`: a `string` feature.
#### discofuse-wikipedia
- `connective_string`: a `string` feature.
- `discourse_type`: a `string` feature.
- `coherent_second_sentence`: a `string` feature.
- `has_coref_type_pronoun`: a `float32` feature.
- `incoherent_first_sentence`: a `string` feature.
- `incoherent_second_sentence`: a `string` feature.
- `has_coref_type_nominal`: a `float32` feature.
- `coherent_first_sentence`: a `string` feature.
### Data Splits
| name | train |validation| test |
|-------------------|-------:|---------:|-----:|
|discofuse-sport |43291020| 440902|445521|
|discofuse-wikipedia|16310585| 168081|163657|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The data is licensed under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@InProceedings{GevaEtAl2019,
title = {DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion},
author = {Geva, Mor and Malmi, Eric and Szpektor, Idan and Berant, Jonathan},
booktitle = {Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
note = {arXiv preprint arXiv:1902.10526},
year = {2019}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset. |
false |
# Dataset Card for "germeval_14"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://sites.google.com/site/germeval2014ner/](https://sites.google.com/site/germeval2014ner/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf](https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf)
- **Point of Contact:** [Darina Benikova](mailto:benikova@aiphes.tu-darmstadt.de)
- **Size of downloaded dataset files:** 10.29 MB
- **Size of the generated dataset:** 18.03 MB
- **Total amount of disk used:** 28.31 MB
### Dataset Summary
The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties: - The data was sampled from German Wikipedia and News Corpora as a collection of citations. - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens. - The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating embeddings among NEs such as [ORG FC Kickers [LOC Darmstadt]].
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
German
## Dataset Structure
### Data Instances
#### germeval_14
- **Size of downloaded dataset files:** 10.29 MB
- **Size of the generated dataset:** 18.03 MB
- **Total amount of disk used:** 28.31 MB
An example of 'train' looks as follows. This example was too long and was cropped:
```json
{
"id": "11",
"ner_tags": [13, 14, 14, 14, 14, 0, 0, 0, 0, 0, 0, 0, 19, 20, 13, 0, 1, 0, 0, 0, 0, 0, 19, 20, 20, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"nested_ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"source": "http://de.wikipedia.org/wiki/Liste_von_Filmen_mit_homosexuellem_Inhalt [2010-01-11] ",
"tokens": "[\"Scenes\", \"of\", \"a\", \"Sexual\", \"Nature\", \"(\", \"GB\", \"2006\", \")\", \"-\", \"Regie\", \":\", \"Ed\", \"Blum\", \"Shortbus\", \"(\", \"USA\", \"2006..."
}
```
### Data Fields
The data fields are the same among all splits.
#### germeval_14
- `id`: a `string` feature.
- `source`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-LOCderiv` (3), `I-LOCderiv` (4).
- `nested_ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-LOCderiv` (3), `I-LOCderiv` (4).
### Data Splits
| name |train|validation|test|
|-----------|----:|---------:|---:|
|germeval_14|24000| 2200|5100|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@inproceedings{benikova-etal-2014-nosta,
title = {NoSta-D Named Entity Annotation for German: Guidelines and Dataset},
author = {Benikova, Darina and
Biemann, Chris and
Reznicek, Marc},
booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)},
month = {may},
year = {2014},
address = {Reykjavik, Iceland},
publisher = {European Language Resources Association (ELRA)},
url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/276_Paper.pdf},
pages = {2524--2531},
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@stefan-it](https://github.com/stefan-it), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. |
false |
# Dataset Card for mt_eng_vietnamese
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://nlp.stanford.edu/projects/nmt/data/iwslt15.en-vi/
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Preprocessed Dataset from IWSLT'15 English-Vietnamese machine translation: English-Vietnamese.
### Supported Tasks and Leaderboards
Machine Translation
### Languages
English, Vietnamese
## Dataset Structure
### Data Instances
An example from the dataset:
```
{
'translation': {
'en': 'In 4 minutes , atmospheric chemist Rachel Pike provides a glimpse of the massive scientific effort behind the bold headlines on climate change , with her team -- one of thousands who contributed -- taking a risky flight over the rainforest in pursuit of data on a key molecule .',
'vi': 'Trong 4 phút , chuyên gia hoá học khí quyển Rachel Pike giới thiệu sơ lược về những nỗ lực khoa học miệt mài đằng sau những tiêu đề táo bạo về biến đổi khí hậu , cùng với đoàn nghiên cứu của mình -- hàng ngàn người đã cống hiến cho dự án này -- một chuyến bay mạo hiểm qua rừng già để tìm kiếm thông tin về một phân tử then chốt .'
}
}
```
### Data Fields
- translation:
- en: text in english
- vi: text in vietnamese
### Data Splits
train: 133318, validation: 1269, test: 1269
## 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{Luong-Manning:iwslt15,
Address = {Da Nang, Vietnam}
Author = {Luong, Minh-Thang and Manning, Christopher D.},
Booktitle = {International Workshop on Spoken Language Translation},
Title = {Stanford Neural Machine Translation Systems for Spoken Language Domain},
Year = {2015}}
```
### Contributions
Thanks to [@Nilanshrajput](https://github.com/Nilanshrajput) for adding this dataset. |
false |
# MuLD
> The Multitask Long Document Benchmark
MuLD (Multitask Long Document Benchmark) is a set of 6 NLP tasks where the inputs consist of at least 10,000 words. The benchmark covers a wide variety of task types including translation, summarization, question answering, and classification. Additionally there is a range of output lengths from a single word classification label all the way up to an output longer than the input text.
- **Repository:** https://github.com/ghomasHudson/muld
- **Paper:** https://arxiv.org/abs/2202.07362
### Supported Tasks and Leaderboards
The 6 MuLD tasks consist of:
- **NarrativeQA** - A question answering dataset requiring an understanding of the plot of books and films.
- **HotpotQA** - An expanded version of HotpotQA requiring multihop reasoning between multiple wikipedia pages. This expanded version includes the full Wikipedia pages.
- **OpenSubtitles** - A translation dataset based on the OpenSubtitles 2018 dataset. The entire subtitles for each tv show is provided, one subtitle per line in both English and German.
- **VLSP (Very Long Scientific Papers)** - An expanded version of the Scientific Papers summarization dataset. Instead of removing very long papers (e.g. thesis), we explicitly include them removing any short papers.
- **AO3 Style Change Detection** - Consists of documents formed from the work of multiple [Archive of Our Own](ao3.org) authors, where the task is to predict the author for each paragraph.
- **Movie Character Types** - Predicting whether a named character is the Hero/Villain given a movie script.
### Dataset Structure
The data is presented in a text-to-text format where each instance contains a input string, output string and (optionally) json encoded metadata.
```
{'input: 'Who was wearing the blue shirt? The beginning...', 'output': ['John'], 'metadata': ''}
```
### Data Fields
- `input`: a string which has a differing structure per task but is presented in a unified format
- `output`: a list of strings where each is a possible answer. Most instances only have a single answer, but some such as narrativeQA and VLSP may have multiple.
- `metadata`: Additional metadata which may be helpful for evaluation. In this version, only the OpenSubtitles task contains metadata (for the ContraPro annotations).
### Data Splits
Each tasks contains different splits depending what was available in the source datasets:
| Task Name | Train | Validation | Test |
|----------------------------|----|----|-----|
| NarrativeQA | ✔️ | ✔️ | ✔️ |
| HotpotQA | ✔️ | ✔️ | |
| AO3 Style Change Detection | ✔️ | ✔️ | ✔️ |
| Movie Character Types | ✔️ | ✔️ | ✔️ |
| VLSP | | | ✔️ |
| OpenSubtitles | ✔️ | | ✔️ |
### Citation Information
```
@misc{hudson2022muld,
title={MuLD: The Multitask Long Document Benchmark},
author={G Thomas Hudson and Noura Al Moubayed},
year={2022},
eprint={2202.07362},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Please also cite the papers directly used in this benchmark. |
false |
# Dataset Card for QA-SRL
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Homepage](https://dada.cs.washington.edu/qasrl/#page-top)
- **Annotation Tool:** [Annotation tool](https://github.com/luheng/qasrl_annotation)
- **Repository:** [Repository](https://dada.cs.washington.edu/qasrl/#dataset)
- **Paper:** [Qa_srl paper](https://www.aclweb.org/anthology/D15-1076.pdf)
- **Point of Contact:** [Luheng He](luheng@cs.washington.edu)
### Dataset Summary
we model predicate-argument structure of a sentence with a set of question-answer pairs. our method allows practical large-scale annotation of training data. We focus on semantic rather than syntactic annotation, and introduce a scalable method for gathering data that allows both training and evaluation.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
This dataset is in english language.
## Dataset Structure
### Data Instances
We use question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contains a verb predicate in the sentence; the answers are phrases in the sentence. For example:
`UCD finished the 2006 championship as Dublin champions , by beating St Vincents in the final .`
Predicate | Question | Answer
---|---|---|
|Finished|Who finished something? | UCD
|Finished|What did someone finish?|the 2006 championship
|Finished|What did someone finish something as? |Dublin champions
|Finished|How did someone finish something? |by beating St Vincents in the final
|beating | Who beat someone? | UCD
|beating|When did someone beat someone? |in the final
|beating|Who did someone beat?| St Vincents
### Data Fields
Annotations provided are as follows:
- `sentence`: contains tokenized sentence
- `sent_id`: is the sentence identifier
- `predicate_idx`:the index of the predicate (its position in the sentence)
- `predicate`: the predicate token
- `question`: contains the question which is a list of tokens. The question always consists of seven slots, as defined in the paper. The empty slots are represented with a marker “_”. The question ends with question mark.
- `answer`: list of answers to the question
### Data Splits
Dataset | Sentences | Verbs | QAs
--- | --- | --- |---|
**newswire-train**|744|2020|4904|
**newswire-dev**|249|664|1606|
**newswire-test**|248|652|1599
**Wikipedia-train**|`1174`|`2647`|`6414`|
**Wikipedia-dev**|`392`|`895`|`2183`|
**Wikipedia-test**|`393`|`898`|`2201`|
**Please note**
This dataset only has wikipedia data. Newswire dataset needs CoNLL-2009 English training data to get the complete data. This training data is under license. Thus, newswire dataset is not included in this data.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
We annotated over 3000 sentences (nearly 8,000 verbs) in total across two domains: newswire (PropBank) and Wikipedia.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
non-expert annotators were given a short tutorial and a small set of sample annotations (about 10 sentences). Annotators were hired if they showed good understanding of English and the task. The entire screening process usually took less than 2 hours.
#### Who are the annotators?
10 part-time, non-exper annotators from Upwork (Previously oDesk)
### 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
[Luheng He](luheng@cs.washington.edu)
### Licensing Information
[More Information Needed]
### Citation Information
```
@InProceedings{huggingface:dataset,
title = {QA-SRL: Question-Answer Driven Semantic Role Labeling},
authors={Luheng He, Mike Lewis, Luke Zettlemoyer},
year={2015}
publisher = {cs.washington.edu},
howpublished={\\url{https://dada.cs.washington.edu/qasrl/#page-top}},
}
```
### Contributions
Thanks to [@bpatidar](https://github.com/bpatidar) for adding this dataset. |
false |
# Dataset Card for RVL-CDIP
## 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:** [The RVL-CDIP Dataset](https://www.cs.cmu.edu/~aharley/rvl-cdip/)
- **Repository:**
- **Paper:** [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://arxiv.org/abs/1502.07058)
- **Leaderboard:** [RVL-CDIP leaderboard](https://paperswithcode.com/dataset/rvl-cdip)
- **Point of Contact:** [Adam W. Harley](mailto:aharley@cmu.edu)
### Dataset Summary
The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels.
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given document into one of 16 classes representing document types (letter, form, etc.). The leaderboard for this task is available [here](https://paperswithcode.com/sota/document-image-classification-on-rvl-cdip).
### Languages
All the classes and documents use English as their primary language.
## Dataset Structure
### Data Instances
A sample from the training set is provided below :
```
{
'image': <PIL.TiffImagePlugin.TiffImageFile image mode=L size=754x1000 at 0x7F9A5E92CA90>,
'label': 15
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing a document.
- `label`: an `int` classification label.
<details>
<summary>Class Label Mappings</summary>
```json
{
"0": "letter",
"1": "form",
"2": "email",
"3": "handwritten",
"4": "advertisement",
"5": "scientific report",
"6": "scientific publication",
"7": "specification",
"8": "file folder",
"9": "news article",
"10": "budget",
"11": "invoice",
"12": "presentation",
"13": "questionnaire",
"14": "resume",
"15": "memo"
}
```
</details>
### Data Splits
| |train|test|validation|
|----------|----:|----:|---------:|
|# of examples|320000|40000|40000|
The dataset was split in proportions similar to those of ImageNet.
- 320000 images were used for training,
- 40000 images for validation, and
- 40000 images for testing.
## Dataset Creation
### Curation Rationale
From the paper:
> This work makes available a new labelled subset of the IIT-CDIP collection, containing 400,000
document images across 16 categories, useful for training new CNNs for document analysis.
### Source Data
#### Initial Data Collection and Normalization
The same as in the IIT-CDIP collection.
#### Who are the source language producers?
The same as in the IIT-CDIP collection.
### Annotations
#### Annotation process
The same as in the IIT-CDIP collection.
#### Who are the annotators?
The same as in the IIT-CDIP collection.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was curated by the authors - Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis.
### Licensing Information
RVL-CDIP is a subset of IIT-CDIP, which came from the [Legacy Tobacco Document Library](https://www.industrydocuments.ucsf.edu/tobacco/), for which license information can be found [here](https://www.industrydocuments.ucsf.edu/help/copyright/).
### Citation Information
```bibtex
@inproceedings{harley2015icdar,
title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval},
author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis},
booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}},
year = {2015}
}
```
### Contributions
Thanks to [@dnaveenr](https://github.com/dnaveenr) for adding this dataset. |
false |
# Dataset Card for SciTLDR
## 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/allenai/scitldr
- **Repository:** https://github.com/allenai/scitldr
- **Paper:** https://arxiv.org/abs/2004.15011
- **Leaderboard:**
- **Point of Contact:** {isabelc,kylel,armanc,danw}@allenai.org
### Dataset Summary
`SciTLDR`: Extreme Summarization of Scientific Documents
SciTLDR is a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden.
### Supported Tasks and Leaderboards
summarization
### Languages
English
## Dataset Structure
SciTLDR is split in to a 60/20/20 train/dev/test split. For each file, each line is a json, formatted as follows
```
{
"source":[
"sent0",
"sent1",
"sent2",
...
],
"source_labels":[binary list in which 1 is the oracle sentence],
"rouge_scores":[precomputed rouge-1 scores],
"paper_id":"PAPER-ID",
"target":[
"author-tldr",
"pr-tldr0",
"pr-tldr1",
...
],
"title":"TITLE"
}
```
The keys `rouge_scores` and `source_labels` are not necessary for any code to run, precomputed Rouge scores are provided for future research.
### Data Instances
{
"source": [
"Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs.",
"MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training.",
"Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages.",
"We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter.",
"We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods.",
"We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point."
],
"source_labels": [
0,
0,
0,
1,
0,
0
],
"rouge_scores": [
0.2399999958000001,
0.26086956082230633,
0.19999999531250012,
0.38095237636054424,
0.2051282003944774,
0.2978723360796741
],
"paper_id": "rJlnfaNYvB",
"target": [
"We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.",
"Proposal for an adaptive loss scaling method during backpropagation for mix precision training where scale rate is decided automatically to reduce the underflow.",
"The authors propose a method to train models in FP16 precision that adopts a more elaborate way to minimize underflow in every layer simultaneously and automatically."
],
"title": "Adaptive Loss Scaling for Mixed Precision Training"
}
### Data Fields
- `source`: The Abstract, Introduction and Conclusion (AIC) or Full text of the paper, with one sentence per line.
- `source_labels`: Binary 0 or 1, 1 denotes the oracle sentence.
- `rouge_scores`: Precomputed ROUGE baseline scores for each sentence.
- `paper_id`: Arxiv Paper ID.
- `target`: Multiple summaries for each sentence, one sentence per line.
- `title`: Title of the paper.
### Data Splits
| | train | valid | test |
|-------------------|-------|--------|------|
| SciTLDR-A | 1992 | 618 | 619 |
| SciTLDR-AIC | 1992 | 618 | 619 |
| SciTLDR-FullText | 1992 | 618 | 619 |
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
https://allenai.org/
### Annotations
#### Annotation process
Given the title and first 128 words of a reviewer comment about a paper,
re-write the summary (if it exists) into a single sentence or an incomplete
phrase. Summaries must be no more than one sentence.
Most summaries are between 15 and 25 words. The average rewritten summary is
20 words long.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
To encourage further research in the area of extreme summarization of scientific documents.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Apache License 2.0
### Citation Information
@article{cachola2020tldr,
title={{TLDR}: Extreme Summarization of Scientific Documents},
author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
journal={arXiv:2004.15011},
year={2020},
}
### Contributions
Thanks to [@Bharat123rox](https://github.com/Bharat123rox) for adding this dataset. |
false |
# Dataset Card for TURK
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** None
- **Repository:** [TURK](https://github.com/cocoxu/simplification)
- **Paper:** [Optimizing Statistical Machine Translation for Text Simplification](https://www.aclweb.org/anthology/Q16-1029/)
- **Leaderboard:** N/A
- **Point of Contact:** [Wei Xu](mailto:wei.xu@cc.gatech.edu)
### Dataset Summary
TURK is a multi-reference dataset for the evaluation of sentence simplification in English. The dataset consists of 2,359 sentences from the [Parallel Wikipedia Simplification (PWKP) corpus](https://www.aclweb.org/anthology/C10-1152/). Each sentence is associated with 8 crowdsourced simplifications that focus on only lexical paraphrasing (no sentence splitting or deletion).
### Supported Tasks and Leaderboards
No Leaderboard for the task.
### Languages
TURK contains English text only (BCP-47: `en`).
## Dataset Structure
### Data Instances
An instance consists of an original sentence and 8 possible reference simplifications that focus on lexical paraphrasing.
```
{'original': 'one side of the armed conflicts is composed mainly of the sudanese military and the janjaweed , a sudanese militia group recruited mostly from the afro-arab abbala tribes of the northern rizeigat region in sudan .',
'simplifications': ['one side of the armed conflicts is made of sudanese military and the janjaweed , a sudanese militia recruited from the afro-arab abbala tribes of the northern rizeigat region in sudan .', 'one side of the armed conflicts consist of the sudanese military and the sudanese militia group janjaweed .', 'one side of the armed conflicts is mainly sudanese military and the janjaweed , which recruited from the afro-arab abbala tribes .', 'one side of the armed conflicts is composed mainly of the sudanese military and the janjaweed , a sudanese militia group recruited mostly from the afro-arab abbala tribes in sudan .', 'one side of the armed conflicts is made up mostly of the sudanese military and the janjaweed , a sudanese militia group whose recruits mostly come from the afro-arab abbala tribes from the northern rizeigat region in sudan .', 'the sudanese military and the janjaweed make up one of the armed conflicts , mostly from the afro-arab abbal tribes in sudan .', 'one side of the armed conflicts is composed mainly of the sudanese military and the janjaweed , a sudanese militia group recruited mostly from the afro-arab abbala tribes of the northern rizeigat regime in sudan .', 'one side of the armed conflicts is composed mainly of the sudanese military and the janjaweed , a sudanese militia group recruited mostly from the afro-arab abbala tribes of the northern rizeigat region in sudan .']}
```
### Data Fields
- `original`: an original sentence from the source datasets
- `simplifications`: a set of reference simplifications produced by crowd workers.
### Data Splits
TURK does not contain a training set; many models use [WikiLarge](https://github.com/XingxingZhang/dress) (Zhang and Lapata, 2017) or [Wiki-Auto](https://github.com/chaojiang06/wiki-auto) (Jiang et. al 2020) for training.
Each input sentence has 8 associated reference simplified sentences. 2,359 input sentences are randomly split into 2,000 validation and 359 test sentences.
| | Dev | Test | Total |
| ----- | ------ | ---- | ----- |
| Input Sentences | 2000 | 359 | 2359 |
| Reference Simplifications | 16000 | 2872 | 18872 |
## Dataset Creation
### Curation Rationale
The TURK dataset was constructed to evaluate the task of text simplification. It contains multiple human-written references that focus on only lexical simplification.
### Source Data
#### Initial Data Collection and Normalization
The input sentences in the dataset are extracted from the [Parallel Wikipedia Simplification (PWKP) corpus](https://www.aclweb.org/anthology/C10-1152/).
#### Who are the source language producers?
The references are crowdsourced from Amazon Mechanical Turk. The annotators were asked to provide simplifications without losing any information or splitting the input sentence. No other demographic or compensation information is provided in the paper.
### Annotations
#### Annotation process
The instructions given to the annotators are available in the paper.
#### Who are the annotators?
The annotators are Amazon Mechanical Turk workers.
### Personal and Sensitive Information
Since the dataset is created from English Wikipedia (August 22, 2009 version), all the information contained in the dataset is already in the public domain.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset helps move forward the research towards text simplification by creating a higher quality validation and test dataset. Progress in text simplification in turn has the potential to increase the accessibility of written documents to wider audiences.
### Discussion of Biases
The dataset may contain some social biases, as the input sentences are based on Wikipedia. Studies have shown that the English Wikipedia contains both gender biases [(Schmahl et al., 2020)](https://research.tudelft.nl/en/publications/is-wikipedia-succeeding-in-reducing-gender-bias-assessing-changes) and racial biases [(Adams et al., 2019)](https://journals.sagepub.com/doi/pdf/10.1177/2378023118823946).
### Other Known Limitations
Since the dataset contains only 2,359 sentences that are derived from Wikipedia, it is limited to a small subset of topics present on Wikipedia.
## Additional Information
### Dataset Curators
TURK was developed by researchers at the University of Pennsylvania. The work was supported by the NSF under grant IIS-1430651 and the NSF GRFP under grant 1232825.
### Licensing Information
[GNU General Public License v3.0](https://github.com/cocoxu/simplification/blob/master/LICENSE)
### Citation Information
```
@article{Xu-EtAl:2016:TACL,
author = {Wei Xu and Courtney Napoles and Ellie Pavlick and Quanze Chen and Chris Callison-Burch},
title = {Optimizing Statistical Machine Translation for Text Simplification},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year = {2016},
url = {https://cocoxu.github.io/publications/tacl2016-smt-simplification.pdf},
pages = {401--415}
}
```
### Contributions
Thanks to [@mounicam](https://github.com/mounicam) for adding this dataset. |
true |
# Human ChatGPT Comparison Corpus (HC3)
We propose the first human-ChatGPT comparison corpus, named **HC3** dataset.
This dataset is introduced in our paper:
- Paper: [***How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection***](https://arxiv.org/abs/2301.07597)
Code, models and analysis are available on our GitHub:
- GitHub: [**Chatgpt-Comparison-Detection project** 🔬](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection)
# Dataset Copyright
If the source datasets used in this corpus has a specific license which is stricter than CC-BY-SA, our products follow the same. If not, they follow CC-BY-SA license.
See [dataset copyright](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection#dataset-copyright).
# Citation
Checkout this papaer [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597)
```
@article{guo-etal-2023-hc3,
title = "How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection",
author = "Guo, Biyang and
Zhang, Xin and
Wang, Ziyuan and
Jiang, Minqi and
Nie, Jinran and
Ding, Yuxuan and
Yue, Jianwei and
Wu, Yupeng",
journal={arXiv preprint arxiv:2301.07597}
year = "2023",
}
``` |
true |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/kakaobrain/KorNLUDatasets)
- **Repository:** [Github](https://github.com/kakaobrain/KorNLUDatasets)
- **Paper:** [Arxiv](https://arxiv.org/abs/2004.03289)
- **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 [@sumanthd17](https://github.com/sumanthd17) for adding this dataset. |
false |
# Dataset Card for AMI Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [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)
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Deprecated:</b> This legacy dataset is outdated. Please, use <a href="https://huggingface.co/datasets/edinburghcstr/ami"> edinburghcstr/ami </a> instead.</p>
</div>
## Dataset Description
- **Homepage:** [AMI corpus](https://groups.inf.ed.ac.uk/ami/corpus/)
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
synchronized to a common timeline. These include close-talking and far-field microphones, individual and
room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings,
the participants also have unsynchronized pens available to them that record what is written. The meetings
were recorded in English using three different rooms with different acoustic properties, and include mostly
non-native speakers.
### Dataset Preprocessing
Individual samples of the AMI dataset contain very large audio files (between 10 and 60 minutes).
Such lengths are unfeasible for most speech recognition models. In the following, we show how the
dataset can effectively be chunked into multiple segments as defined by the dataset creators.
The following function cuts the long audio files into the defined segment lengths:
```python
import librosa
import math
from datasets import load_dataset
SAMPLE_RATE = 16_000
def chunk_audio(batch):
new_batch = {
"audio": [],
"words": [],
"speaker": [],
"lengths": [],
"word_start_times": [],
"segment_start_times": [],
}
audio, _ = librosa.load(batch["file"][0], sr=SAMPLE_RATE)
word_idx = 0
num_words = len(batch["words"][0])
for segment_idx in range(len(batch["segment_start_times"][0])):
words = []
word_start_times = []
start_time = batch["segment_start_times"][0][segment_idx]
end_time = batch["segment_end_times"][0][segment_idx]
# go back and forth with word_idx since segments overlap with each other
while (word_idx > 1) and (start_time < batch["word_end_times"][0][word_idx - 1]):
word_idx -= 1
while word_idx < num_words and (start_time > batch["word_start_times"][0][word_idx]):
word_idx += 1
new_batch["audio"].append(audio[int(start_time * SAMPLE_RATE): int(end_time * SAMPLE_RATE)])
while word_idx < num_words and batch["word_start_times"][0][word_idx] < end_time:
words.append(batch["words"][0][word_idx])
word_start_times.append(batch["word_start_times"][0][word_idx])
word_idx += 1
new_batch["lengths"].append(end_time - start_time)
new_batch["words"].append(words)
new_batch["speaker"].append(batch["segment_speakers"][0][segment_idx])
new_batch["word_start_times"].append(word_start_times)
new_batch["segment_start_times"].append(batch["segment_start_times"][0][segment_idx])
return new_batch
ami = load_dataset("ami", "headset-single")
ami = ami.map(chunk_audio, batched=True, batch_size=1, remove_columns=ami["train"].column_names)
```
The segmented audio files can still be as long as a minute. To further chunk the data into shorter
audio chunks, you can use the following script.
```python
MAX_LENGTH_IN_SECONDS = 20.0
def chunk_into_max_n_seconds(batch):
new_batch = {
"audio": [],
"text": [],
}
sample_length = batch["lengths"][0]
segment_start = batch["segment_start_times"][0]
if sample_length > MAX_LENGTH_IN_SECONDS:
num_chunks_per_sample = math.ceil(sample_length / MAX_LENGTH_IN_SECONDS)
avg_chunk_length = sample_length / num_chunks_per_sample
num_words = len(batch["words"][0])
# start chunking by times
start_word_idx = end_word_idx = 0
chunk_start_time = 0
for n in range(num_chunks_per_sample):
while (end_word_idx < num_words - 1) and (batch["word_start_times"][0][end_word_idx] < segment_start + (n + 1) * avg_chunk_length):
end_word_idx += 1
chunk_end_time = int((batch["word_start_times"][0][end_word_idx] - segment_start) * SAMPLE_RATE)
new_batch["audio"].append(batch["audio"][0][chunk_start_time: chunk_end_time])
new_batch["text"].append(" ".join(batch["words"][0][start_word_idx: end_word_idx]))
chunk_start_time = chunk_end_time
start_word_idx = end_word_idx
else:
new_batch["audio"].append(batch["audio"][0])
new_batch["text"].append(" ".join(batch["words"][0]))
return new_batch
ami = ami.map(chunk_into_max_n_seconds, batched=True, batch_size=1, remove_columns=ami["train"].column_names, num_proc=64)
```
A segmented and chunked dataset of the config `"headset-single"`can be found [here](https://huggingface.co/datasets/ami-wav2vec2/ami_single_headset_segmented_and_chunked).
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task does not have an active leaderboard at the moment.
- `speaker-diarization`: The dataset can be used to train model for Speaker Diarization (SD). The model is presented with an audio file and asked to predict which speaker spoke at what time.
### Languages
The audio is in English.
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file (or files in the case of
the multi-headset or multi-microphone dataset), called `file` and its transcription as
a list of words, called `words`. Additional information about the `speakers`, the `word_start_time`, `word_end_time`, `segment_start_time`, `segment_end_time` is given.
In addition
and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'word_ids': ["ES2004a.D.words1", "ES2004a.D.words2", ...],
'word_start_times': [0.3700000047683716, 0.949999988079071, ...],
'word_end_times': [0.949999988079071, 1.5299999713897705, ...],
'word_speakers': ['A', 'A', ...],
'segment_ids': ["ES2004a.sync.1", "ES2004a.sync.2", ...]
'segment_start_times': [10.944000244140625, 17.618999481201172, ...],
'segment_end_times': [17.618999481201172, 18.722000122070312, ...],
'segment_speakers': ['A', 'B', ...],
'words', ["hmm", "hmm", ...]
'channels': [0, 0, ..],
'file': "/.cache/huggingface/datasets/downloads/af7e748544004557b35eef8b0522d4fb2c71e004b82ba8b7343913a15def465f"
'audio': {'path': "/.cache/huggingface/datasets/downloads/af7e748544004557b35eef8b0522d4fb2c71e004b82ba8b7343913a15def465f",
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
}
```
### Data Fields
- word_ids: a list of the ids of the words
- word_start_times: a list of the start times of when the words were spoken in seconds
- word_end_times: a list of the end times of when the words were spoken in seconds
- word_speakers: a list of speakers one for each word
- segment_ids: a list of the ids of the segments
- segment_start_times: a list of the start times of when the segments start
- segment_end_times: a list of the start times of when the segments ends
- segment_speakers: a list of speakers one for each segment
- words: a list of all the spoken words
- channels: a list of all channels that were used for each word
- file: a path to the audio file
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
### Data Splits
The dataset consists of several configurations, each one having train/validation/test splits:
- headset-single: Close talking audio of single headset. This configuration only includes audio belonging to the headset of the person currently speaking.
- headset-multi (4 channels): Close talking audio of four individual headset. This configuration includes audio belonging to four individual headsets. For each annotation there are 4 audio files 0, 1, 2, 3.
- microphone-single: Far field audio of single microphone. This configuration only includes audio belonging the first microphone, *i.e.* 1-1, of the microphone array.
- microphone-multi (8 channels): Far field audio of microphone array. This configuration includes audio of the first microphone array 1-1, 1-2, ..., 1-8.
In general, `headset-single` and `headset-multi` include significantly less noise than
`microphone-single` and `microphone-multi`.
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| headset-single | 136 (80h) | 18 (9h) | 16 (9h) |
| headset-multi (4 channels) | 136 (320h) | 18 (36h) | 16 (36h) |
| microphone-single | 136 (80h) | 18 (9h) | 16 (9h) |
| microphone-multi (8 channels) | 136 (640h) | 18 (72h) | 16 (72h) |
Note that each sample contains between 10 and 60 minutes of audio data which makes it
impractical for direct transcription. One should make use of the segment and word start times and end times to chunk the samples into smaller samples of manageable size.
## Dataset Creation
All information about the dataset creation can be found
[here](https://groups.inf.ed.ac.uk/ami/corpus/overview.shtml)
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
CC BY 4.0
### Citation Information
#### TODO
### Contributions
Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) and [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
#### TODO |
true |
# Dataset Card for Wikicorpus
## 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.cs.upc.edu/~nlp/wikicorpus/
- **Repository:**
- **Paper:** https://www.cs.upc.edu/~nlp/papers/reese10.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Wikicorpus is a trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia (based on a 2006 dump) and has been automatically enriched with linguistic information. In its present version, it contains over 750 million words.
The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assigns WordNet senses, and WordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Each sub-dataset is monolingual in the languages:
- ca: Catalan
- en: English
- es: Spanish
## 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
The WikiCorpus is licensed under the same license as Wikipedia, that is, the [GNU Free Documentation License](http://www.fsf.org/licensing/licenses/fdl.html)
### Citation Information
```
@inproceedings{reese-etal-2010-wikicorpus,
title = "{W}ikicorpus: A Word-Sense Disambiguated Multilingual {W}ikipedia Corpus",
author = "Reese, Samuel and
Boleda, Gemma and
Cuadros, Montse and
Padr{\'o}, Llu{\'i}s and
Rigau, German",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/222_Paper.pdf",
abstract = "This article presents a new freely available trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia and has been automatically enriched with linguistic information. To our knowledge, this is the largest such corpus that is freely available to the community: In its present version, it contains over 750 million words. The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assigns WordNet senses, and WordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before. We present a first attempt at creating a trilingual lexical resource from the sense-tagged Wikipedia corpora, namely, WikiNet. Moreover, we present two by-products of the project that are of use for the NLP community: An open source Java-based parser for Wikipedia pages developed for the construction of the corpus, and the integration of the WSD algorithm UKB in FreeLing.",
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. |
false |
# Dataset Card for OpusWikipedia
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/Wikipedia.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
This is a corpus of parallel sentences extracted from Wikipedia by Krzysztof Wołk and Krzysztof Marasek.
Tha dataset contains 20 languages and 36 bitexts.
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs,
e.g.
```python
dataset = load_dataset("opus_wikipedia", lang1="it", lang2="pl")
```
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Wikipedia.php
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- ar
- bg
- cs
- de
- el
- en
- es
- fa
- fr
- he
- hu
- it
- nl
- pl
- pt
- ro
- ru
- sl
- tr
- vi
## Dataset Structure
### Data Instances
```
{
'id': '0',
'translation': {
"ar": "* Encyclopaedia of Mathematics online encyclopaedia from Springer, Graduate-level reference work with over 8,000 entries, illuminating nearly 50,000 notions in mathematics.",
"en": "*Encyclopaedia of Mathematics online encyclopaedia from Springer, Graduate-level reference work with over 8,000 entries, illuminating nearly 50,000 notions in mathematics."
}
}
```
### Data Fields
- `id` (`str`): Unique identifier of the parallel sentence for the pair of languages.
- `translation` (`dict`): Parallel sentences for the pair of languages.
### Data Splits
The dataset contains a single `train` split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@article{WOLK2014126,
title = {Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs},
journal = {Procedia Technology},
volume = {18},
pages = {126-132},
year = {2014},
note = {International workshop on Innovations in Information and Communication Science and Technology, IICST 2014, 3-5 September 2014, Warsaw, Poland},
issn = {2212-0173},
doi = {https://doi.org/10.1016/j.protcy.2014.11.024},
url = {https://www.sciencedirect.com/science/article/pii/S2212017314005453},
author = {Krzysztof Wołk and Krzysztof Marasek},
keywords = {Comparable corpora, machine translation, NLP},
}
```
```bibtex
@InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
```
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. |
false | # Dataset Card for _SUCX 3.0 - NER_
## Dataset Description
- **Homepage:** [https://spraakbanken.gu.se/en/resources/suc3](https://spraakbanken.gu.se/en/resources/suc3)
- **Repository:** [https://github.com/kb-labb/sucx3_ner](https://github.com/kb-labb/sucx3_ner)
- **Paper:** [SUC 2.0 manual](http://spraakbanken.gu.se/parole/Docs/SUC2.0-manual.pdf)
- **Point of Contact:**
### Dataset Summary
The dataset is a conversion of the venerable SUC 3.0 dataset into the
huggingface ecosystem.
The original dataset does not contain an official train-dev-test split, which is
introduced here; the tag distribution for the NER tags between the three splits
is mostly the same.
The dataset has three different types of tagsets: manually annotated POS,
manually annotated NER, and automatically annotated NER.
For the automatically annotated NER tags, only sentences were chosen, where the
automatic and manual annotations would match (with their respective categories).
Additionally we provide remixes of the same data with some or all sentences
being lowercased.
### Supported Tasks and Leaderboards
- Part-of-Speech tagging
- Named-Entity-Recognition
### Languages
Swedish
## Dataset Structure
### Data Remixes
- `original_tags` contain the manual NER annotations
- `lower` the whole dataset uncased
- `lower_mix` some of the dataset uncased
- `lower_both` every instance both cased and uncased
- `simple_tags` contain the automatic NER annotations
- `lower` the whole dataset uncased
- `lower_mix` some of the dataset uncased
- `lower_both` every instance both cased and uncased
### Data Instances
For each instance, there is an `id`, with an optional `_lower` suffix to mark
that it has been modified, a `tokens` list of strings containing tokens, a
`pos_tags` list of strings containing POS-tags, and a `ner_tags` list of strings
containing NER-tags.
```json
{"id": "e24d782c-e2475603_lower",
"tokens": ["-", "dels", "har", "vi", "inget", "index", "att", "g\u00e5", "efter", ",", "vi", "kr\u00e4ver", "allts\u00e5", "ers\u00e4ttning", "i", "40-talets", "penningv\u00e4rde", "."],
"pos_tags": ["MID", "KN", "VB", "PN", "DT", "NN", "IE", "VB", "PP", "MID", "PN", "VB", "AB", "NN", "PP", "NN", "NN", "MAD"],
"ner_tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}
```
### Data Fields
- `id`: a string containing the sentence-id
- `tokens`: a list of strings containing the sentence's tokens
- `pos_tags`: a list of strings containing the tokens' POS annotations
- `ner_tags`: a list of strings containing the tokens' NER annotations
### Data Splits
| Dataset Split | Size Percentage of Total Dataset Size | Number of Instances for the Original Tags |
| ------------- | ------------------------------------- | ----------------------------------------- |
| train | 64% | 46\,026 |
| dev | 16% | 11\,506 |
| test | 20% | 14\,383 |
The `simple_tags` remix has fewer instances due to the requirement to match
tags.
## Dataset Creation
See the [original webpage](https://spraakbanken.gu.se/en/resources/suc3)
## Additional Information
### Dataset Curators
[Språkbanken](sb-info@svenska.gu.se)
### Licensing Information
CC BY 4.0 (attribution)
### Citation Information
[SUC 2.0 manual](http://spraakbanken.gu.se/parole/Docs/SUC2.0-manual.pdf)
### Contributions
Thanks to [@robinqrtz](https://github.com/robinqrtz) for adding this dataset.
|
true |
# Dataset Card for "x_stance"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/ZurichNLP/xstance
- **Paper:** [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](https://arxiv.org/abs/2003.08385)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 6.41 MB
- **Size of the generated dataset:** 25.73 MB
- **Total amount of disk used:** 32.14 MB
### Dataset Summary
The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions.
It can be used to train and evaluate stance detection systems.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The comments are partly German, partly French and Italian. The questions are available in all the three languages plus English.
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 6.41 MB
- **Size of the generated dataset:** 25.73 MB
- **Total amount of disk used:** 32.14 MB
An example of 'train' looks as follows.
```
{
"author": "f27b54a137b4",
"comment": "Das Arbeitsgesetz regelt die Arbeitszeiten und schützt den Arbeitnehmer. Es macht doch Sinn, dass wenn eine Nachfrage besteht, die Läden öffnen dürfen und wenn es keine Nachfrage gibt, diese geschlossen bleiben.",
"id": 10045,
"label": "FAVOR",
"language": "de",
"numerical_label": 100,
"question": "Sind Sie für eine vollständige Liberalisierung der Geschäftsöffnungszeiten (Geschäfte können die Öffnungszeiten nach freiem Ermessen festlegen)?",
"question_id": 739,
"topic": "Economy"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `question`: a `string` feature.
- `id`: a `int32` feature.
- `question_id`: a `int32` feature.
- `language`: a `string` feature.
- `comment`: a `string` feature.
- `label`: a `string` feature.
- `numerical_label`: a `int32` feature.
- `author`: a `string` feature.
- `topic`: a `string` feature.
### Data Splits
| name |train|validation|test |
|-------|----:|---------:|----:|
|default|45640| 3926|17705|
## 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
The data have been extracted from the Swiss voting advice platform Smartvote.ch.
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset is licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
@inproceedings{vamvas2020xstance,
author = "Vamvas, Jannis and Sennrich, Rico",
title = "{X-Stance}: A Multilingual Multi-Target Dataset for Stance Detection",
booktitle = "Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \& 16th Conference on Natural Language Processing (KONVENS)",
address = "Zurich, Switzerland",
year = "2020",
month = "jun",
url = "http://ceur-ws.org/Vol-2624/paper9.pdf"
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@jvamvas](https://github.com/jvamvas) for adding this dataset. |
false |
# Dataset Card for MultiParaCrawl
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/MultiParaCrawl.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/MultiParaCrawl.php
E.g.
`dataset = load_dataset("multi_para_crawl", lang1="en", lang2="nl")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
false |
# Dataset Card for "pg19"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/deepmind/pg19](https://github.com/deepmind/pg19)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Compressive Transformers for Long-Range Sequence Modelling](https://arxiv.org/abs/1911.05507)
- **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:** 11.74 GB
- **Size of the generated dataset:** 11.51 GB
- **Total amount of disk used:** 23.25 GB
### Dataset Summary
This repository contains the PG-19 language modeling benchmark.
It includes a set of books extracted from the Project Gutenberg books library, that were published before 1919.
It also contains metadata of book titles and publication dates.
PG-19 is over double the size of the Billion Word benchmark and contains documents that are 20X longer, on average, than the WikiText long-range language modelling benchmark.
Books are partitioned into a train, validation, and test set. Book metadata is stored in metadata.csv which contains (book_id, short_book_title, publication_date).
Unlike prior benchmarks, we do not constrain the vocabulary size --- i.e. mapping rare words to an UNK token --- but instead release the data as an open-vocabulary benchmark. The only processing of the text that has been applied is the removal of boilerplate license text, and the mapping of offensive discriminatory words as specified by Ofcom to placeholder tokens. Users are free to model the data at the character-level, subword-level, or via any mechanism that can model an arbitrary string of text.
To compare models we propose to continue measuring the word-level perplexity, by calculating the total likelihood of the dataset (via any chosen subword vocabulary or character-based scheme) divided by the number of tokens --- specified below in the dataset statistics table.
One could use this dataset for benchmarking long-range language models, or use it to pre-train for other natural language processing tasks which require long-range reasoning, such as LAMBADA or NarrativeQA. We would not recommend using this dataset to train a general-purpose language model, e.g. for applications to a production-system dialogue agent, due to the dated linguistic style of old texts and the inherent biases present in historical writing.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 11.74 GB
- **Size of the generated dataset:** 11.51 GB
- **Total amount of disk used:** 23.25 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"publication_date": 1907,
"short_book_title": "La Fiammetta by Giovanni Boccaccio",
"text": "\"\\n\\n\\n\\nProduced by Ted Garvin, Dave Morgan and PG Distributed Proofreaders\\n\\n\\n\\n\\nLA FIAMMETTA\\n\\nBY\\n\\nGIOVANNI BOCCACCIO\\n...",
"url": "http://www.gutenberg.org/ebooks/10006"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `short_book_title`: a `string` feature.
- `publication_date`: a `int32` feature.
- `url`: a `string` feature.
- `text`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|28602| 50| 100|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset is licensed under [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).
### Citation Information
```
@article{raecompressive2019,
author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and
Hillier, Chloe and Lillicrap, Timothy P},
title = {Compressive Transformers for Long-Range Sequence Modelling},
journal = {arXiv preprint},
url = {https://arxiv.org/abs/1911.05507},
year = {2019},
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@lucidrains](https://github.com/lucidrains), [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
false |
# Dataset Card for ccaligned_multilingual
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://www.statmt.org/cc-aligned/
- **Repository:** [Needs More Information]
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.480.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English. These web-document pairs were constructed by performing language identification on raw web-documents, and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern matching approach yielded more than 100 million aligned documents paired with English. Recognizing that each English document was often aligned to mulitple documents in different target language, we can join on English documents to obtain aligned documents that directly pair two non-English documents (e.g., Arabic-French). This corpus was created from 68 Commoncrawl Snapshots.
To load a language which isn't part of the config, all you need to do is specify the language code. You can find the valid languages in http://www.statmt.org/cc-aligned/ E.g.
```
dataset = load_dataset("ccaligned_multilingual", language_code="fr_XX", type="documents")
```
or
```
dataset = load_dataset("ccaligned_multilingual", language_code="fr_XX", type="sentences")
```
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The text in the dataset is in (137) multiple languages aligned with english.
## Dataset Structure
### Data Instances
An instance of `documents` type for language `ak_GH`:
```
{'Domain': 'islamhouse.com', 'Source_URL': 'https://islamhouse.com/en/audios/373088/', 'Target_URL': 'https://islamhouse.com/ak/audios/373088/', 'translation': {'ak_GH': "Ntwatiaa / wɔabɔ no tɔfa wɔ mu no te ase ma Umrah - Arab kasa|Islamhouse.com|Follow us:|facebook|twitter|taepe|Titles All|Fie wibesite|kasa nyina|Buukuu edi adanse ma prente|Nhyehyɛmu|Nyim/sua Islam|Curriculums|Nyina ndeɛma|Nyina ndeɛma (295)|Buukuu/ nwoma (2)|sini / muuvi (31)|ɔdio (262)|Aɛn websideNew!|Kɔ wura kramosom mu seisei|Ebio|figa/kaasɛ|Farebae|AKAkan|Kratafa titriw|kasa interface( anyimu) : Akan|Kasa ma no mu-nsɛm : Arab kasa|ɔdio|Ntwatiaa / wɔabɔ no tɔfa wɔ mu no te ase ma Umrah|play|pause|stop|mute|unmute|max volume|Kasakyerɛ ni :|Farebae:|17 / 11 / 1432 , 15/10/2011|Nhyehyɛmu:|Jurisprudence/ Esum Nimdea|Som|Hajj na Umrah|Jurisprudence/ Esum Nimdea|Som|Hajj na Umrah|Mmira ma Hajj na Umrah|nkyerɛmu|kasamu /sɛntɛns ma te ase na Umrah wɔ ... mu no hann ma no Quran na Sunnah na te ase ma no nana na no kasamu /sɛntɛns ma bi ma no emerging yi adu obusuani|Akenkane we ye di ko kasa bi su (36)|Afar - Qafár afa|Akan|Amhari ne - አማርኛ|Arab kasa - عربي|Assamese - অসমীয়া|Bengali - বাংলা|Maldive - ދިވެހި|Greek - Ελληνικά|English ( brofo kasa) - English|Persian - فارسی|Fula - pulla|French - Français|Hausa - Hausa|Kurdish - كوردی سۆرانی|Uganda ne - Oluganda|Mandinka - Mandinko|Malayalam - മലയാളം|Nepali - नेपाली|Portuguese - Português|Russian - Русский|Sango - Sango|Sinhalese - සිංහල|Somali - Soomaali|Albania ne - Shqip|Swahili - Kiswahili|Telugu - తెలుగు ప్రజలు|Tajik - Тоҷикӣ|Thai - ไทย|Tagalog - Tagalog|Turkish - Türkçe|Uyghur - ئۇيغۇرچە|Urdu - اردو|Uzbeck ne - Ўзбек тили|Vietnamese - Việt Nam|Wolof - Wolof|Chine ne - 中文|Soma kɔ bi kyerɛ adwen kɔ wɛb ebusuapanin|Soma kɔ ne kɔ hom adamfo|Soma kɔ bi kyerɛ adwen kɔ wɛb ebusuapanin|Nsɔwso fael (1)|1|الموجز في فقه العمرة|MP3 14.7 MB|Enoumah ebatahu|Rituals/Esom ajomadie ewu Hajji mmire .. 1434 AH [01] no fapemso Enum|Fiidbak/ Ye hiya wu jun kyiri|Lenke de yɛe|kɔntakt yɛn|Aɛn webside|Qura'an Kro kronkrom|Balagh|wɔ mfinimfin Dowload faele|Yɛ atuu bra Islam mu afei|Tsin de yɛe ewu|Anaa bomu/combine hɛn melin liste|© Islamhouse Website/ Islam dan webi site|×|×|Yi mu kasa|", 'en_XX': 'SUMMARY in the jurisprudence of Umrah - Arabic - Abdul Aziz Bin Marzooq Al-Turaifi|Islamhouse.com|Follow us:|facebook|twitter|QuranEnc.com|HadeethEnc.com|Type|Titles All|Home Page|All Languages|Categories|Know about Islam|All items|All items (4057)|Books (701)|Articles (548)|Fatawa (370)|Videos (1853)|Audios (416)|Posters (98)|Greeting cards (22)|Favorites (25)|Applications (21)|Desktop Applications (3)|To convert to Islam now !|More|Figures|Sources|Curriculums|Our Services|QuranEnc.com|HadeethEnc.com|ENEnglish|Main Page|Interface Language : English|Language of the content : Arabic|Audios|تعريب عنوان المادة|SUMMARY in the jurisprudence of Umrah|play|pause|stop|mute|unmute|max volume|Lecturer : Abdul Aziz Bin Marzooq Al-Turaifi|Sources:|AlRaya Islamic Recoding in Riyadh|17 / 11 / 1432 , 15/10/2011|Categories:|Islamic Fiqh|Fiqh of Worship|Hajj and Umrah|Islamic Fiqh|Fiqh of Worship|Hajj and Umrah|Pilgrimage and Umrah|Description|SUMMARY in jurisprudence of Umrah: A statement of jurisprudence and Umrah in the light of the Quran and Sunnah and understanding of the Ancestors and the statement of some of the emerging issues related to them.|This page translated into (36)|Afar - Qafár afa|Akane - Akan|Amharic - አማርኛ|Arabic - عربي|Assamese - অসমীয়া|Bengali - বাংলা|Maldivi - ދިވެހި|Greek - Ελληνικά|English|Persian - فارسی|Fula - pulla|French - Français|Hausa - Hausa|kurdish - كوردی سۆرانی|Ugandan - Oluganda|Mandinka - Mandinko|Malayalam - മലയാളം|Nepali - नेपाली|Portuguese - Português|Russian - Русский|Sango - Yanga ti Sango|Sinhalese - සිංහල|Somali - Soomaali|Albanian - Shqip|Swahili - Kiswahili|Telugu - తెలుగు|Tajik - Тоҷикӣ|Thai - ไทย|Tagalog - Tagalog|Turkish - Türkçe|Uyghur - ئۇيغۇرچە|Urdu - اردو|Uzbek - Ўзбек тили|Vietnamese - Việt Nam|Wolof - Wolof|Chinese - 中文|Send a comment to Webmaster|Send to a friend?|Send a comment to Webmaster|Attachments (1)|1|الموجز في فقه العمرة|MP3 14.7 MB|The relevant Material|The rituals of the pilgrimage season .. 1434 AH [ 01] the fifth pillar|The Quality of the Accepted Hajj (Piligrimage) and Its Limitations|Easy Path to the Rules of the Rites of Hajj|A Call to the Pilgrims of the Scared House of Allah|More|feedback|Important links|Contact us|Privacy policy|Islam Q&A|Learning Arabic Language|About Us|Convert To Islam|Noble Quran encyclopedia|IslamHouse.com Reader|Encyclopedia of Translated Prophetic Hadiths|Our Services|The Quran|Balagh|Center for downloading files|To embrace Islam now...|Follow us through|Or join our mailing list.|© Islamhouse Website|×|×|Choose language|'}}
```
An instance of `sentences` type for language `ak_GH`:
```
{'LASER_similarity': 1.4549942016601562, 'translation': {'ak_GH': 'Salah (nyamefere) ye Mmerebeia', 'en_XX': 'What he dislikes when fasting (10)'}}
```
### Data Fields
For `documents` type:
- `Domain`: a `string` feature containing the domain.
- `Source_URL`: a `string` feature containing the source URL.
- `Target_URL`: a `string` feature containing the target URL.
- `translation`: a `dictionary` feature with two keys :
- `en_XX`: a `string` feature containing the content in English.
- <language_code>: a `string` feature containing the content in the `language_code` specified.
For `sentences` type:
- `LASER_similarity`: a `float32` feature representing the LASER similarity score.
- `translation`: a `dictionary` feature with two keys :
- `en_XX`: a `string` feature containing the content in English.
- <language_code>: a `string` feature containing the content in the `language_code` specified.
### Data Splits
Split sizes of some small configurations:
| name |train|
|----------|----:|
|documents-zz_TR|41|
|sentences-zz_TR|34|
|documents-tz_MA|4|
|sentences-tz_MA|33|
|documents-ak_GH|249|
|sentences-ak_GH|478|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@inproceedings{elkishky_ccaligned_2020,
author = {El-Kishky, Ahmed and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Koehn, Philipp},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
month = {November},
title = {{CCAligned}: A Massive Collection of Cross-lingual Web-Document Pairs},
year = {2020}
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.480",
doi = "10.18653/v1/2020.emnlp-main.480",
pages = "5960--5969"
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset. |
false |
# Dataset Card for Question Answering in Context
## 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:** [QuAC](https://quac.ai/)
- **Paper:** [QuAC: Question Answering in Context](https://arxiv.org/abs/1808.07036)
- **Leaderboard:** [QuAC's leaderboard](https://quac.ai/)
- **Point of Contact:** [Google group](https://groups.google.com/forum/#!forum/quac_ai)
### Dataset Summary
Question Answering in Context is a dataset for modeling, understanding, and participating in information seeking dialog. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context.
### Supported Tasks and Leaderboards
The core problem involves predicting a text span to answer a question about a Wikipedia section (extractive question answering). Since QuAC questions include a dialog component, each instance includes a “dialog history” of questions and answers asked in the dialog prior to the given question, along with some additional metadata.
Authors provided [an official evaluation script](https://s3.amazonaws.com/my89public/quac/scorer.py) for evaluation.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A validation examples looks like this (one entry per dialogue):
```
{
'dialogue_id': 'C_6abd2040a75d47168a9e4cca9ca3fed5_0',
'wikipedia_page_title': 'Satchel Paige',
'background': 'Leroy Robert "Satchel" Paige (July 7, 1906 - June 8, 1982) was an American Negro league baseball and Major League Baseball (MLB) pitcher who became a legend in his own lifetime by being known as perhaps the best pitcher in baseball history, by his longevity in the game, and by attracting record crowds wherever he pitched. Paige was a right-handed pitcher, and at age 42 in 1948, he was the oldest major league rookie while playing for the Cleveland Indians. He played with the St. Louis Browns until age 47, and represented them in the All-Star Game in 1952 and 1953.',
'section_title': 'Chattanooga and Birmingham: 1926-29',
'context': 'A former friend from the Mobile slums, Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month, of which Paige would collect $50 with the rest going to his mother. He also agreed to pay Lula Paige a $200 advance, and she agreed to the contract. The local newspapers--the Chattanooga News and Chattanooga Times--recognized from the beginning that Paige was special. In April 1926, shortly after his arrival, he recorded nine strikeouts over six innings against the Atlanta Black Crackers. Part way through the 1927 season, Paige\'s contract was sold to the Birmingham Black Barons of the major Negro National League (NNL). According to Paige\'s first memoir, his contract was for $450 per month, but in his second he said it was for $275. Pitching for the Black Barons, Paige threw hard but was wild and awkward. In his first big game in late June 1927, against the St. Louis Stars, Paige incited a brawl when his fastball hit the hand of St. Louis catcher Mitchell Murray. Murray then charged the mound and Paige raced for the dugout, but Murray flung his bat and struck Paige above the hip. The police were summoned, and the headline of the Birmingham Reporter proclaimed a "Near Riot." Paige improved and matured as a pitcher with help from his teammates, Sam Streeter and Harry Salmon, and his manager, Bill Gatewood. He finished the 1927 season 7-1 with 69 strikeouts and 26 walks in 89 1/3 innings. Over the next two seasons, Paige went 12-5 and 10-9 while recording 176 strikeouts in 1929. (Several sources credit his 1929 strikeout total as the all-time single-season record for the Negro leagues, though there is variation among the sources about the exact number of strikeouts.) On April 29 of that season he recorded 17 strikeouts in a game against the Cuban Stars, which exceeded what was then the major league record of 16 held by Noodles Hahn and Rube Waddell. Six days later he struck out 18 Nashville Elite Giants, a number that was tied in the white majors by Bob Feller in 1938. Due to his increased earning potential, Barons owner R. T. Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd, with both Jackson and Paige taking a cut. CANNOTANSWER',
'turn_ids': ['C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#0', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#1', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#2', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#3', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#4', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#5', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#6', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#7'],
'questions': ['what did he do in Chattanooga', 'how did he discover him', 'what position did he play', 'how did they help him', 'when did he go to Birmingham', 'how did he feel about this', 'how did he do with this team', 'What made him leave the team'],
'followups': [0, 2, 0, 1, 0, 1, 0, 1],
'yesnos': [2, 2, 2, 2, 2, 2, 2, 2]
'answers': {
'answer_starts': [
[480, 39, 0, 67, 39],
[2300, 2300, 2300],
[848, 1023, 848, 848, 1298],
[2300, 2300, 2300, 2300, 2300],
[600, 600, 600, 634, 600],
[2300, 2300, 2300],
[939, 1431, 848, 848, 1514],
[2106, 2106, 2165]
],
'texts': [
['April 1926, shortly after his arrival, he recorded nine strikeouts over six innings against the Atlanta Black Crackers.', 'Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige', 'A former friend from the Mobile slums, Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League.', 'manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month,', 'Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month,'],
['CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER'],
['Pitching for the Black Barons,', 'fastball', 'Pitching for', 'Pitching', 'Paige improved and matured as a pitcher with help from his teammates,'], ['CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER'],
["Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", "Paige's contract was sold to the Birmingham Black Barons of the major Negro National League (NNL", "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons"], ['CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER'],
['game in late June 1927, against the St. Louis Stars, Paige incited a brawl when his fastball hit the hand of St. Louis catcher Mitchell Murray.', 'He finished the 1927 season 7-1 with 69 strikeouts and 26 walks in 89 1/3 innings.', 'Pitching for the Black Barons, Paige threw hard but was wild and awkward.', 'Pitching for the Black Barons, Paige threw hard but was wild and awkward.', 'Over the next two seasons, Paige went 12-5 and 10-9 while recording 176 strikeouts in 1929. ('],
['Due to his increased earning potential, Barons owner R. T. Jackson would "rent" Paige out to other ball clubs', 'Due to his increased earning potential, Barons owner R. T. Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd,', 'Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd, with both Jackson and Paige taking a cut.']
]
},
'orig_answers': {
'answer_starts': [39, 2300, 1298, 2300, 600, 2300, 1514, 2165],
'texts': ['Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month,', 'CANNOTANSWER', 'Paige improved and matured as a pitcher with help from his teammates,', 'CANNOTANSWER', "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", 'CANNOTANSWER', 'Over the next two seasons, Paige went 12-5 and 10-9 while recording 176 strikeouts in 1929. (', 'Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd, with both Jackson and Paige taking a cut.']
},
}
```
### Data Fields
- `dialogue_id`: ID of the dialogue.
- `wikipedia_page_title`: title of the Wikipedia page.
- `background`: first paragraph of the main Wikipedia article.
- `section_tile`: Wikipedia section title.
- `context`: Wikipedia section text.
- `turn_ids`: list of identification of dialogue turns. One list of ids per dialogue.
- `questions`: list of questions in the dialogue. One list of questions per dialogue.
- `followups`: list of followup actions in the dialogue. One list of followups per dialogue. `y`: follow, `m`: maybe follow yp, `n`: don't follow up.
- `yesnos`: list of yes/no in the dialogue. One list of yes/nos per dialogue. `y`: yes, `n`: no, `x`: neither.
- `answers`: dictionary of answers to the questions (validation step of data collection)
- `answer_starts`: list of list of starting offsets. For training, list of single element lists (one answer per question).
- `texts`: list of list of span texts answering questions. For training, list of single element lists (one answer per question).
- `orig_answers`: dictionary of original answers (the ones provided by the teacher in the dialogue)
- `answer_starts`: list of starting offsets
- `texts`: list of span texts answering questions.
### Data Splits
QuAC contains 98,407 QA pairs from 13,594 dialogs. The dialogs were conducted on 8,854 unique sections from 3,611 unique Wikipedia articles, and every dialog contains between four and twelve questions.
The dataset comes with a train/dev split such that there is no overlap in sections across splits. Furthermore, the dev and test sets only include one
dialog per section, in contrast to the training set which can have multiple dialogs per section. Dev and test instances come with five reference answers instead of just one as in the training set; we obtain the extra references to improve the reliability of our evaluations, as questions can have multiple valid answer spans. The test set is not publicly available; instead, researchers must submit their models to the [leaderboard](http://quac.ai), which will run the model on our hidden test set.
The training set contains 83,568 questions (11,567 dialogues), while 7,354 (1,000) and 7,353 (1,002) separate questions are reserved for the dev and test set respectively.
## Dataset Creation
### Curation Rationale
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
### Source Data
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
#### Initial Data Collection and Normalization
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
#### Who are the source language producers?
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
### Annotations
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
#### Annotation process
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
#### Who are the annotators?
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
### Personal and Sensitive Information
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
### Discussion of Biases
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
### Other Known Limitations
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
## Additional Information
### Dataset Curators
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
### Licensing Information
The dataset is distributed under the MIT license.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@inproceedings{choi-etal-2018-quac,
title = "{Q}u{AC}: Question Answering in Context",
author = "Choi, Eunsol and
He, He and
Iyyer, Mohit and
Yatskar, Mark and
Yih, Wen-tau and
Choi, Yejin and
Liang, Percy and
Zettlemoyer, Luke",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D18-1241",
doi = "10.18653/v1/D18-1241",
pages = "2174--2184",
abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at \url{http://quac.ai}.",
}
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. |
true |
# 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
- **Homepage:** [https://scienceqa.github.io/index.html#home](https://scienceqa.github.io/index.html#home)
- **Repository:** [https://github.com/lupantech/ScienceQA](https://github.com/lupantech/ScienceQA)
- **Paper:** [https://arxiv.org/abs/2209.09513](https://arxiv.org/abs/2209.09513)
- **Leaderboard:** [https://paperswithcode.com/dataset/scienceqa](https://paperswithcode.com/dataset/scienceqa)
- **Point of Contact:** [Pan Lu](https://lupantech.github.io/) or file an issue on [Github](https://github.com/lupantech/ScienceQA/issues)
### Dataset Summary
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
### Supported Tasks and Leaderboards
Multi-modal Multiple Choice
### Languages
English
## Dataset Structure
### Data Instances
Explore more samples [here](https://scienceqa.github.io/explore.html).
``` json
{'image': Image,
'question': 'Which of these states is farthest north?',
'choices': ['West Virginia', 'Louisiana', 'Arizona', 'Oklahoma'],
'answer': 0,
'hint': '',
'task': 'closed choice',
'grade': 'grade2',
'subject': 'social science',
'topic': 'geography',
'category': 'Geography',
'skill': 'Read a map: cardinal directions',
'lecture': 'Maps have four cardinal directions, or main directions. Those directions are north, south, east, and west.\nA compass rose is a set of arrows that point to the cardinal directions. A compass rose usually shows only the first letter of each cardinal direction.\nThe north arrow points to the North Pole. On most maps, north is at the top of the map.',
'solution': 'To find the answer, look at the compass rose. Look at which way the north arrow is pointing. West Virginia is farthest north.'}
```
Some records might be missing any or all of image, lecture, solution.
### Data Fields
- `image` : Contextual image
- `question` : Prompt relating to the `lecture`
- `choices` : Multiple choice answer with 1 correct to the `question`
- `answer` : Index of choices corresponding to the correct answer
- `hint` : Hint to help answer the `question`
- `task` : Task description
- `grade` : Grade level from K-12
- `subject` : High level
- `topic` : natural-sciences, social-science, or language-science
- `category` : A subcategory of `topic`
- `skill` : A description of the task required
- `lecture` : A relevant lecture that a `question` is generated from
- `solution` : Instructions on how to solve the `question`
Note that the descriptions can be initialized with the **Show Markdown Data Fields** output of the [Datasets Tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging), you will then only need to refine the generated descriptions.
### Data Splits
- name: train
- num_bytes: 16416902
- num_examples: 12726
- name: validation
- num_bytes: 5404896
- num_examples: 4241
- name: test
- num_bytes: 5441676
- num_examples: 4241
## Dataset Creation
### Curation Rationale
When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used to diagnose the multi-hop reasoning ability and interpretability of an AI system. However, existing datasets fail to provide annotations for the answers, or are restricted to the textual-only modality, small scales, and limited domain diversity. To this end, we present Science Question Answering (ScienceQA).
### Source Data
ScienceQA is collected from elementary and high school science curricula.
#### Initial Data Collection and Normalization
See Below
#### Who are the source language producers?
See Below
### Annotations
Questions in the ScienceQA dataset are sourced from open resources managed by IXL Learning,
an online learning platform curated by experts in the field of K-12 education. The dataset includes
problems that align with California Common Core Content Standards. To construct ScienceQA, we
downloaded the original science problems and then extracted individual components (e.g. questions,
hints, images, options, answers, lectures, and solutions) from them based on heuristic rules.
We manually removed invalid questions, such as questions that have only one choice, questions that
contain faulty data, and questions that are duplicated, to comply with fair use and transformative
use of the law. If there were multiple correct answers that applied, we kept only one correct answer.
Also, we shuffled the answer options of each question to ensure the choices do not follow any
specific pattern. To make the dataset easy to use, we then used semi-automated scripts to reformat
the lectures and solutions. Therefore, special structures in the texts, such as tables and lists, are
easily distinguishable from simple text passages. Similar to ImageNet, ReClor, and PMR datasets,
ScienceQA is available for non-commercial research purposes only and the copyright belongs to
the original authors. To ensure data quality, we developed a data exploration tool to review examples
in the collected dataset, and incorrect annotations were further manually revised by experts. The tool
can be accessed at https://scienceqa.github.io/explore.html.
#### Annotation process
See above
#### Who are the annotators?
See above
### 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
- Pan Lu1,3
- Swaroop Mishra2,3
- Tony Xia1
- Liang Qiu1
- Kai-Wei Chang1
- Song-Chun Zhu1
- Oyvind Tafjord3
- Peter Clark3
- Ashwin Kalyan3
From:
1. University of California, Los Angeles
2. Arizona State University
3. Allen Institute for AI
### Licensing Information
[Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
](https://creativecommons.org/licenses/by-nc-sa/4.0/)
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@inproceedings{lu2022learn,
title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
year={2022}
}
```
### Contributions
Thanks to [Derek Thomas](https://huggingface.co/derek-thomas) [@datavistics](https://github.com/datavistics) for adding this dataset. |
false |
# Wiki Entity Similarity
Usage:
```py
from datasets import load_dataset
corpus = load_dataset('Exr0n/wiki-entity-similarity', '2018thresh20corpus', split='train')
assert corpus[0] == {'article': 'A1000 road', 'link_text': 'A1000', 'is_same': 1}
pairs = load_dataset('Exr0n/wiki-entity-similarity', '2018thresh20pairs', split='train')
assert corpus[0] == {'article': 'Rhinobatos', 'link_text': 'Ehinobatos beurleni', 'is_same': 1}
assert len(corpus) == 4_793_180
```
## Corpus (`name=*corpus`)
The corpora in this are generated by aggregating the link text that refers to various articles in context. For instance, if wiki article A refers to article B as C, then C is added to the list of aliases for article B, and the pair (B, C) is included in the dataset.
Following (DPR https://arxiv.org/pdf/2004.04906.pdf), we use the English Wikipedia dump from Dec. 20, 2018 as the source documents for link collection.
The dataset includes three quality levels, distinguished by the minimum number of inbound links required to include an article in the dataset. This filtering is motivated by the heuristic "better articles have more citations."
| Min. Inbound Links | Number of Articles | Number of Distinct Links |
|------------|--------------------|--------------------------|
| 5 | 1,080,073 | 5,787,081 |
| 10 | 605,775 | 4,407,409 |
| 20 | 324,949 | 3,195,545 |
## Training Pairs (`name=*pairs`)
This dataset also includes training pair datasets (with both positive and negative examples) intended for training classifiers. The train/dev/test split is 75/15/10 % of each corpus.
### Training Data Generation
The training pairs in this dataset are generated by taking each example from the corpus as a positive example, and creating a new negative example from the article title of the positive example and a random link text from a different article.
The articles featured in each split are disjoint from the other splits, and each split has the same number of positive (semantically the same) and negative (semantically different) examples.
For more details on the dataset motivation, see [the paper](https://arxiv.org/abs/2202.13581). If you use this dataset in your work, please cite it using the ArXiv reference.
Generation scripts can be found [in the GitHub repo](https://github.com/Exr0nProjects/wiki-entity-similarity).
|
false |
# Dataset Card for JaQuAD
## Table of Contents
- [Table of Contents](#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-fields)
- [Data Splitting](#data-splitting)
- [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)
- [Acknowledgements](#acknowledgements)
## Dataset Description
- **Repository:** https://github.com/SkelterLabsInc/JaQuAD
- **Paper:** [JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension]()
- **Point of Contact:** [jaquad@skelterlabs.com](jaquad@skelterlabs.com)
- **Size of dataset files:** 24.6 MB
- **Size of the generated dataset:** 48.6 MB
- **Total amount of disk used:** 73.2 MB
### Dataset Summary
Japanese Question Answering Dataset (JaQuAD), released in 2022, is a
human-annotated dataset created for Japanese Machine Reading Comprehension.
JaQuAD is developed to provide a SQuAD-like QA dataset in Japanese.
JaQuAD contains 39,696 question-answer pairs.
Questions and answers are manually curated by human annotators.
Contexts are collected from Japanese Wikipedia articles.
Fine-tuning [BERT-Japanese](https://huggingface.co/cl-tohoku/bert-base-japanese)
on JaQuAD achieves 78.92% for an F1 score and 63.38% for an exact match.
### Supported Tasks
- `extractive-qa`: This dataset is intended to be used for `extractive-qa`.
### Languages
Japanese (`ja`)
## Dataset Structure
### Data Instances
- **Size of dataset files:** 24.6 MB
- **Size of the generated dataset:** 48.6 MB
- **Total amount of disk used:** 73.2 MB
An example of 'validation':
```python
{
"id": "de-001-00-000",
"title": "イタセンパラ",
"context": "イタセンパラ(板鮮腹、Acheilognathuslongipinnis)は、コイ科のタナゴ亜科タナゴ属に分類される淡水>魚の一種。\n別名はビワタナゴ(琵琶鱮、琵琶鰱)。",
"question": "ビワタナゴの正式名称は何?",
"question_type": "Multiple sentence reasoning",
"answers": {
"text": "イタセンパラ",
"answer_start": 0,
"answer_type": "Object",
},
},
```
### Data Fields
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `question_type`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `answer_type`: a `string` feature.
### Data Splitting
JaQuAD consists of three sets, `train`, `validation`, and `test`. They were
created from disjoint sets of Wikipedia articles. The `test` set is not publicly
released yet. The following table shows statistics for each set.
Set | Number of Articles | Number of Contexts | Number of Questions
--------------|--------------------|--------------------|--------------------
Train | 691 | 9713 | 31748
Validation | 101 | 1431 | 3939
Test | 109 | 1479 | 4009
## Dataset Creation
### Curation Rationale
The JaQuAD dataset was created by [Skelter Labs](https://skelterlabs.com/) to
provide a SQuAD-like QA dataset in Japanese. Questions are original and based
on Japanese Wikipedia articles.
### Source Data
The articles used for the contexts are from [Japanese Wikipedia](https://ja.wikipedia.org/).
88.7% of articles are from the curated list of Japanese high-quality Wikipedia
articles, e.g., [featured articles](https://ja.wikipedia.org/wiki/Wikipedia:%E8%89%AF%E8%B3%AA%E3%81%AA%E8%A8%98%E4%BA%8B)
and [good articles](https://ja.wikipedia.org/wiki/Wikipedia:%E7%A7%80%E9%80%B8%E3%81%AA%E8%A8%98%E4%BA%8B).
### Annotations
Wikipedia articles were scrapped and divided into one more multiple paragraphs
as contexts. Annotations (questions and answer spans) are written by fluent
Japanese speakers, including natives and non-natives. Annotators were given a
context and asked to generate non-trivial questions about information in the
context.
### Personal and Sensitive Information
No personal or sensitive information is included in this dataset. Dataset
annotators has been manually verified it.
## Considerations for Using the Data
Users should consider that the articles are sampled from Wikipedia articles but
not representative of all Wikipedia articles.
### Social Impact of Dataset
The social biases of this dataset have not yet been investigated.
### Discussion of Biases
The social biases of this dataset have not yet been investigated. Articles and
questions have been selected for quality and diversity.
### Other Known Limitations
The JaQuAD dataset has limitations as follows:
- Most of them are short answers.
- Assume that a question is answerable using the corresponding context.
This dataset is incomplete yet. If you find any errors in JaQuAD, please contact
us.
## Additional Information
### Dataset Curators
Skelter Labs: [https://skelterlabs.com/](https://skelterlabs.com/)
### Licensing Information
The JaQuAD dataset is licensed under the [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```bibtex
@misc{so2022jaquad,
title={{JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension}},
author={ByungHoon So and Kyuhong Byun and Kyungwon Kang and Seongjin Cho},
year={2022},
eprint={2202.01764},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Acknowledgements
This work was supported by [TPU Research Cloud (TRC) program](https://sites.research.google/trc/).
For training models, we used cloud TPUs provided by TRC. We also thank
annotators who generated JaQuAD.
|
false | # Dataset Card for "saf_communication_networks_english"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotation process](#annotation-process)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:** [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022)
### Dataset Summary
Short Answer Feedback (SAF) dataset is a short answer dataset introduced in [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 31 English questions covering a range of college-level communication networks topics - while the original dataset presented in the paper is comprised of an assortment of both English and German short answer questions (with reference answers). Please refer to the [saf_micro_job_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_micro_job_german) dataset to examine the German subset of the original dataset. Furthermore, a similarly constructed SAF dataset (covering the German legal domain) can be found at [saf_legal_domain_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_legal_domain_german).
### Supported Tasks and Leaderboards
- `short_answer_feedback`: The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.
### Languages
The questions, reference answers, provided answers and the answer feedback in the dataset are written in English.
## Dataset Structure
### Data Instances
An example of an entry of the training split looks as follows.
```
{
"id": "1",
"question": "Is this a question?",
"reference_answer": "Yes, that is a question.",
"provided_answer": "I'm certain this is a question.",
"answer_feedback": "The response is correct.",
"verification_feedback": "Correct",
"score": 1
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature (UUID4 in HEX format).
- `question`: a `string` feature representing a question.
- `reference_answer`: a `string` feature representing a reference answer to the question.
- `provided_answer`: a `string` feature representing an answer that was provided for a particular question.
- `answer_feedback`: a `string` feature representing the feedback given to the provided answers.
- `verification_feedback`: a `string` feature representing an automatic labeling of the score. It can be `Correct` (`score` = maximum points achievable), `Incorrect` (`score` = 0) or `Partially correct` (all intermediate scores).
- `score`: a `float64` feature representing the score given to the provided answer. For most questions it ranges from 0 to 1.
### Data Splits
The dataset is comprised of four data splits.
- `train`: used for training, contains a set of questions and the provided answers to them.
- `validation`: used for validation, contains a set of questions and the provided answers to them (derived from the original training set defined in the paper).
- `test_unseen_answers`: used for testing, contains unseen answers to the questions present in the `train` split.
- `test_unseen_questions`: used for testing, contains unseen questions that do not appear in the `train` split.
| Split |train|validation|test_unseen_answers|test_unseen_questions|
|-------------------|----:|---------:|------------------:|--------------------:|
|Number of instances| 1700| 427| 375| 479|
## Dataset Creation
### Annotation Process
Two graduate students who had completed the communication networks course were selected to evaluate the answers, and both of them underwent a general annotation guideline training (supervised by a Psychology doctoral student with prior work in the field of feedback). After the training, the annotators individually provided feedback to the answers following an agreed upon scoring rubric and the general annotation guideline. The individually annotated answer files were then combined into a cohesive gold standard after discussing and solving possible disagreements.
## Additional Information
### Citation Information
```
@inproceedings{filighera-etal-2022-answer,
title = "Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset",
author = "Filighera, Anna and
Parihar, Siddharth and
Steuer, Tim and
Meuser, Tobias and
Ochs, Sebastian",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.587",
doi = "10.18653/v1/2022.acl-long.587",
pages = "8577--8591",
}
```
### Contributions
Thanks to [@JohnnyBoy2103](https://github.com/JohnnyBoy2103) for adding this dataset. |
false | # Dataset Card for "code_x_glue_cc_cloze_testing_maxmin"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits-sample-size)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-maxmin
### Dataset Summary
CodeXGLUE ClozeTesting-maxmin dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-maxmin
Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
Here we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. Each instance in the dataset contains a masked code function, its docstring and the target word.
The only difference between ClozeTest-maxmin and ClozeTest-all is their selected words sets, where ClozeTest-maxmin only contains two words while ClozeTest-all contains 930 words.
### Supported Tasks and Leaderboards
- `slot-filling`: The dataset can be used to train a model for predicting the missing token from a piece of code, similar to the Cloze test.
### Languages
- Go **programming** language
- Java **programming** language
- Javascript **programming** language
- PHP **programming** language
- Python **programming** language
- Ruby **programming** language
## Dataset Structure
### Data Instances
#### go
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "maxmin-1",
"nl_tokens": ["SetMaxStructPoolSize", "sets", "the", "struct", "pools", "max", "size", ".", "this", "may", "be", "usefull", "for", "fine", "grained", "performance", "tuning", "towards", "your", "application", "however", "the", "default", "should", "be", "fine", "for", "nearly", "all", "cases", ".", "only", "increase", "if", "you", "have", "a", "deeply", "nested", "struct", "structure", ".", "NOTE", ":", "this", "method", "is", "not", "thread", "-", "safe", "NOTE", ":", "this", "is", "only", "here", "to", "keep", "compatibility", "with", "v5", "in", "v6", "the", "method", "will", "be", "removed"],
"pl_tokens": ["func", "(", "v", "*", "Validate", ")", "SetMaxStructPoolSize", "(", "<mask>", "int", ")", "{", "structPool", "=", "&", "sync", ".", "Pool", "{", "New", ":", "newStructErrors", "}", "\n", "}"]
}
```
#### java
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "maxmin-1",
"nl_tokens": ["Test", "whether", "find", "can", "be", "found", "at", "position", "startPos", "in", "the", "string", "src", "."],
"pl_tokens": ["public", "static", "boolean", "startsWith", "(", "char", "[", "]", "src", ",", "char", "[", "]", "find", ",", "int", "startAt", ")", "{", "int", "startPos", "=", "startAt", ";", "boolean", "result", "=", "true", ";", "// Check ranges", "if", "(", "src", ".", "length", "<", "startPos", "+", "find", ".", "length", ")", "{", "result", "=", "false", ";", "}", "else", "{", "final", "int", "<mask>", "=", "find", ".", "length", ";", "for", "(", "int", "a", "=", "0", ";", "a", "<", "max", "&&", "result", ";", "a", "++", ")", "{", "if", "(", "src", "[", "startPos", "]", "!=", "find", "[", "a", "]", ")", "{", "result", "=", "false", ";", "}", "startPos", "++", ";", "}", "}", "return", "result", ";", "}"]
}
```
#### javascript
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "maxmin-1",
"nl_tokens": ["string", ".", "max", "Maximum", "length", "of", "the", "string"],
"pl_tokens": ["function", "(", "string", ")", "{", "// string.check check sting type and size", "return", "(", "(", "typeof", "string", "===", "'string'", "||", "string", "instanceof", "String", ")", "&&", "string", ".", "length", ">=", "this", ".", "<mask>", "&&", "string", ".", "length", "<=", "this", ".", "max", "&&", "(", "!", "this", ".", "match", "||", "string", ".", "match", "(", "this", ".", "match", ")", ")", ")", ";", "}"]
}
```
#### php
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "maxmin-1",
"nl_tokens": ["Read", "the", "next", "character", "from", "the", "supplied", "string", ".", "Return", "null", "when", "we", "have", "run", "out", "of", "characters", "."],
"pl_tokens": ["public", "function", "readOne", "(", ")", "{", "if", "(", "$", "this", "->", "pos", "<=", "$", "this", "->", "<mask>", ")", "{", "$", "value", "=", "$", "this", "->", "string", "[", "$", "this", "->", "pos", "]", ";", "$", "this", "->", "pos", "+=", "1", ";", "}", "else", "{", "$", "value", "=", "null", ";", "}", "return", "$", "value", ";", "}"]
}
```
#### python
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "maxmin-1",
"nl_tokens": ["Returns", "intermediary", "colors", "for", "given", "list", "of", "colors", "."],
"pl_tokens": ["def", "_interpolate", "(", "self", ",", "colors", ",", "n", "=", "100", ")", ":", "gradient", "=", "[", "]", "for", "i", "in", "_range", "(", "n", ")", ":", "l", "=", "len", "(", "colors", ")", "-", "1", "x", "=", "int", "(", "1.0", "*", "i", "/", "n", "*", "l", ")", "x", "=", "<mask>", "(", "x", "+", "0", ",", "l", ")", "y", "=", "min", "(", "x", "+", "1", ",", "l", ")", "base", "=", "1.0", "*", "n", "/", "l", "*", "x", "d", "=", "(", "i", "-", "base", ")", "/", "(", "1.0", "*", "n", "/", "l", ")", "r", "=", "colors", "[", "x", "]", ".", "r", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "r", "*", "d", "g", "=", "colors", "[", "x", "]", ".", "g", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "g", "*", "d", "b", "=", "colors", "[", "x", "]", ".", "b", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "b", "*", "d", "a", "=", "colors", "[", "x", "]", ".", "a", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "a", "*", "d", "gradient", ".", "append", "(", "color", "(", "r", ",", "g", ",", "b", ",", "a", ",", "mode", "=", "\"rgb\"", ")", ")", "gradient", ".", "append", "(", "colors", "[", "-", "1", "]", ")", "return", "gradient"]
}
```
#### ruby
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "maxmin-1",
"nl_tokens": ["Delete", "all", "copies", "that", "are", "older", "than", "the", "max", "age", "provided", "in", "seconds", "."],
"pl_tokens": ["def", "clean", "(", "<mask>", ":", "24", "*", "60", "*", "60", ")", "Futex", ".", "new", "(", "file", ",", "log", ":", "@log", ")", ".", "open", "do", "list", "=", "load", "list", ".", "reject!", "do", "|", "s", "|", "if", "s", "[", ":time", "]", ">=", "Time", ".", "now", "-", "max", "false", "else", "@log", ".", "debug", "(", "\"Copy ##{s[:name]}/#{s[:host]}:#{s[:port]} is too old, over #{Age.new(s[:time])}\"", ")", "true", "end", "end", "save", "(", "list", ")", "deleted", "=", "0", "files", ".", "each", "do", "|", "f", "|", "next", "unless", "list", ".", "find", "{", "|", "s", "|", "s", "[", ":name", "]", "==", "File", ".", "basename", "(", "f", ",", "Copies", "::", "EXT", ")", "}", ".", "nil?", "file", "=", "File", ".", "join", "(", "@dir", ",", "f", ")", "size", "=", "File", ".", "size", "(", "file", ")", "File", ".", "delete", "(", "file", ")", "@log", ".", "debug", "(", "\"Copy at #{f} deleted: #{Size.new(size)}\"", ")", "deleted", "+=", "1", "end", "list", ".", "select!", "do", "|", "s", "|", "cp", "=", "File", ".", "join", "(", "@dir", ",", "\"#{s[:name]}#{Copies::EXT}\"", ")", "wallet", "=", "Wallet", ".", "new", "(", "cp", ")", "begin", "wallet", ".", "refurbish", "raise", "\"Invalid protocol #{wallet.protocol} in #{cp}\"", "unless", "wallet", ".", "protocol", "==", "Zold", "::", "PROTOCOL", "true", "rescue", "StandardError", "=>", "e", "FileUtils", ".", "rm_rf", "(", "cp", ")", "@log", ".", "debug", "(", "\"Copy at #{cp} deleted: #{Backtrace.new(e)}\"", ")", "deleted", "+=", "1", "false", "end", "end", "save", "(", "list", ")", "deleted", "end", "end"]
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### go, java, javascript, php, python, ruby
|field name| type | description |
|----------|----------------|------------------------------|
|id |int32 | Index of the sample |
|idx |string | Original index in the dataset|
|nl_tokens |Sequence[string]| Natural language tokens |
|pl_tokens |Sequence[string]| Programming language tokens |
### Data Splits
| name |train|
|----------|----:|
|go | 152|
|java | 482|
|javascript| 272|
|php | 407|
|python | 1264|
|ruby | 38|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Data from CodeSearchNet Challenge dataset.
[More Information Needed]
#### Who are the source language producers?
Software Engineering developers.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@article{CodeXGLUE,
title={CodeXGLUE: An Open Challenge for Code Intelligence},
journal={arXiv},
year={2020},
}
@article{feng2020codebert,
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others},
journal={arXiv preprint arXiv:2002.08155},
year={2020}
}
@article{husain2019codesearchnet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. |
false |
# Dataset Card for WikiAuto
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [WikiAuto github repository](https://github.com/chaojiang06/wiki-auto)
- **Paper:** [Neural CRF Model for Sentence Alignment in Text Simplification](https://arxiv.org/abs/2005.02324)
- **Point of Contact:** [Chao Jiang](jiang.1530@osu.edu)
### Dataset Summary
WikiAuto provides a set of aligned sentences from English Wikipedia and Simple English Wikipedia as a resource to train sentence simplification systems.
The authors first crowd-sourced a set of manual alignments between sentences in a subset of the Simple English Wikipedia and their corresponding versions in English Wikipedia (this corresponds to the `manual` config in this version of dataset), then trained a neural CRF system to predict these alignments.
The trained alignment prediction model was then applied to the other articles in Simple English Wikipedia with an English counterpart to create a larger corpus of aligned sentences (corresponding to the `auto`, `auto_acl`, `auto_full_no_split`, and `auto_full_with_split` configs here).
### Supported Tasks and Leaderboards
The dataset was created to support a `text-simplification` task. Success in these tasks is typically measured using the [SARI](https://huggingface.co/metrics/sari) and [FKBLEU](https://huggingface.co/metrics/fkbleu) metrics described in the paper [Optimizing Statistical Machine Translation for Text Simplification](https://www.aclweb.org/anthology/Q16-1029.pdf).
### Languages
While both the input and output of the proposed task are in English (`en`), it should be noted that it is presented as a translation task where Wikipedia Simple English is treated as its own idiom. For a statement of what is intended (but not always observed) to constitute Simple English on this platform, see [Simple English in Wikipedia](https://simple.wikipedia.org/wiki/Wikipedia:About#Simple_English).
## Dataset Structure
### Data Instances
The data in all of the configurations looks a little different.
A `manual` config instance consists of a sentence from the Simple English Wikipedia article, one from the linked English Wikipedia article, IDs for each of them, and a label indicating whether they are aligned. Sentences on either side can be repeated so that the aligned sentences are in the same instances. For example:
```
{'alignment_label': 1,
'normal_sentence_id': '0_66252-1-0-0',
'simple_sentence_id': '0_66252-0-0-0',
'normal_sentence': 'The Local Government Act 1985 is an Act of Parliament in the United Kingdom.', 'simple_sentence': 'The Local Government Act 1985 was an Act of Parliament in the United Kingdom', 'gleu_score': 0.800000011920929}
```
Is followed by
```
{'alignment_label': 0,
'normal_sentence_id': '0_66252-1-0-1',
'simple_sentence_id': '0_66252-0-0-0',
'normal_sentence': 'Its main effect was to abolish the six county councils of the metropolitan counties that had been set up in 1974, 11 years earlier, by the Local Government Act 1972, along with the Greater London Council that had been established in 1965.',
'simple_sentence': 'The Local Government Act 1985 was an Act of Parliament in the United Kingdom', 'gleu_score': 0.08641975373029709}
```
The `auto` config shows a pair of an English and corresponding Simple English Wikipedia as an instance, with an alignment at the paragraph and sentence level:
```
{'example_id': '0',
'normal': {'normal_article_content': {'normal_sentence': ["Lata Mondal ( ; born: 16 January 1993, Dhaka) is a Bangladeshi cricketer who plays for the Bangladesh national women's cricket team.",
'She is a right handed batter.',
'Mondal was born on January 16, 1993 in Dhaka, Bangladesh.',
"Mondal made her ODI career against the Ireland women's cricket team on November 26, 2011.",
"Mondal made her T20I career against the Ireland women's cricket team on August 28, 2012.",
"In October 2018, she was named in Bangladesh's squad for the 2018 ICC Women's World Twenty20 tournament in the West Indies.",
"Mondal was a member of the team that won a silver medal in cricket against the China national women's cricket team at the 2010 Asian Games in Guangzhou, China."],
'normal_sentence_id': ['normal-41918715-0-0',
'normal-41918715-0-1',
'normal-41918715-1-0',
'normal-41918715-2-0',
'normal-41918715-3-0',
'normal-41918715-3-1',
'normal-41918715-4-0']},
'normal_article_id': 41918715,
'normal_article_title': 'Lata Mondal',
'normal_article_url': 'https://en.wikipedia.org/wiki?curid=41918715'},
'paragraph_alignment': {'normal_paragraph_id': ['normal-41918715-0'],
'simple_paragraph_id': ['simple-702227-0']},
'sentence_alignment': {'normal_sentence_id': ['normal-41918715-0-0',
'normal-41918715-0-1'],
'simple_sentence_id': ['simple-702227-0-0', 'simple-702227-0-1']},
'simple': {'simple_article_content': {'simple_sentence': ["Lata Mondal (born: 16 January 1993) is a Bangladeshi cricketer who plays for the Bangladesh national women's cricket team.",
'She is a right handed bat.'],
'simple_sentence_id': ['simple-702227-0-0', 'simple-702227-0-1']},
'simple_article_id': 702227,
'simple_article_title': 'Lata Mondal',
'simple_article_url': 'https://simple.wikipedia.org/wiki?curid=702227'}}
```
Finally, the `auto_acl`, the `auto_full_no_split`, and the `auto_full_with_split` configs were obtained by selecting the aligned pairs of sentences from `auto` to provide a ready-to-go aligned dataset to train a sequence-to-sequence system. While `auto_acl` corresponds to the filtered version of the data used to train the systems in the paper, `auto_full_no_split` and `auto_full_with_split` correspond to the unfiltered versions with and without sentence splits respectively. In the `auto_full_with_split` config, we join the sentences in the simple article mapped to the same sentence in the complex article to capture sentence splitting. Split sentences are separated by a `<SEP>` token. In the `auto_full_no_split` config, we do not join the splits and treat them as separate pairs. An instance is a single pair of sentences:
```
{'normal_sentence': 'In early work , Rutherford discovered the concept of radioactive half-life , the radioactive element radon , and differentiated and named alpha and beta radiation .\n',
'simple_sentence': 'Rutherford discovered the radioactive half-life , and the three parts of radiation which he named Alpha , Beta , and Gamma .\n'}
```
### Data Fields
The data has the following field:
- `normal_sentence`: a sentence from English Wikipedia.
- `normal_sentence_id`: a unique ID for each English Wikipedia sentence. The last two dash-separated numbers correspond to the paragraph number in the article and the sentence number in the paragraph.
- `simple_sentence`: a sentence from Simple English Wikipedia.
- `simple_sentence_id`: a unique ID for each Simple English Wikipedia sentence. The last two dash-separated numbers correspond to the paragraph number in the article and the sentence number in the paragraph.
- `alignment_label`: signifies whether a pair of sentences is aligned: labels are `2:partialAligned`, `1:aligned` and `0:notAligned`
- `paragraph_alignment`: a first step of alignment mapping English and Simple English paragraphs from linked articles
- `sentence_alignment`: the full alignment mapping English and Simple English sentences from linked articles
- `gleu_score`: the sentence level GLEU (Google-BLEU) score for each pair.
### Data Splits
In `auto`, the `part_2` split corresponds to the articles used in `manual`, and `part_1` has the rest of Wikipedia.
The `manual` config is provided with a `train`/`dev`/`test` split with the following amounts of data:
| | train | validation | test |
|------------------------|--------:|-----------:|--------:|
| Total sentence pairs | 373801 | 73249 | 118074 |
| Aligned sentence pairs | 1889 | 346 | 677 |
## Dataset Creation
### Curation Rationale
Simple English Wikipedia provides a ready source of training data for text simplification systems, as 1. articles in different languages are linked, making it easier to find parallel data and 2. the Simple English data is written by users for users rather than by professional translators. However, even though articles are aligned, finding a good sentence-level alignment can remain challenging. This work aims to provide a solution for this problem. By manually annotating a sub-set of the articles, they manage to achieve an F1 score of over 88% on predicting alignment, which allows to create a good quality sentence level aligned corpus using all of Simple English Wikipedia.
### Source Data
#### Initial Data Collection and Normalization
The authors mention that they "extracted 138,095 article pairs from the 2019/09 Wikipedia dump [...] using an improved version of the [WikiExtractor](https://github.com/attardi/wikiextractor) library". The [SpaCy](https://spacy.io/) library is used for sentence splitting.
#### Who are the source language producers?
The dataset uses langauge from Wikipedia: some demographic information is provided [here](https://en.wikipedia.org/wiki/Wikipedia:Who_writes_Wikipedia%3F).
### Annotations
#### Annotation process
Sentence alignment labels were obtained for 500 randomly sampled document pairs (10,123 sentence pairs total). The authors pre-selected several alignment candidates from English Wikipedia for each Simple Wikipedia sentence based on various similarity metrics, then asked the crowd-workers to annotate these pairs.
#### Who are the annotators?
No demographic annotation is provided for the crowd workers.
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was created by Chao Jiang, Mounica Maddela, Wuwei Lan, Yang Zhong, and Wei Xu working at Ohio State University.
### Licensing Information
The dataset is not licensed by itself, but the source Wikipedia data is under a `cc-by-sa-3.0` license.
### Citation Information
You can cite the paper presenting the dataset as:
```
@inproceedings{acl/JiangMLZX20,
author = {Chao Jiang and
Mounica Maddela and
Wuwei Lan and
Yang Zhong and
Wei Xu},
editor = {Dan Jurafsky and
Joyce Chai and
Natalie Schluter and
Joel R. Tetreault},
title = {Neural {CRF} Model for Sentence Alignment in Text Simplification},
booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational
Linguistics, {ACL} 2020, Online, July 5-10, 2020},
pages = {7943--7960},
publisher = {Association for Computational Linguistics},
year = {2020},
url = {https://www.aclweb.org/anthology/2020.acl-main.709/}
}
```
### Contributions
Thanks to [@yjernite](https://github.com/yjernite), [@mounicam](https://github.com/mounicam) for adding this dataset. |
false |
# Dataset Card for autshumato
## 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://repo.sadilar.org/handle/20.500.12185/7/discover]()
- **Repository:** []()
- **Paper:** []()
- **Leaderboard:** []()
- **Point of Contact:** []()
### Dataset Summary
Multilingual information access is stipulated in the South African constitution. In practise, this
is hampered by a lack of resources and capacity to perform the large volumes of translation
work required to realise multilingual information access. One of the aims of the Autshumato
project is to develop machine translation systems for three South African languages pairs.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
[More Information Needed]
### Dataset Curators
[More Information Needed]
### Licensing Information
### Citation Information
```
@article{groenewald2010processing,
title={Processing parallel text corpora for three South African language pairs in the Autshumato project},
author={Groenewald, Hendrik J and du Plooy, Liza},
journal={AfLaT 2010},
pages={27},
year={2010}
}
```
### Contributions
Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset. |
false |
# Dataset Card for "tner/wikiann"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/P17-1178/](https://aclanthology.org/P17-1178/)
- **Dataset:** WikiAnn
- **Domain:** Wikipedia
- **Number of Entity:** 3
### Dataset Summary
WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `LOC`, `ORG`, `PER`
## Dataset Structure
### Data Instances
An example of `train` of `ja` looks as follows.
```
{
'tokens': ['#', '#', 'ユ', 'リ', 'ウ', 'ス', '・', 'ベ', 'ー', 'リ', 'ッ', 'ク', '#', '1', '9','9','9'],
'tags': [6, 6, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikiann/raw/main/dataset/label.json).
```python
{
"B-LOC": 0,
"B-ORG": 1,
"B-PER": 2,
"I-LOC": 3,
"I-ORG": 4,
"I-PER": 5,
"O": 6
}
```
### Data Splits
| language | train | validation | test |
|:-------------|--------:|-------------:|-------:|
| ace | 100 | 100 | 100 |
| bg | 20000 | 10000 | 10000 |
| da | 20000 | 10000 | 10000 |
| fur | 100 | 100 | 100 |
| ilo | 100 | 100 | 100 |
| lij | 100 | 100 | 100 |
| mzn | 100 | 100 | 100 |
| qu | 100 | 100 | 100 |
| su | 100 | 100 | 100 |
| vi | 20000 | 10000 | 10000 |
| af | 5000 | 1000 | 1000 |
| bh | 100 | 100 | 100 |
| de | 20000 | 10000 | 10000 |
| fy | 1000 | 1000 | 1000 |
| io | 100 | 100 | 100 |
| lmo | 100 | 100 | 100 |
| nap | 100 | 100 | 100 |
| rm | 100 | 100 | 100 |
| sv | 20000 | 10000 | 10000 |
| vls | 100 | 100 | 100 |
| als | 100 | 100 | 100 |
| bn | 10000 | 1000 | 1000 |
| diq | 100 | 100 | 100 |
| ga | 1000 | 1000 | 1000 |
| is | 1000 | 1000 | 1000 |
| ln | 100 | 100 | 100 |
| nds | 100 | 100 | 100 |
| ro | 20000 | 10000 | 10000 |
| sw | 1000 | 1000 | 1000 |
| vo | 100 | 100 | 100 |
| am | 100 | 100 | 100 |
| bo | 100 | 100 | 100 |
| dv | 100 | 100 | 100 |
| gan | 100 | 100 | 100 |
| it | 20000 | 10000 | 10000 |
| lt | 10000 | 10000 | 10000 |
| ne | 100 | 100 | 100 |
| ru | 20000 | 10000 | 10000 |
| szl | 100 | 100 | 100 |
| wa | 100 | 100 | 100 |
| an | 1000 | 1000 | 1000 |
| br | 1000 | 1000 | 1000 |
| el | 20000 | 10000 | 10000 |
| gd | 100 | 100 | 100 |
| ja | 20000 | 10000 | 10000 |
| lv | 10000 | 10000 | 10000 |
| nl | 20000 | 10000 | 10000 |
| rw | 100 | 100 | 100 |
| ta | 15000 | 1000 | 1000 |
| war | 100 | 100 | 100 |
| ang | 100 | 100 | 100 |
| bs | 15000 | 1000 | 1000 |
| eml | 100 | 100 | 100 |
| gl | 15000 | 10000 | 10000 |
| jbo | 100 | 100 | 100 |
| map-bms | 100 | 100 | 100 |
| nn | 20000 | 1000 | 1000 |
| sa | 100 | 100 | 100 |
| te | 1000 | 1000 | 1000 |
| wuu | 100 | 100 | 100 |
| ar | 20000 | 10000 | 10000 |
| ca | 20000 | 10000 | 10000 |
| en | 20000 | 10000 | 10000 |
| gn | 100 | 100 | 100 |
| jv | 100 | 100 | 100 |
| mg | 100 | 100 | 100 |
| no | 20000 | 10000 | 10000 |
| sah | 100 | 100 | 100 |
| tg | 100 | 100 | 100 |
| xmf | 100 | 100 | 100 |
| arc | 100 | 100 | 100 |
| cbk-zam | 100 | 100 | 100 |
| eo | 15000 | 10000 | 10000 |
| gu | 100 | 100 | 100 |
| ka | 10000 | 10000 | 10000 |
| mhr | 100 | 100 | 100 |
| nov | 100 | 100 | 100 |
| scn | 100 | 100 | 100 |
| th | 20000 | 10000 | 10000 |
| yi | 100 | 100 | 100 |
| arz | 100 | 100 | 100 |
| cdo | 100 | 100 | 100 |
| es | 20000 | 10000 | 10000 |
| hak | 100 | 100 | 100 |
| kk | 1000 | 1000 | 1000 |
| mi | 100 | 100 | 100 |
| oc | 100 | 100 | 100 |
| sco | 100 | 100 | 100 |
| tk | 100 | 100 | 100 |
| yo | 100 | 100 | 100 |
| as | 100 | 100 | 100 |
| ce | 100 | 100 | 100 |
| et | 15000 | 10000 | 10000 |
| he | 20000 | 10000 | 10000 |
| km | 100 | 100 | 100 |
| min | 100 | 100 | 100 |
| or | 100 | 100 | 100 |
| sd | 100 | 100 | 100 |
| tl | 10000 | 1000 | 1000 |
| zea | 100 | 100 | 100 |
| ast | 1000 | 1000 | 1000 |
| ceb | 100 | 100 | 100 |
| eu | 10000 | 10000 | 10000 |
| hi | 5000 | 1000 | 1000 |
| kn | 100 | 100 | 100 |
| mk | 10000 | 1000 | 1000 |
| os | 100 | 100 | 100 |
| sh | 20000 | 10000 | 10000 |
| tr | 20000 | 10000 | 10000 |
| zh-classical | 100 | 100 | 100 |
| ay | 100 | 100 | 100 |
| ckb | 1000 | 1000 | 1000 |
| ext | 100 | 100 | 100 |
| hr | 20000 | 10000 | 10000 |
| ko | 20000 | 10000 | 10000 |
| ml | 10000 | 1000 | 1000 |
| pa | 100 | 100 | 100 |
| si | 100 | 100 | 100 |
| tt | 1000 | 1000 | 1000 |
| zh-min-nan | 100 | 100 | 100 |
| az | 10000 | 1000 | 1000 |
| co | 100 | 100 | 100 |
| fa | 20000 | 10000 | 10000 |
| hsb | 100 | 100 | 100 |
| ksh | 100 | 100 | 100 |
| mn | 100 | 100 | 100 |
| pdc | 100 | 100 | 100 |
| simple | 20000 | 1000 | 1000 |
| ug | 100 | 100 | 100 |
| zh-yue | 20000 | 10000 | 10000 |
| ba | 100 | 100 | 100 |
| crh | 100 | 100 | 100 |
| fi | 20000 | 10000 | 10000 |
| hu | 20000 | 10000 | 10000 |
| ku | 100 | 100 | 100 |
| mr | 5000 | 1000 | 1000 |
| pl | 20000 | 10000 | 10000 |
| sk | 20000 | 10000 | 10000 |
| uk | 20000 | 10000 | 10000 |
| zh | 20000 | 10000 | 10000 |
| bar | 100 | 100 | 100 |
| cs | 20000 | 10000 | 10000 |
| fiu-vro | 100 | 100 | 100 |
| hy | 15000 | 1000 | 1000 |
| ky | 100 | 100 | 100 |
| ms | 20000 | 1000 | 1000 |
| pms | 100 | 100 | 100 |
| sl | 15000 | 10000 | 10000 |
| ur | 20000 | 1000 | 1000 |
| bat-smg | 100 | 100 | 100 |
| csb | 100 | 100 | 100 |
| fo | 100 | 100 | 100 |
| ia | 100 | 100 | 100 |
| la | 5000 | 1000 | 1000 |
| mt | 100 | 100 | 100 |
| pnb | 100 | 100 | 100 |
| so | 100 | 100 | 100 |
| uz | 1000 | 1000 | 1000 |
| be-x-old | 5000 | 1000 | 1000 |
| cv | 100 | 100 | 100 |
| fr | 20000 | 10000 | 10000 |
| id | 20000 | 10000 | 10000 |
| lb | 5000 | 1000 | 1000 |
| mwl | 100 | 100 | 100 |
| ps | 100 | 100 | 100 |
| sq | 5000 | 1000 | 1000 |
| vec | 100 | 100 | 100 |
| be | 15000 | 1000 | 1000 |
| cy | 10000 | 1000 | 1000 |
| frr | 100 | 100 | 100 |
| ig | 100 | 100 | 100 |
| li | 100 | 100 | 100 |
| my | 100 | 100 | 100 |
| pt | 20000 | 10000 | 10000 |
| sr | 20000 | 10000 | 10000 |
| vep | 100 | 100 | 100 |
### Citation Information
```
@inproceedings{pan-etal-2017-cross,
title = "Cross-lingual Name Tagging and Linking for 282 Languages",
author = "Pan, Xiaoman and
Zhang, Boliang and
May, Jonathan and
Nothman, Joel and
Knight, Kevin and
Ji, Heng",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1178",
doi = "10.18653/v1/P17-1178",
pages = "1946--1958",
abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
}
``` |
false | # Dataset Card for "code_x_glue_cc_cloze_testing_all"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits-sample-size)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-all
### Dataset Summary
CodeXGLUE ClozeTesting-all dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-all
Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
Here we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. Each instance in the dataset contains a masked code function, its docstring and the target word.
The only difference between ClozeTest-maxmin and ClozeTest-all is their selected words sets, where ClozeTest-maxmin only contains two words while ClozeTest-all contains 930 words.
### Supported Tasks and Leaderboards
- `slot-filling`: The dataset can be used to train a model for predicting the missing token from a piece of code, similar to the Cloze test.
### Languages
- Go **programming** language
- Java **programming** language
- Javascript **programming** language
- PHP **programming** language
- Python **programming** language
- Ruby **programming** language
## Dataset Structure
### Data Instances
#### go
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["MarshalJSON", "supports", "json", ".", "Marshaler", "interface"],
"pl_tokens": ["func", "(", "v", "ContextRealtimeData", ")", "MarshalJSON", "(", ")", "(", "[", "]", "byte", ",", "error", ")", "{", "w", ":=", "jwriter", ".", "<mask>", "{", "}", "\n", "easyjsonC5a4559bEncodeGithubComChromedpCdprotoWebaudio7", "(", "&", "w", ",", "v", ")", "\n", "return", "w", ".", "Buffer", ".", "BuildBytes", "(", ")", ",", "w", ".", "Error", "\n", "}"]
}
```
#### java
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["/", "*", "(", "non", "-", "Javadoc", ")"],
"pl_tokens": ["@", "Override", "public", "int", "peekBit", "(", ")", "throws", "AACException", "{", "int", "ret", ";", "if", "(", "bitsCached", ">", "0", ")", "{", "ret", "=", "(", "cache", ">>", "(", "bitsCached", "-", "1", ")", ")", "&", "1", ";", "}", "else", "{", "final", "int", "word", "=", "readCache", "(", "true", ")", ";", "ret", "=", "(", "<mask>", ">>", "WORD_BITS", "-", "1", ")", "&", "1", ";", "}", "return", "ret", ";", "}"]
}
```
#### javascript
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["Cast", "query", "params", "according", "to", "type"],
"pl_tokens": ["function", "castQueryParams", "(", "relId", ",", "data", ",", "{", "relationships", "}", ")", "{", "const", "relationship", "=", "relationships", "[", "relId", "]", "if", "(", "!", "relationship", ".", "query", ")", "{", "return", "{", "}", "}", "return", "Object", ".", "keys", "(", "relationship", ".", "query", ")", ".", "reduce", "(", "(", "params", ",", "<mask>", ")", "=>", "{", "const", "value", "=", "getField", "(", "data", ",", "relationship", ".", "query", "[", "key", "]", ")", "if", "(", "value", "===", "undefined", ")", "{", "throw", "new", "TypeError", "(", "'Missing value for query param'", ")", "}", "return", "{", "...", "params", ",", "[", "key", "]", ":", "value", "}", "}", ",", "{", "}", ")", "}"]
}
```
#### php
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["Get", "choices", "."],
"pl_tokens": ["protected", "<mask>", "getChoices", "(", "FormFieldTranslation", "$", "translation", ")", "{", "$", "choices", "=", "preg_split", "(", "'/\\r\\n|\\r|\\n/'", ",", "$", "translation", "->", "getOption", "(", "'choices'", ")", ",", "-", "1", ",", "PREG_SPLIT_NO_EMPTY", ")", ";", "return", "array_combine", "(", "$", "choices", ",", "$", "choices", ")", ";", "}"]
}
```
#### python
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["Post", "a", "review"],
"pl_tokens": ["def", "post_review", "(", "session", ",", "review", ")", ":", "# POST /api/projects/0.1/reviews/", "<mask>", "=", "make_post_request", "(", "session", ",", "'reviews'", ",", "json_data", "=", "review", ")", "json_data", "=", "response", ".", "json", "(", ")", "if", "response", ".", "status_code", "==", "200", ":", "return", "json_data", "[", "'status'", "]", "else", ":", "raise", "ReviewNotPostedException", "(", "message", "=", "json_data", "[", "'message'", "]", ",", "error_code", "=", "json_data", "[", "'error_code'", "]", ",", "request_id", "=", "json_data", "[", "'request_id'", "]", ")"]
}
```
#### ruby
An example of 'train' looks as follows.
```
{
"id": 0,
"idx": "all-1",
"nl_tokens": ["By", "default", "taskers", "don", "t", "see", "the", "flor", "variables", "in", "the", "execution", ".", "If", "include_vars", "or", "exclude_vars", "is", "present", "in", "the", "configuration", "of", "the", "tasker", "some", "or", "all", "of", "the", "variables", "are", "passed", "."],
"pl_tokens": ["def", "gather_vars", "(", "executor", ",", "tconf", ",", "message", ")", "# try to return before a potentially costly call to executor.vars(nid)", "return", "nil", "if", "(", "tconf", ".", "keys", "&", "%w[", "include_vars", "exclude_vars", "]", ")", ".", "empty?", "# default behaviour, don't pass variables to taskers", "iv", "=", "expand_filter", "(", "tconf", "[", "'include_vars'", "]", ")", "return", "nil", "if", "iv", "==", "false", "ev", "=", "expand_filter", "(", "tconf", "[", "'exclude_vars'", "]", ")", "return", "{", "}", "if", "ev", "==", "true", "vars", "=", "executor", ".", "vars", "(", "message", "[", "'nid'", "]", ")", "return", "vars", "if", "iv", "==", "true", "vars", "=", "vars", ".", "select", "{", "|", "k", ",", "v", "|", "var_match", "(", "k", ",", "iv", ")", "}", "if", "<mask>", "vars", "=", "vars", ".", "reject", "{", "|", "k", ",", "v", "|", "var_match", "(", "k", ",", "ev", ")", "}", "if", "ev", "vars", "end"]
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### go, java, javascript, php, python, ruby
|field name| type | description |
|----------|----------------|------------------------------|
|id |int32 | Index of the sample |
|idx |string | Original index in the dataset|
|nl_tokens |Sequence[string]| Natural language tokens |
|pl_tokens |Sequence[string]| Programming language tokens |
### Data Splits
| name |train|
|----------|----:|
|go |25282|
|java |40492|
|javascript|13837|
|php |51930|
|python |40137|
|ruby | 4437|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Data from CodeSearchNet Challenge dataset.
[More Information Needed]
#### Who are the source language producers?
Software Engineering developers.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@article{CodeXGLUE,
title={CodeXGLUE: An Open Challenge for Code Intelligence},
journal={arXiv},
year={2020},
}
@article{feng2020codebert,
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others},
journal={arXiv preprint arXiv:2002.08155},
year={2020}
}
@article{husain2019codesearchnet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. |
true |
# Dataset Card for "social_bias_frames"
## 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://homes.cs.washington.edu/~msap/social-bias-frames/](https://homes.cs.washington.edu/~msap/social-bias-frames/)
- **Repository:** [https://homes.cs.washington.edu/~msap/social-bias-frames/](https://homes.cs.washington.edu/~msap/social-bias-frames/)
- **Paper:** [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://www.aclweb.org/anthology/2020.acl-main.486.pdf)
- **Leaderboard:**
- **Point of Contact:** [Maartin Sap](mailto:msap@cs.washington.edu)
- **Size of downloaded dataset files:** 6.32 MB
- **Size of the generated dataset:** 44.47 MB
- **Total amount of disk used:** 50.80 MB
### Dataset Summary
Warning: this document and dataset contain content that may be offensive or upsetting.
Social Bias Frames is a new way of representing the biases and offensiveness that are implied in language. For example, these frames are meant to distill the implication that "women (candidates) are less qualified" behind the statement "we shouldn’t lower our standards to hire more women." The Social Bias Inference Corpus (SBIC) supports large-scale learning and evaluation of social implications with over 150k structured annotations of social media posts, spanning over 34k implications about a thousand demographic groups.
### Supported Tasks and Leaderboards
This dataset supports both classification and generation. Sap et al. developed several models using the SBIC. They report an F1 score of 78.8 in predicting whether the posts in the test set were offensive, an F1 score of 78.6 in predicting whether the posts were intending to be offensive, an F1 score of 80.7 in predicting whether the posts were lewd, and an F1 score of 69.9 in predicting whether the posts were targeting a specific group.
Another of Sap et al.’s models performed better in the generation task. They report a BLUE score of 77.9, a Rouge-L score of 68.7, and a WMD score of 0.74 in generating a description of the targeted group given a post as well as a BLUE score of 52.6, a Rouge-L score of 44.9, and a WMD score of 2.79 in generating a description of the implied offensive statement given a post. See the paper for further details.
### Languages
The language in SBIC is predominantly white-aligned English (78%, using a lexical dialect detector, [Blodgett et al., 2016](https://www.aclweb.org/anthology/D16-1120)). The curators find less than 10% of posts in SBIC are detected to have the AAE dialect category. The BCP-47 language tag is, presumably, en-US.
## Dataset Structure
### Data Instances
Each instance contains a post that may contain an offensive statement and annotated information concerning the nature of the offensive implication as well as the demographics of the annotator and origin of the post. See the [Social Bias Frames dataset viewer](https://huggingface.co/datasets/viewer/?dataset=social_bias_frames) to explore more examples.
#### default
- **Size of downloaded dataset files:** 6.32 MB
- **Size of the generated dataset:** 44.47 MB
- **Total amount of disk used:** 50.80 MB
An example of 'validation' looks as follows.
```
{
"HITId": "3Z8UJEJOCZ74QHXOTFP0JOOGD7039J",
"WorkerId": "7162172225567546910",
"annotatorAge": "27.0",
"annotatorGender": "woman",
"annotatorMinority": "women",
"annotatorPolitics": "liberal",
"annotatorRace": "white",
"dataSource": "t/davidson",
"intentYN": "1.0",
"offensiveYN": "1.0",
"post": "Every half-breed cop in America is trying to rape white women.",
"sexPhrase": "rape white women",
"sexReason": "rape",
"sexYN": "1.0",
"speakerMinorityYN": "0.0",
"targetCategory": "race",
"targetMinority": "mixed folks",
"targetStereotype": "mixed folks are rapists.",
"whoTarget": "1.0"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- _whoTarget_: a string, ‘0.0’ if the target is a group, ‘1.0’ if the target is an individual, and blank if the post is not offensive
- _intentYN_: a string indicating if the intent behind the statement was to offend. This is a categorical variable with four possible answers, ‘1.0’ if yes, ‘0.66’ if probably, ‘0.33’ if probably not, and ‘0.0’ if no.
- _sexYN_: a string indicating whether the post contains a sexual or lewd reference. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no.
- _sexReason_: a string containing a free text explanation of what is sexual if indicated so, blank otherwise
- _offensiveYN_: a string indicating if the post could be offensive to anyone. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no.
- _annotatorGender_: a string indicating the gender of the MTurk worker
- _annotatorMinority_: a string indicating whether the MTurk worker identifies as a minority
- _sexPhrase_: a string indicating which part of the post references something sexual, blank otherwise
- _speakerMinorityYN_: a string indicating whether the speaker was part of the same minority group that's being targeted. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no.
- _WorkerId_: a string hashed version of the MTurk workerId
- _HITId_: a string id that uniquely identifies each post
- _annotatorPolitics_: a string indicating the political leaning of the MTurk worker
- _annotatorRace_: a string indicating the race of the MTurk worker
- _annotatorAge_: a string indicating the age of the MTurk worker
- _post_: a string containing the text of the post that was annotated
- _targetMinority_: a string indicating the demographic group targeted
- _targetCategory_: a string indicating the high-level category of the demographic group(s) targeted
- _targetStereotype_: a string containing the implied statement
- _dataSource_: a string indicating the source of the post (`t/...`: means Twitter, `r/...`: means a subreddit)
### Data Splits
To ensure that no post appeared in multiple splits, the curators defined a training instance as the post and its three sets of annotations. They then split the dataset into train, validation, and test sets (75%/12.5%/12.5%).
| name |train |validation|test |
|-------|-----:|---------:|----:|
|default|112900| 16738|17501|
## Dataset Creation
### Curation Rationale
The main aim for this dataset is to cover a wide variety of social biases that are implied in text, both subtle and overt, and make the biases representative of real world discrimination that people experience [RWJF 2017](https://web.archive.org/web/20200620105955/https://www.rwjf.org/en/library/research/2017/10/discrimination-in-america--experiences-and-views.html). The curators also included some innocuous statements, to balance out biases, offensive, or harmful content.
### Source Data
The curators included online posts from the following sources sometime between 2014-2019:
- r/darkJokes, r/meanJokes, r/offensiveJokes
- Reddit microaggressions ([Breitfeller et al., 2019](https://www.aclweb.org/anthology/D19-1176/))
- Toxic language detection Twitter corpora ([Waseem & Hovy, 2016](https://www.aclweb.org/anthology/N16-2013/); [Davidson et al., 2017](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/viewPaper/15665); [Founa et al., 2018](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/viewPaper/17909))
- Data scraped from hate sites (Gab, Stormfront, r/incels, r/mensrights)
#### Initial Data Collection and Normalization
The curators wanted posts to be as self-contained as possible, therefore, they applied some filtering to prevent posts from being highly context-dependent. For Twitter data, they filtered out @-replies, retweets, and links, and subsample posts such that there is a smaller correlation between AAE and offensiveness (to avoid racial bias; [Sap et al., 2019](https://www.aclweb.org/anthology/P19-1163/)). For Reddit, Gab, and Stormfront, they only selected posts that were one sentence long, don't contain links, and are between 10 and 80 words. Furthemore, for Reddit, they automatically removed posts that target automated moderation.
#### Who are the source language producers?
Due to the nature of this corpus, there is no way to know who the speakers are. But, the speakers of the Reddit, Gab, and Stormfront posts are likely white men (see [Gender by subreddit](http://bburky.com/subredditgenderratios/), [Gab users](https://en.wikipedia.org/wiki/Gab_(social_network)#cite_note-insidetheright-22), [Stormfront description](https://en.wikipedia.org/wiki/Stormfront_(website))).
### Annotations
#### Annotation process
For each post, Amazon Mechanical Turk workers indicate whether the post is offensive, whether the intent was to offend, and whether it contains lewd or sexual content. Only if annotators indicate potential offensiveness do they answer the group implication question. If the post targets or references a group or demographic, workers select or write which one(s); per selected group, they then write two to four stereotypes. Finally, workers are asked whether they think the speaker is part of one of the minority groups referenced by the post. The curators collected three annotations per post, and restricted the worker pool to the U.S. and Canada. The annotations in SBIC showed 82.4% pairwise agreement and Krippendorf’s α=0.45 on average.
Recent work has highlighted various negative side effects caused by annotating potentially abusive or harmful content (e.g., acute stress; Roberts, 2016). The curators mitigated these by limiting the number of posts that one worker could annotate in one day, paying workers above minimum wage ($7–12), and providing crisis management resources to the annotators.
#### Who are the annotators?
The annotators are Amazon Mechanical Turk workers aged 36±10 years old. The annotators consisted of 55% women, 42% men, and <1% non-binary and 82% identified as White, 4% Asian, 4% Hispanic, 4% Black. Information on their first language(s) and professional backgrounds was not collected.
### Personal and Sensitive Information
Usernames are not included with the data, but the site where the post was collected is, so the user could potentially be recovered.
## Considerations for Using the Data
### Social Impact of Dataset
The curators recognize that studying Social Bias Frames necessarily requires confronting online content that may be offensive or disturbing but argue that deliberate avoidance does not eliminate such problems. By assessing social media content through the lens of Social Bias Frames, automatic flagging or AI-augmented writing interfaces may be analyzed for potentially harmful online content with detailed explanations for users or moderators to consider and verify. In addition, the collective analysis over large corpora can also be insightful for educating people on reducing unconscious biases in their language by encouraging empathy towards a targeted group.
### Discussion of Biases
Because this is a corpus of social biases, a lot of posts contain implied or overt biases against the following groups (in decreasing order of prevalence):
- gender/sexuality
- race/ethnicity
- religion/culture
- social/political
- disability body/age
- victims
The curators warn that technology trained on this dataset could have side effects such as censorship and dialect-based racial bias.
### Other Known Limitations
Because the curators found that the dataset is predominantly written in White-aligned English, they caution researchers to consider the potential for dialect or identity-based biases in labelling ([Davidson et al.,2019](https://www.aclweb.org/anthology/W19-3504.pdf); [Sap et al., 2019a](https://www.aclweb.org/anthology/P19-1163.pdf)) before deploying technology based on SBIC.
## Additional Information
### Dataset Curators
This dataset was developed by Maarten Sap of the Paul G. Allen School of Computer Science & Engineering at the University of Washington, Saadia Gabriel, Lianhui Qin, Noah A Smith, and Yejin Choi of the Paul G. Allen School of Computer Science & Engineering and the Allen Institute for Artificial Intelligence, and Dan Jurafsky of the Linguistics & Computer Science Departments of Stanford University.
### Licensing Information
The SBIC is licensed under the [Creative Commons 4.0 License](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{sap-etal-2020-social,
title = "Social Bias Frames: Reasoning about Social and Power Implications of Language",
author = "Sap, Maarten and
Gabriel, Saadia and
Qin, Lianhui and
Jurafsky, Dan and
Smith, Noah A. and
Choi, Yejin",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.486",
doi = "10.18653/v1/2020.acl-main.486",
pages = "5477--5490",
abstract = "Warning: this paper contains content that may be offensive or upsetting. Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but rather the implied meanings, that frame people{'}s judgments about others. For example, given a statement that {``}we shouldn{'}t lower our standards to hire more women,{''} most listeners will infer the implicature intended by the speaker - that {``}women (candidates) are less qualified.{''} Most semantic formalisms, to date, do not capture such pragmatic implications in which people express social biases and power differentials in language. We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others. In addition, we introduce the Social Bias Inference Corpus to support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups. We then establish baseline approaches that learn to recover Social Bias Frames from unstructured text. We find that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias (80{\%} F1), they are not effective at spelling out more detailed explanations in terms of Social Bias Frames. Our study motivates future work that combines structured pragmatic inference with commonsense reasoning on social implications.",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@otakumesi](https://github.com/otakumesi), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
false |
# Dataset Card for SUPERB
## 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:** [http://superbbenchmark.org](http://superbbenchmark.org)
- **Repository:** [https://github.com/s3prl/s3prl](https://github.com/s3prl/s3prl)
- **Paper:** [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051)
- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [Lewis Tunstall](mailto:lewis@huggingface.co) and [Albert Villanova](mailto:albert@huggingface.co)
### Dataset Summary
SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.
### Supported Tasks and Leaderboards
The SUPERB leaderboard can be found here https://superbbenchmark.org/leaderboard and consists of the following tasks:
#### pr
Phoneme Recognition (PR) transcribes an utterance into the smallest content units. This task includes alignment modeling to avoid potentially inaccurate forced alignment. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/dev-clean/test-clean subsets are adopted in SUPERB for training/validation/testing. Phoneme transcriptions are obtained from the LibriSpeech official g2p-model-5 and the conversion script in Kaldi librispeech s5 recipe. The evaluation metric is phone error rate (PER).
#### asr
Automatic Speech Recognition (ASR) transcribes utterances into words. While PR analyzes the improvement in modeling phonetics, ASR reflects the significance of the improvement in a real-world scenario. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/devclean/test-clean subsets are used for training/validation/testing. The evaluation metric is word error rate (WER).
#### ks
Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used [Speech Commands dataset v1.0](https://www.tensorflow.org/datasets/catalog/speech_commands) for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. The evaluation metric is accuracy (ACC)
##### Example of usage:
Use these auxillary functions to:
- load the audio file into an audio data array
- sample from long `_silence_` audio clips
For other examples of handling long `_silence_` clips see the [S3PRL](https://github.com/s3prl/s3prl/blob/099ce807a6ffa6bf2482ceecfcaf83dea23da355/s3prl/downstream/speech_commands/dataset.py#L80)
or [TFDS](https://github.com/tensorflow/datasets/blob/6b8cfdb7c3c0a04e731caaa8660ce948d0a67b1e/tensorflow_datasets/audio/speech_commands.py#L143) implementations.
```python
def map_to_array(example):
import soundfile as sf
speech_array, sample_rate = sf.read(example["file"])
example["speech"] = speech_array
example["sample_rate"] = sample_rate
return example
def sample_noise(example):
# Use this function to extract random 1 sec slices of each _silence_ utterance,
# e.g. inside `torch.utils.data.Dataset.__getitem__()`
from random import randint
if example["label"] == "_silence_":
random_offset = randint(0, len(example["speech"]) - example["sample_rate"] - 1)
example["speech"] = example["speech"][random_offset : random_offset + example["sample_rate"]]
return example
```
#### qbe
Query by Example Spoken Term Detection (QbE) detects a spoken term (query) in an audio database (documents) by binary discriminating a given pair of query and document into a match or not. The English subset in [QUESST 2014 challenge](https://github.com/s3prl/s3prl/tree/master/downstream#qbe-query-by-example-spoken-term-detection) is adopted since we focus on investigating English as the first step. The evaluation metric is maximum term weighted value (MTWV) which balances misses and false alarms.
#### ic
Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. SUPERB uses the [Fluent Speech Commands dataset](https://github.com/s3prl/s3prl/tree/master/downstream#ic-intent-classification---fluent-speech-commands), where each utterance is tagged with three intent labels: action, object, and location. The evaluation metric is accuracy (ACC).
#### sf
Slot Filling (SF) predicts a sequence of semantic slot-types from an utterance, like a slot-type FromLocation for a spoken word Taipei, which is known as a slot-value. Both slot-types and slot-values are essential for an SLU system to function. The evaluation metrics thus include slot-type F1 score and slotvalue CER. [Audio SNIPS](https://github.com/s3prl/s3prl/tree/master/downstream#sf-end-to-end-slot-filling) is adopted, which synthesized multi-speaker utterances for SNIPS. Following the standard split in SNIPS, US-accent speakers are further selected for training, and others are for validation/testing.
#### si
Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing. The widely used [VoxCeleb1 dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) is adopted, and the evaluation metric is accuracy (ACC).
#### asv
Automatic Speaker Verification (ASV) verifies whether the speakers of a pair of utterances match as a binary classification, and speakers in the testing set may not appear in the training set. Thus, ASV is more challenging than SID. VoxCeleb1 is used without VoxCeleb2 training data and noise augmentation. The evaluation metric is equal error rate (EER).
#### sd
Speaker Diarization (SD) predicts *who is speaking when* for each timestamp, and multiple speakers can speak simultaneously. The model has to encode rich speaker characteristics for each frame and should be able to represent mixtures of signals. [LibriMix](https://github.com/s3prl/s3prl/tree/master/downstream#sd-speaker-diarization) is adopted where LibriSpeech train-clean-100/dev-clean/test-clean are used to generate mixtures for training/validation/testing. We focus on the two-speaker scenario as the first step. The time-coded speaker labels were generated using alignments from Kaldi LibriSpeech ASR model. The evaluation metric is diarization error rate (DER).
##### Example of usage
Use these auxiliary functions to:
- load the audio file into an audio data array
- generate the label array
```python
def load_audio_file(example, frame_shift=160):
import soundfile as sf
example["array"], example["sample_rate"] = sf.read(
example["file"], start=example["start"] * frame_shift, stop=example["end"] * frame_shift
)
return example
def generate_label(example, frame_shift=160, num_speakers=2, rate=16000):
import numpy as np
start = example["start"]
end = example["end"]
frame_num = end - start
speakers = sorted({speaker["speaker_id"] for speaker in example["speakers"]})
label = np.zeros((frame_num, num_speakers), dtype=np.int32)
for speaker in example["speakers"]:
speaker_index = speakers.index(speaker["speaker_id"])
start_frame = np.rint(speaker["start"] * rate / frame_shift).astype(int)
end_frame = np.rint(speaker["end"] * rate / frame_shift).astype(int)
rel_start = rel_end = None
if start <= start_frame < end:
rel_start = start_frame - start
if start < end_frame <= end:
rel_end = end_frame - start
if rel_start is not None or rel_end is not None:
label[rel_start:rel_end, speaker_index] = 1
example["label"] = label
return example
```
#### er
Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset [IEMOCAP](https://github.com/s3prl/s3prl/tree/master/downstream#er-emotion-recognition) is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion classes to leave the final four classes with a similar amount of data points and cross-validates on five folds of the standard splits. The evaluation metric is accuracy (ACC).
### Languages
The language data in SUPERB is in English (BCP-47 `en`)
## Dataset Structure
### Data Instances
#### pr
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### asr
An example from each split looks like:
```python
{'chapter_id': 1240,
'file': 'path/to/file.flac',
'audio': {'path': 'path/to/file.flac',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'id': '103-1240-0000',
'speaker_id': 103,
'text': 'CHAPTER ONE MISSUS RACHEL LYNDE IS SURPRISED MISSUS RACHEL LYNDE '
'LIVED JUST WHERE THE AVONLEA MAIN ROAD DIPPED DOWN INTO A LITTLE '
'HOLLOW FRINGED WITH ALDERS AND LADIES EARDROPS AND TRAVERSED BY A '
'BROOK'}
```
#### ks
An example from each split looks like:
```python
{
'file': '/path/yes/af7a8296_nohash_1.wav',
'audio': {'path': '/path/yes/af7a8296_nohash_1.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'label': 0 # 'yes'
}
```
#### qbe
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### ic
```python
{
'file': "/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav",
'audio': {'path': '/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'speaker_id': '2BqVo8kVB2Skwgyb',
'text': 'Turn the bedroom lights off',
'action': 3, # 'deactivate'
'object': 7, # 'lights'
'location': 0 # 'bedroom'
}
```
#### sf
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### si
```python
{
'file': '/path/wav/id10003/na8-QEFmj44/00003.wav',
'audio': {'path': '/path/wav/id10003/na8-QEFmj44/00003.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'label': 2 # 'id10003'
}
```
#### asv
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sd
An example from each split looks like:
```python
{
'record_id': '1578-6379-0038_6415-111615-0009',
'file': 'path/to/file.wav',
'audio': {'path': 'path/to/file.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'start': 0,
'end': 1590,
'speakers': [
{'speaker_id': '1578', 'start': 28, 'end': 657},
{'speaker_id': '6415', 'start': 28, 'end': 1576}
]
}
```
#### er
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
####Note abouth the `audio` fields
When accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
#### pr
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### asr
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `text` (`string`): The transcription of the audio file.
- `speaker_id` (`integer`): A unique ID of the speaker. The same speaker id can be found for multiple data samples.
- `chapter_id` (`integer`): ID of the audiobook chapter which includes the transcription.
- `id` (`string`): A unique ID of the data sample.
#### ks
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `label` (`ClassLabel`): Label of the spoken command. Possible values:
- `0: "yes", 1: "no", 2: "up", 3: "down", 4: "left", 5: "right", 6: "on", 7: "off", 8: "stop", 9: "go", 10: "_silence_", 11: "_unknown_"`
#### qbe
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### ic
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `speaker_id` (`string`): ID of the speaker.
- `text` (`string`): Transcription of the spoken command.
- `action` (`ClassLabel`): Label of the command's action. Possible values:
- `0: "activate", 1: "bring", 2: "change language", 3: "deactivate", 4: "decrease", 5: "increase"`
- `object` (`ClassLabel`): Label of the command's object. Possible values:
- `0: "Chinese", 1: "English", 2: "German", 3: "Korean", 4: "heat", 5: "juice", 6: "lamp", 7: "lights", 8: "music", 9: "newspaper", 10: "none", 11: "shoes", 12: "socks", 13: "volume"`
- `location` (`ClassLabel`): Label of the command's location. Possible values:
- `0: "bedroom", 1: "kitchen", 2: "none", 3: "washroom"`
#### sf
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### si
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `label` (`ClassLabel`): Label (ID) of the speaker. Possible values:
- `0: "id10001", 1: "id10002", 2: "id10003", ..., 1250: "id11251"`
#### asv
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sd
The data fields in all splits are:
- `record_id` (`string`): ID of the record.
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `start` (`integer`): Start frame of the audio.
- `end` (`integer`): End frame of the audio.
- `speakers` (`list` of `dict`): List of speakers in the audio. Each item contains the fields:
- `speaker_id` (`string`): ID of the speaker.
- `start` (`integer`): Frame when the speaker starts speaking.
- `end` (`integer`): Frame when the speaker stops speaking.
#### er
- `file` (`string`): Path to the WAV audio file.
- `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `label` (`ClassLabel`): Label of the speech emotion. Possible values:
- `0: "neu", 1: "hap", 2: "ang", 3: "sad"`
### Data Splits
#### pr
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### asr
| | train | validation | test |
|-----|------:|-----------:|-----:|
| asr | 28539 | 2703 | 2620 |
#### ks
| | train | validation | test |
|----|------:|-----------:|-----:|
| ks | 51094 | 6798 | 3081 |
#### qbe
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### ic
| | train | validation | test |
|----|------:|-----------:|-----:|
| ic | 23132 | 3118 | 3793 |
#### sf
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### si
| | train | validation | test |
|----|-------:|-----------:|-----:|
| si | 138361 | 6904 | 8251 |
#### asv
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sd
The data is split into "train", "dev" and "test" sets, each containing the following number of examples:
| | train | dev | test |
|----|------:|-----:|-----:|
| sd | 13901 | 3014 | 3002 |
#### er
The data is split into 5 sets intended for 5-fold cross-validation:
| | session1 | session2 | session3 | session4 | session5 |
|----|---------:|---------:|---------:|---------:|---------:|
| er | 1085 | 1023 | 1151 | 1031 | 1241 |
## 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
```
@article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
Xuankai Chang and
Guan{-}Ting Lin and
Tzu{-}Hsien Huang and
Wei{-}Cheng Tseng and
Ko{-}tik Lee and
Da{-}Rong Liu and
Zili Huang and
Shuyan Dong and
Shang{-}Wen Li and
Shinji Watanabe and
Abdelrahman Mohamed and
Hung{-}yi Lee},
title = {{SUPERB:} Speech processing Universal PERformance Benchmark},
journal = {CoRR},
volume = {abs/2105.01051},
year = {2021},
url = {https://arxiv.org/abs/2105.01051},
archivePrefix = {arXiv},
eprint = {2105.01051},
timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Note that each SUPERB dataset has its own citation. Please see the source to see
the correct citation for each contained dataset.
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) and [@anton-l](https://github.com/anton-l) for adding this dataset.
|
false | # Dataset Card for Human parsing data (ATR)
## 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 dataset has 17,706 images and mask pairs. It is just a copy of
[Deep Human Parsing](https://github.com/lemondan/HumanParsing-Dataset) ATR dataset. The mask labels are:
"0": "Background",
"1": "Hat",
"2": "Hair",
"3": "Sunglasses",
"4": "Upper-clothes",
"5": "Skirt",
"6": "Pants",
"7": "Dress",
"8": "Belt",
"9": "Left-shoe",
"10": "Right-shoe",
"11": "Face",
"12": "Left-leg",
"13": "Right-leg",
"14": "Left-arm",
"15": "Right-arm",
"16": "Bag",
"17": "Scarf"
### 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
```bibtex
@ARTICLE{ATR, author={Xiaodan Liang and Si Liu and Xiaohui Shen and Jianchao Yang and Luoqi Liu and Jian Dong and Liang Lin and Shuicheng Yan}, journal={Pattern Analysis and Machine Intelligence, IEEE Transactions on}, title={Deep Human Parsing with Active Template Regression}, year={2015}, volume={37}, number={12}, pages={2402-2414}, doi={10.1109/TPAMI.2015.2408360}, ISSN={0162-8828}, month={Dec}}
@InProceedings{CO-CNN, author={Xiaodan Liang and Chunyan Xu and Xiaohui Shen and Jianchao Yang and Si Liu and Jinhui Tang and Liang Lin and Shuicheng Yan}, journal ={Pattern Analysis and Machine Intelligence, IEEE Transactions on}, title={ICCV}, year={2015}, }
``` |
true |
# Dataset Card for Allociné
## 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:** [Allociné dataset repository](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/tree/master/allocine_dataset)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Théophile Blard](mailto:theophile.blard@gmail.com)
### Dataset Summary
The Allociné dataset is a French-language dataset for sentiment analysis. The texts are movie reviews written between 2006 and 2020 by members of the [Allociné.fr](https://www.allocine.fr/) community for various films. It contains 100k positive and 100k negative reviews divided into train (160k), validation (20k), and test (20k).
### Supported Tasks and Leaderboards
- `text-classification`, `sentiment-classification`: The dataset can be used to train a model for sentiment classification. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset. A BERT-based model, [tf-allociné](https://huggingface.co/tblard/tf-allocine), achieves 97.44% accuracy on the test set.
### Languages
The text is in French, as spoken by users of the [Allociné.fr](https://www.allocine.fr/) website. The BCP-47 code for French is fr.
## Dataset Structure
### Data Instances
Each data instance contains the following features: _review_ and _label_. In the Hugging Face distribution of the dataset, the _label_ has 2 possible values, _0_ and _1_, which correspond to _negative_ and _positive_ respectively. See the [Allociné corpus viewer](https://huggingface.co/datasets/viewer/?dataset=allocine) to explore more examples.
An example from the Allociné train set looks like the following:
```
{'review': 'Premier film de la saga Kozure Okami, "Le Sabre de la vengeance" est un très bon film qui mêle drame et action, et qui, en 40 ans, n'a pas pris une ride.',
'label': 1}
```
### Data Fields
- 'review': a string containing the review text
- 'label': an integer, either _0_ or _1_, indicating a _negative_ or _positive_ review, respectively
### Data Splits
The Allociné dataset has 3 splits: _train_, _validation_, and _test_. The splits contain disjoint sets of movies. The following table contains the number of reviews in each split and the percentage of positive and negative reviews.
| Dataset Split | Number of Instances in Split | Percent Negative Reviews | Percent Positive Reviews |
| ------------- | ---------------------------- | ------------------------ | ------------------------ |
| Train | 160,000 | 49.6% | 50.4% |
| Validation | 20,000 | 51.0% | 49.0% |
| Test | 20,000 | 52.0% | 48.0% |
## Dataset Creation
### Curation Rationale
The Allociné dataset was developed to support large-scale sentiment analysis in French. It was released alongside the [tf-allociné](https://huggingface.co/tblard/tf-allocine) model and used to compare the performance of several language models on this task.
### Source Data
#### Initial Data Collection and Normalization
The reviews and ratings were collected using a list of [film page urls](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_films_urls.txt) and the [allocine_scraper.py](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_scraper.py) tool. Up to 30 reviews were collected for each film.
The reviews were originally labeled with a rating from 0.5 to 5.0 with a step of 0.5 between each rating. Ratings less than or equal to 2 are labeled as negative and ratings greater than or equal to 4 are labeled as positive. Only reviews with less than 2000 characters are included in the dataset.
#### Who are the source language producers?
The dataset contains movie reviews produced by the online community of the [Allociné.fr](https://www.allocine.fr/) website.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Reviewer usernames or personal information were not collected with the reviews, but could potentially be recovered. The content of each review may include information and opinions about the film's actors, film crew, and plot.
## Considerations for Using the Data
### Social Impact of Dataset
Sentiment classification is a complex task which requires sophisticated language understanding skills. Successful models can support decision-making based on the outcome of the sentiment analysis, though such models currently require a high degree of domain specificity.
It should be noted that the community represented in the dataset may not represent any downstream application's potential users, and the observed behavior of a model trained on this dataset may vary based on the domain and use case.
### Discussion of Biases
The Allociné website lists a number of topics which violate their [terms of service](https://www.allocine.fr/service/conditions.html#charte). Further analysis is needed to determine the extent to which moderators have successfully removed such content.
### Other Known Limitations
The limitations of the Allociné dataset have not yet been investigated, however [Staliūnaitė and Bonfil (2017)](https://www.aclweb.org/anthology/W17-5410.pdf) detail linguistic phenomena that are generally present in sentiment analysis but difficult for models to accurately label, such as negation, adverbial modifiers, and reviewer pragmatics.
## Additional Information
### Dataset Curators
The Allociné dataset was collected by Théophile Blard.
### Licensing Information
The Allociné dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT).
### Citation Information
> Théophile Blard, French sentiment analysis with BERT, (2020), GitHub repository, <https://github.com/TheophileBlard/french-sentiment-analysis-with-bert>
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@TheophileBlard](https://github.com/TheophileBlard), [@lewtun](https://github.com/lewtun) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset. |
false |
## Dataset Description
- **Repository:** [SLED Github repository](https://github.com/Mivg/SLED)
- **Paper:** [Efficient Long-Text Understanding with Short-Text Models
](https://arxiv.org/pdf/2208.00748.pdf)
# Dataset Card for SCROLLS
## Overview
This dataset is based on the [SCROLLS](https://huggingface.co/datasets/tau/scrolls) dataset ([paper](https://arxiv.org/pdf/2201.03533.pdf)), the [SQuAD 1.1](https://huggingface.co/datasets/squad) dataset and the [HotpotQA](https://huggingface.co/datasets/hotpot_qa) dataset.
It doesn't contain any unpblished data, but includes the configuration needed for the [Efficient Long-Text Understanding with Short-Text Models
](https://arxiv.org/pdf/2208.00748.pdf) paper.
## Tasks
The tasks included are:
#### GovReport ([Huang et al., 2021](https://arxiv.org/pdf/2104.02112.pdf))
GovReport is a summarization dataset of reports addressing various national policy issues published by the
Congressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary.
The reports and their summaries are longer than their equivalents in other popular long-document summarization datasets;
for example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively.
#### SummScreenFD ([Chen et al., 2021](https://arxiv.org/pdf/2104.07091.pdf))
SummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones).
Given a transcript of a specific episode, the goal is to produce the episode's recap.
The original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts.
For SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows,
making it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows.
Community-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze.
#### QMSum ([Zhong et al., 2021](https://arxiv.org/pdf/2104.05938.pdf))
QMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains.
The corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control,
and committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues.
Annotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions,
while ensuring that the relevant text for answering each query spans at least 200 words or 10 turns.
#### NarrativeQA ([Kočiský et al., 2021](https://arxiv.org/pdf/1712.07040.pdf))
NarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites.
Annotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs,
resulting in about 30 questions and answers for each of the 1,567 books and scripts.
They were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast.
Each question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical).
#### Qasper ([Dasigi et al., 2021](https://arxiv.org/pdf/2105.03011.pdf))
Qasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC).
Questions were written by NLP practitioners after reading only the title and abstract of the papers,
while another set of NLP practitioners annotated the answers given the entire document.
Qasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones.
#### QuALITY ([Pang et al., 2021](https://arxiv.org/pdf/2112.08608.pdf))
QuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg,
the Open American National Corpus, and more.
Experienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them,
human annotators must read large portions of the given document.
Reference answers were then calculated using the majority vote between of the annotators and writer's answers.
To measure the difficulty of their questions, Pang et al. conducted a speed validation process,
where another set of annotators were asked to answer questions given only a short period of time to skim through the document.
As a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer.
#### ContractNLI ([Koreeda and Manning, 2021](https://arxiv.org/pdf/2110.01799.pdf))
Contract NLI is a natural language inference dataset in the legal domain.
Given a non-disclosure agreement (the premise), the task is to predict whether a particular legal statement (the hypothesis) is entailed, not entailed (neutral), or cannot be entailed (contradiction) from the contract.
The NDAs were manually picked after simple filtering from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) and Google.
The dataset contains a total of 607 contracts and 17 unique hypotheses, which were combined to produce the dataset's 10,319 examples.
#### SQuAD 1.1 ([Rajpurkar et al., 2016](https://arxiv.org/pdf/1606.05250.pdf))
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.
#### HotpotQA ([Yang et al., 2018](https://arxiv.org/pdf/1809.09600.pdf))
HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features:
(1) the questions require finding and reasoning over multiple supporting documents to answer;
(2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas;
(3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervisionand explain the predictions;
(4) we offer a new type of factoid comparison questions to testQA systems’ ability to extract relevant facts and perform necessary comparison.
## Data Fields
All the datasets in the benchmark are in the same input-output format
- `input`: a `string` feature. The input document.
- `input_prefix`: an optional `string` feature, for the datasets containing prefix (e.g. question)
- `output`: a `string` feature. The target.
- `id`: a `string` feature. Unique per input.
- `pid`: a `string` feature. Unique per input-output pair (can differ from 'id' in NarrativeQA and Qasper, where there is more then one valid target).
The dataset that contain `input_prefix` are:
- SQuAD - the question
- HotpotQA - the question
- qmsum - the query
- qasper - the question
- narrative_qa - the question
- quality - the question + the four choices
- contract_nli - the hypothesis
## Controlled experiments
To test multiple properties of SLED, we modify SQuAD 1.1 [Rajpurkar et al., 2016](https://arxiv.org/pdf/1606.05250.pdf)
and HotpotQA [Yang et al., 2018](https://arxiv.org/pdf/1809.09600.pdf) to create a few controlled experiments settings.
Those are accessible via the following configurations:
- squad - Contains the original version of SQuAD 1.1 (question + passage)
- squad_ordered_distractors - For each example, 9 random distrctor passages are concatenated (separated by '\n')
- squad_shuffled_distractors - For each example, 9 random distrctor passages are added (separated by '\n'), and jointly the 10 passages are randomly shuffled
- hotpotqa - A clean version of HotpotQA, where each input contains only the two gold passages (separated by '\n')
- hotpotqa_second_only - In each example, the input contains only the second gold passage
## Citation
If you use this dataset, **please make sure to cite all the original dataset papers as well SCROLLS.** [[bibtex](https://drive.google.com/uc?export=download&id=1IUYIzQD9DPsECw0JWkwk4Ildn8JOMtuU)]
```
@inproceedings{Ivgi2022EfficientLU,
title={Efficient Long-Text Understanding with Short-Text Models},
author={Maor Ivgi and Uri Shaham and Jonathan Berant},
year={2022}
}
``` |
true |
# Dataset Card for [More Information Needed]
## 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/aliannejadi/ClariQ
- **Repository:** https://github.com/aliannejadi/ClariQ
- **Paper:** https://arxiv.org/abs/2009.11352
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
The Conv AI 3 challenge is organized as part of the Search-oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In Information Retrieval (IR) settings such a situation is handled mainly through the diversification of search result page. It is however much more challenging in dialogue settings. Hence, we aim to study the following situation for dialogue settings:
- a user is asking an ambiguous question (where ambiguous question is a question to which one can return > 1 possible answers)
- the system must identify that the question is ambiguous, and, instead of trying to answer it directly, ask a good clarifying question.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are a few examples from the dataset:
```
{'topic_id': 8,
'facet_id': 'F0968',
'initial_request': 'I want to know about appraisals.',
'topic_desc': 'Find information about the appraisals in nearby companies.',
'clarification_need': 2,
'question_id': 'F0001',
'question': 'are you looking for a type of appraiser',
'answer': 'im looking for nearby companies that do home appraisals',
'facet_desc': 'Get the TYPE of Appraisals'
'conversation_context': [],
'context_id': 968}
```
```
{'topic_id': 8,
'facet_id': 'F0969',
'initial_request': 'I want to know about appraisals.',
'topic_desc': 'Find information about the type of appraisals.',
'clarification_need': 2,
'question_id': 'F0005',
'question': 'are you looking for a type of appraiser',
'facet_desc': 'Get the TYPE of Appraisals'
'answer': 'yes jewelry',
'conversation_context': [],
'context_id': 969}
```
```
{'topic_id': 293,
'facet_id': 'F0729',
'initial_request': 'Tell me about the educational advantages of social networking sites.',
'topic_desc': 'Find information about the educational benefits of the social media sites',
'clarification_need': 2,
'question_id': 'F0009'
'question': 'which social networking sites would you like information on',
'answer': 'i don have a specific one in mind just overall educational benefits to social media sites',
'facet_desc': 'Detailed information about the Networking Sites.'
'conversation_context': [{'question': 'what level of schooling are you interested in gaining the advantages to social networking sites', 'answer': 'all levels'}, {'question': 'what type of educational advantages are you seeking from social networking', 'answer': 'i just want to know if there are any'}],
'context_id': 976573}
```
### Data Fields
- `topic_id`: the ID of the topic (`initial_request`).
- `initial_request`: the query (text) that initiates the conversation.
- `topic_desc`: a full description of the topic as it appears in the TREC Web Track data.
- `clarification_need`: a label from 1 to 4, indicating how much it is needed to clarify a topic. If an `initial_request` is self-contained and would not need any clarification, the label would be 1. While if a `initial_request` is absolutely ambiguous, making it impossible for a search engine to guess the user's right intent before clarification, the label would be 4.
- `facet_id`: the ID of the facet.
- `facet_desc`: a full description of the facet (information need) as it appears in the TREC Web Track data.
- `question_id`: the ID of the question..
- `question`: a clarifying question that the system can pose to the user for the current topic and facet.
- `answer`: an answer to the clarifying question, assuming that the user is in the context of the current row (i.e., the user's initial query is `initial_request`, their information need is `facet_desc`, and `question` has been posed to the user).
### 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
@misc{aliannejadi2020convai3,
title={ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)},
author={Mohammad Aliannejadi and Julia Kiseleva and Aleksandr Chuklin and Jeff Dalton and Mikhail Burtsev},
year={2020},
eprint={2009.11352},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. |
false |
# Dataset Card for GEM/web_nlg
## Dataset Description
- **Homepage:** https://webnlg-challenge.loria.fr/
- **Repository:** https://gitlab.com/shimorina/webnlg-dataset
- **Paper:** http://www.aclweb.org/anthology/P17-1017, [WebNLG Challenge 2017 Report
- **Leaderboard:** https://beng.dice-research.org/gerbil/
- **Point of Contact:** [Needs More Information]
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/web_nlg).
### Dataset Summary
WebNLG is a bi-lingual dataset (English, Russian) of parallel DBpedia triple sets and short texts that cover about 450 different DBpedia properties. The WebNLG data was originally created to promote the development of RDF verbalisers able to generate short text and to handle micro-planning (i.e., sentence segmentation and ordering, referring expression generation, aggregation); the goal of the task is to generate texts starting from 1 to 7 input triples which have entities in common (so the input is actually a connected Knowledge Graph). The dataset contains about 17,000 triple sets and 45,000 crowdsourced texts in English, and 7,000 triples sets and 19,000 crowdsourced texts in Russian. A challenging test set section with entities and/or properties that have not been seen at training time is available.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/web_nlg')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/web_nlg).
#### website
[Website](https://webnlg-challenge.loria.fr/)
#### paper
[First Dataset Release](http://www.aclweb.org/anthology/P17-1017), [WebNLG Challenge 2017 Report](https://www.aclweb.org/anthology/W17-3518/), [WebNLG Challenge 2020 Report](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf)
#### authors
The principle curator of the dataset is Anastasia Shimorina (Université de Lorraine / LORIA, France). Throughout the WebNLG releases, several people contributed to their construction: Claire Gardent (CNRS / LORIA, France), Shashi Narayan (Google, UK), Laura Perez-Beltrachini (University of Edinburgh, UK), Elena Khasanova, and Thiago Castro Ferreira (Federal University of Minas Gerais, Brazil).
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](https://webnlg-challenge.loria.fr/)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Gitlab](https://gitlab.com/shimorina/webnlg-dataset)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[First Dataset Release](http://www.aclweb.org/anthology/P17-1017), [WebNLG Challenge 2017 Report](https://www.aclweb.org/anthology/W17-3518/), [WebNLG Challenge 2020 Report](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
Initial release of the dataset:
```
@inproceedings{gardent2017creating,
author = "Gardent, Claire
and Shimorina, Anastasia
and Narayan, Shashi
and Perez-Beltrachini, Laura",
title = "Creating Training Corpora for NLG Micro-Planners",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
year = "2017",
publisher = "Association for Computational Linguistics",
pages = "179--188",
location = "Vancouver, Canada",
doi = "10.18653/v1/P17-1017",
url = "http://www.aclweb.org/anthology/P17-1017"
}
```
The latest version 3.0:
```
@inproceedings{castro-ferreira20:bilin-bi-direc-webnl-shared,
title={The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task Overview and Evaluation Results (WebNLG+ 2020)},
author={Castro Ferreira, Thiago and
Gardent, Claire and
Ilinykh, Nikolai and
van der Lee, Chris and
Mille, Simon and
Moussallem, Diego and
Shimorina, Anastasia},
booktitle = {Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+ 2020)},
pages = "55--76",
year = 2020,
address = {Dublin, Ireland (Virtual)},
publisher = {Association for Computational Linguistics}}
```
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
webnlg-challenge@inria.fr
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
yes
#### Leaderboard Link
<!-- info: Provide a link to the leaderboard. -->
<!-- scope: periscope -->
[Website](https://beng.dice-research.org/gerbil/)
#### Leaderboard Details
<!-- info: Briefly describe how the leaderboard evaluates models. -->
<!-- scope: microscope -->
The model outputs are evaluated against the crowdsourced references; the leaderboard reports BLEU-4, METEOR, chrF++, TER, BERTScore and BLEURT scores.
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
yes
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Russian`, `English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The WebNLG dataset was created to promote the development (_i_) of RDF verbalisers and (_ii_) of microplanners able to handle a wide range of linguistic constructions. The dataset aims at covering knowledge in different domains ("categories"). The same properties and entities can appear in several categories.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
A model should verbalize all and only the provided input triples in natural language.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Université de Lorraine / LORIA, France, CNRS / LORIA, France, University of Edinburgh, UK, Federal University of Minas Gerais, Brazil
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
The principle curator of the dataset is Anastasia Shimorina (Université de Lorraine / LORIA, France). Throughout the WebNLG releases, several people contributed to their construction: Claire Gardent (CNRS / LORIA, France), Shashi Narayan (Google, UK), Laura Perez-Beltrachini (University of Edinburgh, UK), Elena Khasanova, and Thiago Castro Ferreira (Federal University of Minas Gerais, Brazil).
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
The dataset construction was funded by the French National Research Agency (ANR).
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Simon Mille and Sebastian Gehrmann added the dataset and wrote the data card.
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
See [official documentation](https://webnlg-challenge.loria.fr/docs/).
`entry`: a data instance of the benchmark. Each entry has five attributes: a DBpedia category (`category`), entry ID (`eid`), shape, shape type, and triple set size (`size`).
- `shape`: a string representation of the RDF tree with nested parentheses where `X` is a node (see [Newick tree format](https://en.wikipedia.org/wiki/Newick_format)).
- `shape_type`: a type of the tree shape. We [identify](https://www.aclweb.org/anthology/C16-1141.pdf) three types of tree shapes:
* `chain` (the object of one triple is the subject of the other);
* `sibling` (triples with a shared subject);
* `mixed` (both `chain` and `sibling` types present).
- `eid`: an entry ID. It is unique only within a category and a size.
- `category`: a DBpedia category (Astronaut, City, MusicalWork, Politician, etc.).
- `size`: the number of RDF triples in a set. Ranges from 1 to 7.
Each `entry` has three fields: `originaltripleset`, `modifiedtripleset`, and `lexs`.
`originaltripleset`: a set of RDF triples as extracted from [DBpedia](https://wiki.dbpedia.org/). Each set of RDF triples is a tree. Triples have the subject-predicate-object structure.
`modifiedtripleset`: a set of RDF triples as presented to crowdworkers (for more details on modifications, see below).
Original and modified triples serve different purposes: the original triples — to link data to a knowledge base (DBpedia), whereas the modified triples — to ensure consistency and homogeneity throughout the data. To train models, the modified triples should be used.
`lexs` (shortened for lexicalisations): a natural language text verbalising the triples. Each lexicalisation has two attributes: a comment (`comment`), and a lexicalisation ID (`lid`). By default, comments have the value `good`, except rare cases when they were manually marked as `toFix`. That was done during the corpus creation, when it was seen that a lexicalisation did not exactly match a triple set.
Russian data has additional optional fields comparing to English:
`<dbpedialinks>`: RDF triples extracted from DBpedia between English and Russian entities by means of the property `sameAs`.
`<links>`: RDF triples created manually for some entities to serve as pointers to translators. There are two types of them:
* with `sameAs` (`Spaniards | sameAs | испанцы`)
* with `includes` (`Tomatoes, guanciale, cheese, olive oil | includes | гуанчиале`). Those were mostly created for string literals to translate some parts of them.
Lexicalisations in the Russian WebNLG have a new parameter `lang` (values: `en`, `ru`) because original English texts were kept in the Russian version (see the example above).
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
"entry": {
"category": "Company",
"size": "4",
"shape": "(X (X) (X) (X) (X))",
"shape_type": "sibling",
"eid": "Id21",
"lexs": [
{
"comment": "good",
"lex": "Trane, which was founded on January 1st 1913 in La Crosse, Wisconsin, is based in Ireland. It has 29,000 employees.",
"lid": "Id1"
}
],
"modifiedtripleset": [
{
"subject": "Trane",
"property": "foundingDate",
"object": "1913-01-01"
},
{
"subject": "Trane",
"property": "location",
"object": "Ireland"
},
{
"subject": "Trane",
"property": "foundationPlace",
"object": "La_Crosse,_Wisconsin"
},
{
"subject": "Trane",
"property": "numberOfEmployees",
"object": "29000"
}
],
"originaltriplesets": {
"originaltripleset": [
{
"subject": "Trane",
"property": "foundingDate",
"object": "1913-01-01"
},
{
"subject": "Trane",
"property": "location",
"object": "Ireland"
},
{
"subject": "Trane",
"property": "foundationPlace",
"object": "La_Crosse,_Wisconsin"
},
{
"subject": "Trane",
"property": "numberOfEmployees",
"object": "29000"
}
]
}
}
}
```
The XML-formatted example is [here](https://webnlg-challenge.loria.fr/docs/#example).
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
| English (v3.0) | Train | Dev | Test |
|-----------------|--------|-------|-------|
| **triple sets** | 13,211 | 1,667 | 1,779 |
| **texts** | 35,426 | 4,464 | 5,150 |
|**properties** | 372 | 290 | 220 |
| Russian (v3.0) | Train | Dev | Test |
|-----------------|--------|-------|-------|
| **triple sets** | 5,573 | 790 | 1,102 |
| **texts** | 14,239 | 2,026 | 2,780 |
|**properties** | 226 | 115 | 192 |
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
Due to the constrained generation task, this dataset can be used to evaluate very specific and narrow generation capabilities.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
yes
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The RDF-triple format is unique to WebNLG.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
surface realization
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
No changes to the main content of the dataset. The [version 3.0](https://gitlab.com/shimorina/webnlg-dataset/-/tree/master/release_v3.0) of the dataset is used.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
yes
#### Split Information
<!-- info: Describe how the new splits were created -->
<!-- scope: periscope -->
23 special test sets for WebNLG were added to the GEM evaluation suite, 12 for English and 11 for Russian.
For both languages, we created subsets of the training and development sets of ~500 randomly selected inputs each. The inputs were sampled proportionally from each category.
Two types of transformations have been applied to WebNLG: (i) input scrambling (English and Russian) and (ii) numerical value replacements (English); in both cases, a subset of about 500 inputs was randomly selected. For (i), the order of the triples was randomly reassigned (each triple kept the same Subject-Property-Object internal order). For (ii), the change was performed respecting the format of the current cardinal value (e.g., alpha, integer, or floating-point) and replacing it with a new random value. The new number is lower-bounded between zero and upper bounded to be within to the highest power of 10 unit for the given value (e.g., replacing 54 would result in a random value between 0-100). Floating values maintain the degree of precision.
For both languages, we did identify different subsets of the test set that we could compare to each other so that we would have a better understanding of the results. There are currently 8 selections that we have made:
Selection 1 (size): input length. This selection corresponds to the number of predicates in the input. By comparing inputs of different lengths, we can see to what extent NLG systems are able to handle different input sizes. The table below provides the relevant frequencies. Please be aware that comparing selections with fewer than 100 items may result in unreliable comparisons.
| Input length | Frequency English | Frequency Russian |
|----------------|-------------------|-------------------|
| 1 | 369 | 254 |
| 2 | 349 | 200 |
| 3 | 350 | 214 |
| 4 | 305 | 214 |
| 5 | 213 | 159 |
| 6 | 114 | 32 |
| 7 | 79 | 29 |
Selection 2 (frequency): seen/unseen single predicates. This selection corresponds to the inputs with only one predicate. We compare which predicates are seen/unseen in the training data. The table below provides the relevant frequencies. Note that the comparison is only valid for English. Not for Russian, since there is only one example of unseen single predicates.
| _ in training | Frequency English | Frequency Russian |
|---------------|-------------------|-------------------|
| Seen | 297 | 253 |
| Unseen | 72 | 1 |
Selection 3 (frequency): seen/unseen combinations of predicates. This selection checks for all combinations of predicates whether that combination has been seen in the training data. For example: if the combination of predicates A and B is seen, that means that there is an input in the training data consisting of two triples, where one triple uses predicate A and the other uses predicate B. If the combination is unseen, then the converse is true. The table below provides the relevant frequencies.
| _ in training | Frequency English | Frequency Russian |
|---------------|-------------------|-------------------|
| unseen | 1295 | 354 |
| seen | 115 | 494 |
Selection 4 (frequency): seen/unseen arguments. This selection checks for all input whether or not all arg1s and arg2s in the input have been seen during the training phase. For this selection, *Seen* is the default. Only if all arg1 instances for a particular input are unseen, do we count the arg1s of the input as unseen. The same holds for arg2. So "seen" here really means that at least some of the arg1s or arg2s are seen in the input. The table below provides the relevant frequencies. Note that the comparison is only valid for English. Not for Russian, since there are very few examples of unseen combinations of predicates.
| Arguments seen in training? | Frequency English | Frequency Russian |
|-----------------------------|-------------------|-------------------|
| both_seen | 518 | 1075 |
| both_unseen | 1177 | 4 |
| arg1_unseen | 56 | 19 |
| arg2_unseen | 28 | 4 |
Selection 5 (shape): repeated subjects. For this selection, the subsets are based on the times a subject is repeated in the input; it only takes into account the maximum number of times a subject is repeated, that is, if in one input a subject appears 3 times and a different subject 2 times, this input will be in the "3_subjects_same' split. Unique_subjects means all subjects are different.
| Max num. of repeated subjects | Frequency English | Frequency Russian |
|-------------------------------|-------------------|-------------------|
| unique_subjects | 453 | 339 |
| 2_subjects_same | 414 | 316 |
| 3_subjects_same | 382 | 217 |
| 4_subjects_same | 251 | 143 |
| 5_subjects_same | 158 | 56 |
| 6_subjects_same | 80 | 19 |
| 7_subjects_same | 41 | 12 |
Selection 6 (shape): repeated objects. Same as for subjects above, but for objects. There are much less cases of repeated objects, so there are only two categories for this selection, unique_objects and some_objects_repeated; for the latter, we have up to 3 coreferring objects in English, and XXX in Russian.
| Max num. of repeated objects | Frequency English | Frequency Russian |
|------------------------------|-------------------|-------------------|
| unique_objects | 1654 | 1099 |
| some_objects_same | 125 | 3 |
Selection 7 (shape): repeated properties. Same as for objects above, but for properties; up to two properties can be the same in English, up to XXX in Russian.
| Max num. of repeated properties | Frequency English | Frequency Russian |
|---------------------------------|-------------------|-------------------|
| unique_properties | 1510 | 986 |
| some_properties_same | 269 | 116 |
Selection 8 (shape): entities that appear both as subject and object. For this selection, we grouped together the inputs in which no entity is found as both subject and object, and on the other side inputs in which one or more entity/ies appear both as subject and as object. We found up to two such entities per input in English, and up to XXX in Russian.
| Max num. of objects and subjects in common | Frequency English | Frequency Russian |
|--------------------------------------------|-------------------|-------------------|
| unique_properties | 1322 | 642 |
| some_properties_same | 457 | 460 |
#### Split Motivation
<!-- info: What aspects of the model's generation capacities were the splits created to test? -->
<!-- scope: periscope -->
Robustness
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
Dataset construction: [main dataset paper](https://www.aclweb.org/anthology/P17-1017/), [RDF triple extraction](https://www.aclweb.org/anthology/C16-1141/), [Russian translation](https://www.aclweb.org/anthology/W19-3706/)
WebNLG Challenge 2017: [webpage](https://webnlg-challenge.loria.fr/challenge_2017/), [paper](https://www.aclweb.org/anthology/W17-3518/)
WebNLG Challenge 2020: [webpage](https://webnlg-challenge.loria.fr/challenge_2020/), [paper](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf)
Enriched version of WebNLG: [repository](https://github.com/ThiagoCF05/webnlg), [paper](https://www.aclweb.org/anthology/W18-6521/)
Related research papers: [webpage](https://webnlg-challenge.loria.fr/research/)
## Previous Results
### Previous Results
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
For both languages, the participating systems are automatically evaluated in a multi-reference scenario. Each English hypothesis is compared to a maximum of 5 references, and each Russian one to a maximum of 7 references. On average, English data has 2.89 references per test instance, and Russian data has 2.52 references per instance.
In a human evaluation, example are uniformly sampled across size of triple sets and the following dimensions are assessed (on MTurk and Yandex.Toloka):
1. Data Coverage: Does the text include descriptions of all predicates presented in the data?
2. Relevance: Does the text describe only such predicates (with related subjects and objects), which are found in the data?
3. Correctness: When describing predicates which are found in the data, does the text mention correct the objects and adequately introduces the subject for this specific predicate?
4. Text Structure: Is the text grammatical, well-structured, written in acceptable English language?
5. Fluency: Is it possible to say that the text progresses naturally, forms a coherent whole and it is easy to understand the text?
For additional information like the instructions, we refer to the original paper.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
We evaluated a wide range of models as part of the GEM benchmark.
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
Results can be found on the [GEM website](https://gem-benchmark.com/results).
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
yes - related tasks
#### Social Impact Observations
<!-- info: Did any of these previous uses result in observations about the social impact of the systems? In particular, has there been work outlining the risks and limitations of the system? Provide links and descriptions here. -->
<!-- scope: microscope -->
We do not foresee any negative social impact in particular from this dataset or task.
Positive outlooks: Being able to generate good quality text from RDF data would permit, e.g., making this data more accessible to lay users, enriching existing text with information drawn from knowledge bases such as DBpedia or describing, comparing and relating entities present in these knowledge bases.
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
yes
#### Links and Summaries of Analysis Work
<!-- info: Provide links to and summaries of works analyzing these biases. -->
<!-- scope: microscope -->
This dataset is created using DBpedia RDF triples which naturally exhibit biases that have been found to exist in Wikipedia such as some forms of, e.g., gender bias.
The choice of [entities](https://gitlab.com/shimorina/webnlg-dataset/-/blob/master/supplementary/entities_dict.json), described by RDF trees, was not controlled. As such, they may contain gender biases; for instance, all the astronauts described by RDF triples are male. Hence, in texts, pronouns _he/him/his_ occur more often. Similarly, entities can be related to the Western culture more often than to other cultures.
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
In English, the dataset is limited to the language that crowdraters speak. In Russian, the language is heavily biased by the translationese of the translation system that is post-edited.
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
There is no PII in this dataset.
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`non-commercial use only`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
The quality of the crowdsourced references is limited, in particular in terms of fluency/naturalness of the collected texts.
Russian data was machine-translated and then post-edited by crowdworkers, so some examples may still exhibit issues related to bad translations.
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Only a limited number of domains are covered in this dataset. As a result, it cannot be used as a general-purpose realizer.
|
true |
# Dataset Card for "FairLex"
## 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/coastalcph/fairlex
- **Repository:** https://github.com/coastalcph/fairlex
- **Paper:** https://aclanthology.org/2022.acl-long.301/
- **Leaderboard:** -
- **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk)
### Dataset Summary
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian, and Chinese), and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, CAIL). We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads. We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019). For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese CAIL). [[Link to Models](https://huggingface.co/models?search=fairlex)]
### Supported Tasks and Leaderboards
The supported tasks are the following:
<table>
<tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Language</td><td>Task Type</td><td>Classes</td><tr>
<tr><td>ECtHR</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>en</td><td>Multi-label classification</td><td>10+1</td></tr>
<tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>en</td><td>Multi-class classification</td><td>11</td></tr>
<tr><td>FSCS</td><td> <a href="https://aclanthology.org/2021.nllp-1.3/">Niklaus et al. (2021)</a></td><td>Swiss Law</td><td>en, fr , it</td><td>Binary classification</td><td>2</td></tr>
<tr><td>CAIL</td><td> <a href="https://arxiv.org/abs/2103.13868">Wang et al. (2021)</a></td><td>Chinese Law</td><td>zh</td><td>Multi-class classification</td><td>6</td></tr>
</table>
#### ecthr
The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). We use the dataset of Chalkidis et al. (2021), which contains 11K cases from ECtHR's public database.
Each case is mapped to *articles* of the ECHR that were violated (if any). This is a multi-label text classification task. Given the facts of a case, the goal is to predict the ECHR articles that were violated, if any, as decided (ruled) by the court. The cases are chronologically split into training (9k, 2001--16), development (1k, 2016--17), and test (1k, 2017--19) sets.
To facilitate the study of the fairness of text classifiers, we record for each case the following attributes: (a) The _defendant states_, which are the European states that allegedly violated the ECHR. The defendant states for each case is a subset of the 47 Member States of the Council of Europe; To have statistical support, we group defendant states in two groups:
Central-Eastern European states, on one hand, and all other states, as classified by the EuroVoc thesaurus. (b) The _applicant's age_ at the time of the decision. We extract the birth year of the applicant from the case facts, if possible, and classify its case in an age group (<=35, <=64, or older); and (c) the _applicant's gender_, extracted from the facts, if possible based on pronouns, classified in two categories (male, female).
#### scotus
The US Supreme Court (SCOTUS) is the highest federal court in the United States of America and generally hears only the most controversial or otherwise complex cases that have not been sufficiently well solved by lower courts.
We combine information from SCOTUS opinions with the Supreme Court DataBase (SCDB) (Spaeth, 2020). SCDB provides metadata (e.g., date of publication, decisions, issues, decision directions, and many more) for all cases. We consider the available 14 thematic issue areas (e.g, Criminal Procedure, Civil Rights, Economic Activity, etc.). This is a single-label multi-class document classification task. Given the court's opinion, the goal is to predict the issue area whose focus is on the subject matter of the controversy (dispute). SCOTUS contains a total of 9,262 cases that we split chronologically into 80% for training (7.4k, 1946--1982), 10% for development (914, 1982--1991) and 10% for testing (931, 1991--2016).
From SCDB, we also use the following attributes to study fairness: (a) the _type of respondent_, which is a manual categorization of respondents (defendants) in five categories (person, public entity, organization, facility, and other); and (c) the _direction of the decision_, i.e., whether the decision is liberal, or conservative, provided by SCDB.
#### fscs
The Federal Supreme Court of Switzerland (FSCS) is the last level of appeal in Switzerland and similarly to SCOTUS, the court generally hears only the most controversial or otherwise complex cases which have not been sufficiently well solved by lower courts. The court often focuses only on small parts of the previous decision, where they discuss possible wrong reasoning by the lower court. The Swiss-Judgment-Predict dataset (Niklaus et al., 2021) contains more than 85K decisions from the FSCS written in one of three languages (50K German, 31K French, 4K Italian) from the years 2000 to 2020.
The dataset is not parallel, i.e., all cases are unique and decisions are written only in a single language.
The dataset provides labels for a simplified binary (_approval_, _dismissal_) classification task. Given the facts of the case, the goal is to predict if the plaintiff's request is valid or partially valid. The cases are also chronologically split into training (59.7k, 2000-2014), development (8.2k, 2015-2016), and test (17.4k, 2017-2020) sets.
The dataset provides three additional attributes: (a) the _language_ of the FSCS written decision, in either German, French, or Italian; (b) the _legal area_ of the case (public, penal, social, civil, or insurance law) derived from the chambers where the decisions were heard; and (c) the _region_ that denotes in which federal region was the case originated.
#### cail
The Supreme People's Court of China (CAIL) is the last level of appeal in China and considers cases that originated from the high people's courts concerning matters of national importance. The Chinese AI and Law challenge (CAIL) dataset (Xiao et al., 2018) is a Chinese legal NLP dataset for judgment prediction and contains over 1m criminal cases. The dataset provides labels for *relevant article of criminal code* prediction, *charge* (type of crime) prediction, imprisonment *term* (period) prediction, and monetary *penalty* prediction. The publication of the original dataset has been the topic of an active debate in the NLP community(Leins et al., 2020; Tsarapatsanis and Aletras, 2021; Bender, 2021).
Recently, Wang et al. (2021) re-annotated a subset of approx. 100k cases with demographic attributes. Specifically, the new dataset has been annotated with: (a) the _applicant's gender_, classified in two categories (male, female); and (b) the _region_ of the court that denotes in which out of the 7 provincial-level administrative regions was the case judged. We re-split the dataset chronologically into training (80k, 2013-2017), development (12k, 2017-2018), and test (12k, 2018) sets. In our study, we re-frame the imprisonment _term_ prediction and examine a soft version, dubbed _crime severity_ prediction task, a multi-class classification task, where given the facts of a case, the goal is to predict how severe was the committed crime with respect to the imprisonment term. We approximate crime severity by the length of imprisonment term, split in 6 clusters (0, <=12, <=36, <=60, <=120, >120 months).
### Languages
We consider datasets in English, German, French, Italian, and Chinese.
## Dataset Structure
### Data Instances
#### ecthr
An example of 'train' looks as follows.
```json
{
"text": "1. At the beginning of the events relevant to the application, K. had a daughter, P., and a son, M., born in 1986 and 1988 respectively. ... ",
"labels": [4],
"defendant_state": 1,
"applicant_gender": 0,
"applicant_age": 0
}
```
#### scotus
An example of 'train' looks as follows.
```json
{
"text": "United States Supreme Court MICHIGAN NAT. BANK v. MICHIGAN(1961) No. 155 Argued: Decided: March 6, 1961 </s> R. S. 5219 permits States to tax the shares of national banks, but not at a greater rate than . . . other moneyed capital . . . coming into competition with the business of national banks ...",
"label": 9,
"decision_direction": 0,
"respondent_type": 3
}
```
#### fscs
An example of 'train' looks as follows.
```json
{
"text": "A.- Der 1955 geborene V._ war seit 1. September 1986 hauptberuflich als technischer Kaufmann bei der Firma A._ AG tätig und im Rahmen einer Nebenbeschäftigung (Nachtarbeit) ab Mai 1990 bei einem Bewachungsdienst angestellt gewesen, als er am 10....",
"label": 0,
"decision_language": 0,
"legal_are": 5,
"court_region": 2
}
```
#### cail
An example of 'train' looks as follows.
```json
{
"text": "南宁市兴宁区人民检察院指控，2012年1月1日19时许，被告人蒋满德在南宁市某某路某号某市场内，因经营问题与被害人杨某某发生争吵并推打 ...",
"label": 0,
"defendant_gender": 0,
"court_region": 5
}
```
### Data Fields
#### ecthr_a
- `text`: a `string` feature (factual paragraphs (facts) from the case description).
- `labels`: a list of classification labels (a list of violated ECHR articles, if any). The ECHR articles considered are 2, 3, 5, 6, 8, 9, 11, 14, P1-1.
- `defendant_state`: Defendant State group (C.E. European, Rest of Europe)
- `applicant_gender`: The gender of the applicant (N/A, Male, Female)
- `applicant_age`: The age group of the applicant (N/A, <=35, <=64, or older)
#### scotus
- `text`: a `string` feature (the court opinion).
- `label`: a classification label (the relevant issue area). The issue areas are: (1, Criminal Procedure), (2, Civil Rights), (3, First Amendment), (4, Due Process), (5, Privacy), (6, Attorneys), (7, Unions), (8, Economic Activity), (9, Judicial Power), (10, Federalism), (11, Interstate Relations), (12, Federal Taxation), (13, Miscellaneous), (14, Private Action).
- `respondent_type`: the type of respondent, which is a manual categorization (clustering) of respondents (defendants) in five categories (person, public entity, organization, facility, and other).
- `decision_direction`: the direction of the decision, i.e., whether the decision is liberal, or conservative, provided by SCDB.
#### fscs
- `text`: a `string` feature (an EU law).
- `label`: a classification label (approval or dismissal of the appeal).
- `language`: the language of the FSCS written decision, (German, French, or Italian).
- `legal_area`: the legal area of the case (public, penal, social, civil, or insurance law) derived from the chambers where the decisions were heard.
- `region`: the region that denotes in which federal region was the case originated.
#### cail
- `text`: a `string` feature (the factual description of the case).
- `label`: a classification label (crime severity derived by the imprisonment term).
- `defendant_gender`: the gender of the defendant (Male or Female).
- `court_region`: the region of the court that denotes in which out of the 7 provincial-level administrative regions was the case judged.
### Data Splits
<table>
<tr><td>Dataset </td><td>Training</td><td>Development</td><td>Test</td><td>Total</td></tr>
<tr><td>ECtHR</td><td>9000</td><td>1000</td><td>1000</td><td>11000</td></tr>
<tr><td>SCOTUS</td><td>7417</td><td>914</td><td>931</td><td>9262</td></tr>
<tr><td>FSCS</td><td>59709</td><td>8208</td><td>17357</td><td>85274</td></tr>
<tr><td>CAIL</td><td>80000</td><td>12000</td><td>12000</td><td>104000</td></tr>
</table>
## 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
<table>
<tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Language</td><td>Task Type</td><td>Classes</td><tr>
<tr><td>ECtHR</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>en</td><td>Multi-label classification</td><td>10+1</td></tr>
<tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>en</td><td>Multi-class classification</td><td>14</td></tr>
<tr><td>FSCS</td><td> <a href="https://aclanthology.org/2021.nllp-1.3/">Niklaus et al. (2021)</a></td><td>Swiss Law</td><td>en, fr , it</td><td>Binary classification</td><td>2</td></tr>
<tr><td>CAIL</td><td> <a href="https://arxiv.org/abs/2105.03887">Wang et al. (2021)</a></td><td>Chinese Law</td><td>zh</td><td>Multi-class classification</td><td>6</td></tr>
</table>
#### Initial Data Collection and Normalization
We standardize and put together four datasets: ECtHR (Chalkidis et al., 2021), SCOTUS (Spaeth et al., 2020), FSCS (Niklaus et al., 2021), and CAIL (Xiao et al., 2018; Wang et al., 2021) that are already publicly available.
The benchmark is not a blind stapling of pre-existing resources, we augment previous datasets. In the case of ECtHR, previously unavailable demographic attributes have been released to make the original dataset amenable for fairness research. For SCOTUS, two resources (court opinions with SCDB) have been combined for the very same reason, while the authors provide a manual categorization (clustering) of respondents.
All datasets, except SCOTUS, are publicly available and have been previously published. If datasets or the papers where they were introduced were not compiled or written by the authors, the original work is referenced and authors encourage FairLex users to do so as well. In fact, this work should only be referenced, in addition to citing the original work, when jointly experimenting with multiple FairLex datasets and using the FairLex evaluation framework and infrastructure, or using any newly introduced annotations (ECtHR, SCOTUS). Otherwise only the original work should be cited.
#### 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?
All classification labels rely on legal decisions (ECtHR, FSCS, CAIL), or are part of archival procedures (SCOTUS).
The demographic attributes and other metadata are either provided by the legal databases or have been extracted automatically from the text by means of Regular Expressions.
Consider the **Dataset Description** and **Discussion of Biases** sections, and the original publication for detailed information.
### Personal and Sensitive Information
The data is in general partially anonymized in accordance with the applicable national law. The data is considered to be in the public sphere from a privacy perspective. This is a very sensitive matter, as the courts try to keep a balance between transparency (the public's right to know) and privacy (respect for private and family life).
ECtHR cases are partially annonymized by the court. Its data is processed and made public in accordance with the European Data Protection Law.
SCOTUS cases may also contain personal information and the data is processed and made available by the US Supreme Court, whose proceedings are public. While this ensures compliance with US law, it is very likely that similarly to the ECtHR any processing could be justified by either implied consent or legitimate interest under European law. In FSCS, the names of the parties have been redacted by the courts according to the official guidelines. CAIL cases are also partially anonymized by the courts according to the courts' policy. Its data is processed and made public in accordance with Chinese Law.
## Considerations for Using the Data
### Social Impact of Dataset
This work can help practitioners to build assisting technology for legal professionals - with respect to the legal framework (jurisdiction) they operate -; technology that does not only rely on performance on majority groups but also considering minorities and the robustness of the developed models across them. This is an important application field, where more research should be conducted (Tsarapatsanis and Aletras, 2021) in order to improve legal services and democratize law, but more importantly, highlight (inform the audience on) the various multi-aspect shortcomings seeking a responsible and ethical (fair) deployment of technology.
### Discussion of Biases
The current version of FairLex covers a very small fraction of legal applications, jurisdictions, and protected attributes. The benchmark inevitably cannot cover "_everything in the whole wide (legal) world_" (Raji et al., 2021), but nonetheless, we believe that the published resources will help critical research in the area of fairness.
Some protected attributes within the datasets are extracted automatically, i.e., the gender and the age of the ECtHR dataset, by means of Regular Expressions, or manually clustered by the authors, such as the defendant state in the ECtHR dataset and the respondent attribute in the SCOTUS dataset. Those assumptions and simplifications can hold in an experimental setting only and by no means should be used in real-world applications where some simplifications, e.g., binary gender, would not be appropriate. By no means, do the authors or future users have to endorse the law standards or framework of the examined datasets, to any degree rather than the publication and use of the data.
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Curators
*Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Letizia, Sebastian Felix Schwemer, Anders Søgaard.*
*FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing.*
*2022. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.*
**Note:** The original datasets have been originally curated by others, and further curated (updated) by means of this benchmark.
### Licensing Information
The benchmark is released under a [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. The licensing is compatible with the licensing of former material (remixed, transformed datasets).
### Citation Information
[*Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Letizia, Sebastian Felix Schwemer, Anders Søgaard.*
*FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing.*
*2022. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.*](https://aclanthology.org/2022.acl-long.301/)
```
@inproceedings{chalkidis-etal-2022-fairlex,
author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and
Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders},
title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
year={2022},
address={Dublin, Ireland}
}
```
**Note:** Please consider citing and giving credits to all publications releasing the examined datasets.
### Contributions
Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.
|
false |
# Dataset Card for BrWaC
## 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:** [BrWaC homepage](https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC)
- **Repository:** [BrWaC repository](https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC)
- **Paper:** [The brWaC Corpus: A New Open Resource for Brazilian Portuguese](https://www.aclweb.org/anthology/L18-1686/)
- **Point of Contact:** [Jorge A. Wagner Filho](mailto:jawfilho@inf.ufrgs.br)
### Dataset Summary
The BrWaC (Brazilian Portuguese Web as Corpus) is a large corpus constructed following the Wacky framework,
which was made public for research purposes. The current corpus version, released in January 2017, is composed by
3.53 million documents, 2.68 billion tokens and 5.79 million types. Please note that this resource is available
solely for academic research purposes, and you agreed not to use it for any commercial applications.
Manually download at https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Portuguese
## Dataset Structure
### Data Instances
An example from the BrWaC dataset looks as follows:
```
{
"doc_id": "netg-1afc73",
"text": {
"paragraphs": [
[
"Conteúdo recente"
],
[
"ESPUMA MARROM CHAMADA \"NINGUÉM MERECE\""
],
[
"31 de Agosto de 2015, 7:07 , por paulo soavinski - | No one following this article yet."
],
[
"Visualizado 202 vezes"
],
[
"JORNAL ELETRÔNICO DA ILHA DO MEL"
],
[
"Uma espuma marrom escuro tem aparecido com frequência na Praia de Fora.",
"Na faixa de areia ela aparece disseminada e não chama muito a atenção.",
"No Buraco do Aipo, com muitas pedras, ela aparece concentrada.",
"É fácil saber que esta espuma estranha está lá, quando venta.",
"Pequenos algodões de espuma começam a flutuar no espaço, pertinho da Praia do Saquinho.",
"Quem pode ajudar na coleta deste material, envio a laboratório renomado e pagamento de análises, favor entrar em contato com o site."
]
]
},
"title": "ESPUMA MARROM CHAMADA ‟NINGUÃÂM MERECE‟ - paulo soavinski",
"uri": "http://blogoosfero.cc/ilhadomel/pousadasilhadomel.com.br/espuma-marrom-chamada-ninguem-merece"
}
```
### Data Fields
- `doc_id`: The document ID
- `title`: The document title
- `uri`: URI where the document was extracted from
- `text`: A list of document paragraphs (with a list of sentences in it as a list of strings)
### Data Splits
The data is only split into train set with size of 3530796 samples.
## 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{wagner2018brwac,
title={The brwac corpus: A new open resource for brazilian portuguese},
author={Wagner Filho, Jorge A and Wilkens, Rodrigo and Idiart, Marco and Villavicencio, Aline},
booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
### Contributions
Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset. |
false |
# Dataset Card for "wiki_snippets"
## 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://dumps.wikimedia.org](https://dumps.wikimedia.org)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
Wikipedia version split into plain text snippets for dense semantic indexing.
### 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 2 configurations of the dataset (with 100 snippet passage length and 0 overlap) in
English:
- wiki40b_en_100_0: Wiki-40B
- wikipedia_en_100_0: Wikipedia
### Data Instances
#### wiki40b_en_100_0
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 12.94 GB
- **Total amount of disk used:** 12.94 GB
An example of 'train' looks as follows:
```
{'_id': '{"datasets_id": 0, "wiki_id": "Q1294448", "sp": 2, "sc": 0, "ep": 6, "ec": 610}',
'datasets_id': 0,
'wiki_id': 'Q1294448',
'start_paragraph': 2,
'start_character': 0,
'end_paragraph': 6,
'end_character': 610,
'article_title': 'Ági Szalóki',
'section_title': 'Life',
'passage_text': "Ági Szalóki Life She started singing as a toddler, considering Márta Sebestyén a role model. Her musical background is traditional folk music; she first won recognition for singing with Ökrös in a traditional folk style, and Besh o droM, a Balkan gypsy brass band. With these ensembles she toured around the world from the Montreal Jazz Festival, through Glastonbury Festival to the Théatre de la Ville in Paris, from New York to Beijing.\nSince 2005, she began to pursue her solo career and explore various genres, such as jazz, thirties ballads, or children's songs.\nUntil now, three of her six released albums"}
```
#### wikipedia_en_100_0
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 26.41 GB
- **Total amount of disk used:** 26.41 GB
An example of 'train' looks as follows:
```
{'_id': '{"datasets_id": 0, "wiki_id": "Anarchism", "sp": 0, "sc": 0, "ep": 2, "ec": 129}',
'datasets_id': 0,
'wiki_id': 'Anarchism',
'start_paragraph': 0,
'start_character': 0,
'end_paragraph': 2,
'end_character': 129,
'article_title': 'Anarchism',
'section_title': 'Start',
'passage_text': 'Anarchism is a political philosophy and movement that is sceptical of authority and rejects all involuntary, coercive forms of hierarchy. Anarchism calls for the abolition of the state, which it holds to be unnecessary, undesirable, and harmful. As a historically left-wing movement, placed on the farthest left of the political spectrum, it is usually described alongside communalism and libertarian Marxism as the libertarian wing (libertarian socialism) of the socialist movement, and has a strong historical association with anti-capitalism and socialism. Humans lived in societies without formal hierarchies long before the establishment of formal states, realms, or empires. With the'}
```
### Data Fields
The data fields are the same for all configurations:
- `_id`: a `string` feature.
- `datasets_id`: a `int32` feature.
- `wiki_id`: a `string` feature.
- `start_paragraph`: a `int32` feature.
- `start_character`: a `int32` feature.
- `end_paragraph`: a `int32` feature.
- `end_character`: a `int32` feature.
- `article_title`: a `string` feature.
- `section_title`: a `string` feature.
- `passage_text`: a `string` feature.
### Data Splits
| name | train |
|:-------------------|---------:|
| wiki40b_en_100_0 | 17553713 |
| wikipedia_en_100_0 | 33849898 |
## 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
See licensing information of source datasets.
### Citation Information
Cite source datasets:
- Wiki-40B:
```
@inproceedings{49029,
title = {Wiki-40B: Multilingual Language Model Dataset},
author = {Mandy Guo and Zihang Dai and Denny Vrandecic and Rami Al-Rfou},
year = {2020},
booktitle = {LREC 2020}
}
```
- Wikipedia:
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@yjernite](https://github.com/yjernite) for adding this dataset. |
false |
# GovReport dataset for summarization
Dataset for summarization of long documents.\
Adapted from this [repo](https://github.com/luyang-huang96/LongDocSum) and this [paper](https://arxiv.org/pdf/2104.02112.pdf)\
This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable:
```python
"ccdv/govreport-summarization": ("report", "summary")
```
### Data Fields
- `id`: paper id
- `report`: a string containing the body of the report
- `summary`: a string containing the summary of the report
### Data Splits
This dataset has 3 splits: _train_, _validation_, and _test_. \
Token counts with a RoBERTa tokenizer.
| Dataset Split | Number of Instances | Avg. tokens |
| ------------- | --------------------|:----------------------|
| Train | 17,517 | < 9,000 / < 500 |
| Validation | 973 | < 9,000 / < 500 |
| Test | 973 | < 9,000 / < 500 |
# Cite original article
```
@misc{huang2021efficient,
title={Efficient Attentions for Long Document Summarization},
author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang},
year={2021},
eprint={2104.02112},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
false |
# Dataset Card for VIVOS
## Table of Contents
- [Dataset Card for VIVOS](#dataset-card-for-vivos)
- [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://doi.org/10.5281/zenodo.7068130
- **Repository:** [Needs More Information]
- **Paper:** [A non-expert Kaldi recipe for Vietnamese Speech Recognition System](https://aclanthology.org/W16-5207/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [AILAB](mailto:ailab@hcmus.edu.vn)
### Dataset Summary
VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for Vietnamese Automatic Speech Recognition task.
The corpus was prepared by AILAB, a computer science lab of VNUHCM - University of Science, with Prof. Vu Hai Quan is the head of.
We publish this corpus in hope to attract more scientists to solve Vietnamese speech recognition problems.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Vietnamese
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called `path` and its transcription, called `sentence`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'speaker_id': 'VIVOSSPK01',
'path': '/home/admin/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/vivos/train/waves/VIVOSSPK01/VIVOSSPK01_R001.wav',
'audio': {'path': '/home/admin/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/vivos/train/waves/VIVOSSPK01/VIVOSSPK01_R001.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'sentence': 'KHÁCH SẠN'}
```
### Data Fields
- speaker_id: An id for which speaker (voice) made the recording
- path: The path to the audio file
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- sentence: The sentence the user was prompted to speak
### Data Splits
The speech material has been subdivided into portions for train and test.
Speech was recorded in a quiet environment with high quality microphone, speakers were asked to read one sentence at a time.
| | Train | Test |
| ---------------- | ----- | ----- |
| Speakers | 46 | 19 |
| Utterances | 11660 | 760 |
| Duration | 14:55 | 00:45 |
| Unique Syllables | 4617 | 1692 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
The dataset was initially prepared by AILAB, a computer science lab of VNUHCM - University of Science.
### Licensing Information
Public Domain, Creative Commons Attribution NonCommercial ShareAlike v4.0 ([CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode))
### Citation Information
```
@inproceedings{luong-vu-2016-non,
title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System",
author = "Luong, Hieu-Thi and
Vu, Hai-Quan",
booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-5207",
pages = "51--55",
}
```
### Contributions
Thanks to [@binh234](https://github.com/binh234) for adding this dataset. |
false |
# Dataset Card for "sentence-compression"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression)
- **Repository:** [More Information Needed](https://github.com/google-research-datasets/sentence-compression)
- **Paper:** [More Information Needed](https://www.aclweb.org/anthology/D13-1155/)
- **Point of Contact:** [Katja Filippova](altun@google.com)
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
- **Total amount of disk used:** 14.2 MB
### Dataset Summary
Dataset with pairs of equivalent sentences.
The dataset is provided "AS IS" without any warranty, express or implied.
Google disclaims all liability for any damages, direct or indirect, resulting from using the dataset.
Disclaimer: The team releasing sentence-compression did not upload the dataset to the Hub and did not write a dataset card. These steps were done by the Hugging Face team.
### Supported Tasks
- [Sentence Transformers](https://huggingface.co/sentence-transformers) training; useful for semantic search and sentence similarity.
### Languages
- English.
## Dataset Structure
Each example in the dataset contains pairs of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".
```
{"set": [sentence_1, sentence_2]}
{"set": [sentence_1, sentence_2]}
...
{"set": [sentence_1, sentence_2]}
```
This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using similar pairs of sentences.
### Usage Example
Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with:
```python
from datasets import load_dataset
dataset = load_dataset("embedding-data/sentence-compression")
```
The dataset is loaded as a `DatasetDict` and has the format:
```python
DatasetDict({
train: Dataset({
features: ['set'],
num_rows: 180000
})
})
```
Review an example `i` with:
```python
dataset["train"][i]["set"]
```
### Curation Rationale
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
#### Who are the source language producers?
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
#### Who are the annotators?
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Personal and Sensitive Information
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Discussion of Biases
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Other Known Limitations
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Licensing Information
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Contributions
|
false |
# Dataset Card for RecipeNLG
## 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://recipenlg.cs.put.poznan.pl/
- **Repository:** https://github.com/Glorf/recipenlg
- **Paper:** https://www.aclweb.org/anthology/volumes/2020.inlg-1/
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation.
While the RecipeNLG dataset is based on the Recipe1M+ dataset, it greatly expands the number of recipes available.
The new dataset provides over 1 million new, preprocessed and deduplicated recipes on top of the Recipe1M+ dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
```
{'id': 0,
'title': 'No-Bake Nut Cookies',
'ingredients': ['1 c. firmly packed brown sugar',
'1/2 c. evaporated milk',
'1/2 tsp. vanilla',
'1/2 c. broken nuts (pecans)',
'2 Tbsp. butter or margarine',
'3 1/2 c. bite size shredded rice biscuits'],
'directions': ['In a heavy 2-quart saucepan, mix brown sugar, nuts, evaporated milk and butter or margarine.',
'Stir over medium heat until mixture bubbles all over top.',
'Boil and stir 5 minutes more. Take off heat.',
'Stir in vanilla and cereal; mix well.',
'Using 2 teaspoons, drop and shape into 30 clusters on wax paper.',
'Let stand until firm, about 30 minutes.'],
'link': 'www.cookbooks.com/Recipe-Details.aspx?id=44874',
'source': 0,
'ner': ['brown sugar',
'milk',
'vanilla',
'nuts',
'butter',
'bite size shredded rice biscuits']}
```
### Data Fields
- `id` (`int`): ID.
- `title` (`str`): Title of the recipe.
- `ingredients` (`list` of `str`): Ingredients.
- `directions` (`list` of `str`): Instruction steps.
- `link` (`str`): URL link.
- `source` (`ClassLabel`): Origin of each recipe record, with possible value {"Gathered", "Recipes1M"}:
- "Gathered" (0): Additional recipes gathered from multiple cooking web pages, using automated scripts in a web scraping process.
- "Recipes1M" (1): Recipes from "Recipe1M+" dataset.
- `ner` (`list` of `str`): NER food entities.
### Data Splits
The dataset contains a single `train` split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
I (the "Researcher") have requested permission to use the RecipeNLG dataset (the "Dataset") at Poznań University of Technology (PUT). In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Dataset only for non-commercial research and educational purposes.
2. PUT makes no representations or warranties regarding the Dataset, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Dataset and shall defend and indemnify PUT, including its employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Dataset including but not limited to Researcher's use of any copies of copyrighted images or text that he or she may create from the Dataset.
4. Researcher may provide research associates and colleagues with access to the Dataset provided that they first agree to be bound by these terms and conditions.
5. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
### Citation Information
```bibtex
@inproceedings{bien-etal-2020-recipenlg,
title = "{R}ecipe{NLG}: A Cooking Recipes Dataset for Semi-Structured Text Generation",
author = "Bie{\'n}, Micha{\l} and
Gilski, Micha{\l} and
Maciejewska, Martyna and
Taisner, Wojciech and
Wisniewski, Dawid and
Lawrynowicz, Agnieszka",
booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
month = dec,
year = "2020",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.inlg-1.4",
pages = "22--28",
}
```
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
true |
# Dataset Card for BIOSSES
## 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://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html
- **Repository:** https://github.com/gizemsogancioglu/biosses
- **Paper:** [BIOSSES: a semantic sentence similarity estimation system for the biomedical domain](https://academic.oup.com/bioinformatics/article/33/14/i49/3953954)
- **Point of Contact:** [Gizem Soğancıoğlu](gizemsogancioglu@gmail.com) and [Arzucan Özgür](gizemsogancioglu@gmail.com)
### Dataset Summary
BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/) containing articles from the biomedical domain. The sentence pairs in BIOSSES were selected from citing sentences, i.e. sentences that have a citation to a reference article.
The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). In the original paper the mean of the scores assigned by the five human annotators was taken as the gold standard. The Pearson correlation between the gold standard scores and the scores estimated by the models was used as the evaluation metric. The strength of correlation can be assessed by the general guideline proposed by Evans (1996) as follows:
- very strong: 0.80–1.00
- strong: 0.60–0.79
- moderate: 0.40–0.59
- weak: 0.20–0.39
- very weak: 0.00–0.19
### Supported Tasks and Leaderboards
Biomedical Semantic Similarity Scoring.
### Languages
English.
## Dataset Structure
### Data Instances
For each instance, there are two sentences (i.e. sentence 1 and 2), and its corresponding similarity score (the mean of the scores assigned by the five human annotators).
```
{'sentence 1': 'Here, looking for agents that could specifically kill KRAS mutant cells, they found that knockdown of GATA2 was synthetically lethal with KRAS mutation'
'sentence 2': 'Not surprisingly, GATA2 knockdown in KRAS mutant cells resulted in a striking reduction of active GTP-bound RHO proteins, including the downstream ROCK kinase'
'score': 2.2}
```
### Data Fields
- `sentence 1`: string
- `sentence 2`: string
- `score`: float ranging from 0 (no relation) to 4 (equivalent)
### Data Splits
No data splits provided.
## Dataset Creation
### Curation Rationale
### Source Data
The [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/).
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). The score range was described based on the guidelines of SemEval 2012 Task 6 on STS (Agirre et al., 2012). Besides the annotation instructions, example sentences from the biomedical literature were provided to the annotators for each of the similarity degrees.
The table below shows the Pearson correlation of the scores of each annotator with respect to the average scores of the remaining four annotators. It is observed that there is strong association among the scores of the annotators. The lowest correlations are 0.902, which can be considered as an upper bound for an algorithmic measure evaluated on this dataset.
| |Correlation r |
|----------:|--------------:|
|Annotator A| 0.952|
|Annotator B| 0.958|
|Annotator C| 0.917|
|Annotator D| 0.902|
|Annotator E| 0.941|
#### 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
- Gizem Soğancıoğlu, gizemsogancioglu@gmail.com
- Hakime Öztürk, hakime.ozturk@boun.edu.tr
- Arzucan Özgür, gizemsogancioglu@gmail.com
Bogazici University, Istanbul, Turkey
### Licensing Information
BIOSSES is made available under the terms of [The GNU Common Public License v.3.0](https://www.gnu.org/licenses/gpl-3.0.en.html).
### Citation Information
@article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={So{\u{g}}anc{\i}o{\u{g}}lu, Gizem and {\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
year={2017},
publisher={Oxford University Press}
}
### Contributions
Thanks to [@bwang482](https://github.com/bwang482) for adding this dataset. |
true |
# Dataset Card for AttrScore
- Repository: https://github.com/OSU-NLP-Group/AttrScore
- Paper: [Automatic Evaluation of Attribution by Large Language Models] (https://arxiv.org/pdf/2305.06311.pdf)
- Point of Contact: [Xiang Yue](mailto:yue.149@osu.edu)
### Citation Information
```bib
@article{yue2023automatic,
title={Automatic Evaluation of Attribution by Large Language Models},
author={Yue, Xiang and Wang, Boshi and Zhang, Kai and Chen, Ziru and Su, Yu and Sun, Huan},
journal={arXiv preprint arXiv:2305.06311},
year={2023}
}
```
### Dataset Summary
A recent focus of large language model (LLM) development, as exemplified by generative search engines, is to incorporate external references to generate and support its claims. However, evaluating the attribution, i.e., verifying whether the generated statement is indeed fully supported by the cited reference, remains an open problem.
We construct this dataset, which contains both training and test data for the evaluation of attribution. The training data are repurposed from related tasks, such as question answering, fact-checking, natural language inference, and summarization. The test data, cotains a set simulated from QA datasets and a set manually curated from a generative search engine, New Bing.
## Dataset Structure
### Data Instances
{
"query": "",
"answer": "Bastedo cared for all the animals that inhabit the earth.",
"reference": "Alexandra Lendon Bastedo (9 March 1946 - 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.",
"label": "Extrapolatory",
"dataset": "anli"
}
{
"query": The persian gulf war began when iraq invaded what country?
"answer": The Persian Gulf War began when Iraq invaded Kuwait.
"reference": First Iraq War or Iraq War, before the term \"Iraq War\" became identified instead with the 2003 Iraq War. The Iraqi Army's occupation of Kuwait that began 2 August 1990 was met with international condemnation and brought immediate economic sanctions against Iraq by members of the UN Security Council. Together with the UK's prime minister Margaret Thatcher - who had resisted the invasion by Argentina of the Falkland Islands a decade earlier - George H. W. Bush deployed US forces into Saudi Arabia, and urged other countries to send their own forces to the scene. An array of nations joined the coalition, forming the",
"label": "Attributable",
"dataset": "NaturalQuestions"
}
### Data Fields
- "query": query (may be empty)
- "answer": answer to the query
- "reference": a document or a paragraph
- "label": whether the reference can support the answer to the query ("attributable", "extrapolatory", "contradictory")
- "dataset": the original dataset of the data instance
|
false |
# Dataset Card for BookCorpusOpen
## 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/soskek/bookcorpus/issues/27](https://github.com/soskek/bookcorpus/issues/27)
- **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:** 2.40 GB
- **Size of the generated dataset:** 6.64 GB
- **Total amount of disk used:** 9.05 GB
### Dataset Summary
Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.
This version of bookcorpus has 17868 dataset items (books). Each item contains two fields: title and text. The title is the name of the book (just the file name) while text contains unprocessed book text. The bookcorpus has been prepared by Shawn Presser and is generously hosted by The-Eye. The-Eye is a non-profit, community driven platform dedicated to the archiving and long-term preservation of any and all data including but by no means limited to... websites, books, games, software, video, audio, other digital-obscura and ideas.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 2.40 GB
- **Size of the generated dataset:** 6.64 GB
- **Total amount of disk used:** 9.05 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "\"\\n\\nzONE\\n\\n## The end and the beginning\\n\\nby\\n\\nPhilip F. Blood\\n\\nSMASHWORDS EDITION\\n\\nVersion 3.55\\n\\nPUBLISHED BY:\\n\\nPhi...",
"title": "zone-the-end-and-the-beginning.epub.txt"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `title`: a `string` feature.
- `text`: a `string` feature.
### Data Splits
| name |train|
|----------|----:|
|plain_text|17868|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The books have been crawled from smashwords.com, see their [terms of service](https://www.smashwords.com/about/tos) for more information.
A data sheet for this dataset has also been created and published in [Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus](https://arxiv.org/abs/2105.05241)
### Citation Information
```
@InProceedings{Zhu_2015_ICCV,
title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}
```
### Contributions
Thanks to [@vblagoje](https://github.com/vblagoje) for adding this dataset. |
false |
# Dataset Card for CoNLL2012 shared task data based on OntoNotes 5.0
## 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:** [CoNLL-2012 Shared Task](https://conll.cemantix.org/2012/data.html), [Author's page](https://cemantix.org/data/ontonotes.html)
- **Repository:** [Mendeley](https://data.mendeley.com/datasets/zmycy7t9h9)
- **Paper:** [Towards Robust Linguistic Analysis using OntoNotes](https://aclanthology.org/W13-3516/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre,
multilingual corpus manually annotated with syntactic, semantic and discourse information.
This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task.
It includes v4 train/dev and v9 test data for English/Chinese/Arabic and corrected version v12 train/dev/test data (English only).
The source of data is the Mendeley Data repo [ontonotes-conll2012](https://data.mendeley.com/datasets/zmycy7t9h9), which seems to be as the same as the official data, but users should use this dataset on their own responsibility.
See also summaries from paperwithcode, [OntoNotes 5.0](https://paperswithcode.com/dataset/ontonotes-5-0) and [CoNLL-2012](https://paperswithcode.com/dataset/conll-2012-1)
For more detailed info of the dataset like annotation, tag set, etc., you can refer to the documents in the Mendeley repo mentioned above.
### Supported Tasks and Leaderboards
- [Named Entity Recognition on Ontonotes v5 (English)](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ontonotes-v5)
- [Coreference Resolution on OntoNotes](https://paperswithcode.com/sota/coreference-resolution-on-ontonotes)
- [Semantic Role Labeling on OntoNotes](https://paperswithcode.com/sota/semantic-role-labeling-on-ontonotes)
- ...
### Languages
V4 data for Arabic, Chinese, English, and V12 data for English
## Dataset Structure
### Data Instances
```
{
{'document_id': 'nw/wsj/23/wsj_2311',
'sentences': [{'part_id': 0,
'words': ['CONCORDE', 'trans-Atlantic', 'flights', 'are', '$', '2, 'to', 'Paris', 'and', '$', '3, 'to', 'London', '.']},
'pos_tags': [25, 18, 27, 43, 2, 12, 17, 25, 11, 2, 12, 17, 25, 7],
'parse_tree': '(TOP(S(NP (NNP CONCORDE) (JJ trans-Atlantic) (NNS flights) )(VP (VBP are) (NP(NP(NP ($ $) (CD 2,400) )(PP (IN to) (NP (NNP Paris) ))) (CC and) (NP(NP ($ $) (CD 3,200) )(PP (IN to) (NP (NNP London) ))))) (. .) ))',
'predicate_lemmas': [None, None, None, 'be', None, None, None, None, None, None, None, None, None, None],
'predicate_framenet_ids': [None, None, None, '01', None, None, None, None, None, None, None, None, None, None],
'word_senses': [None, None, None, None, None, None, None, None, None, None, None, None, None, None],
'speaker': None,
'named_entities': [7, 6, 0, 0, 0, 15, 0, 5, 0, 0, 15, 0, 5, 0],
'srl_frames': [{'frames': ['B-ARG1', 'I-ARG1', 'I-ARG1', 'B-V', 'B-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'O'],
'verb': 'are'}],
'coref_spans': [],
{'part_id': 0,
'words': ['In', 'a', 'Centennial', 'Journal', 'article', 'Oct.', '5', ',', 'the', 'fares', 'were', 'reversed', '.']}]}
'pos_tags': [17, 13, 25, 25, 24, 25, 12, 4, 13, 27, 40, 42, 7],
'parse_tree': '(TOP(S(PP (IN In) (NP (DT a) (NML (NNP Centennial) (NNP Journal) ) (NN article) ))(NP (NNP Oct.) (CD 5) ) (, ,) (NP (DT the) (NNS fares) )(VP (VBD were) (VP (VBN reversed) )) (. .) ))',
'predicate_lemmas': [None, None, None, None, None, None, None, None, None, None, None, 'reverse', None],
'predicate_framenet_ids': [None, None, None, None, None, None, None, None, None, None, None, '01', None],
'word_senses': [None, None, None, None, None, None, None, None, None, None, None, None, None],
'speaker': None,
'named_entities': [0, 0, 4, 22, 0, 12, 30, 0, 0, 0, 0, 0, 0],
'srl_frames': [{'frames': ['B-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'B-ARGM-TMP', 'I-ARGM-TMP', 'O', 'B-ARG1', 'I-ARG1', 'O', 'B-V', 'O'],
'verb': 'reversed'}],
'coref_spans': [],
}
```
### Data Fields
- **`document_id`** (*`str`*): This is a variation on the document filename
- **`sentences`** (*`List[Dict]`*): All sentences of the same document are in a single example for the convenience of concatenating sentences.
Every element in `sentences` is a *`Dict`* composed of the following data fields:
- **`part_id`** (*`int`*) : Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
- **`words`** (*`List[str]`*) :
- **`pos_tags`** (*`List[ClassLabel]` or `List[str]`*) : This is the Penn-Treebank-style part of speech. When parse information is missing, all parts of speech except the one for which there is some sense or proposition annotation are marked with a XX tag. The verb is marked with just a VERB tag.
- tag set : Note tag sets below are founded by scanning all the data, and I found it seems to be a little bit different from officially stated tag sets. See official documents in the [Mendeley repo](https://data.mendeley.com/datasets/zmycy7t9h9)
- arabic : str. Because pos tag in Arabic is compounded and complex, hard to represent it by `ClassLabel`
- chinese v4 : `datasets.ClassLabel(num_classes=36, names=["X", "AD", "AS", "BA", "CC", "CD", "CS", "DEC", "DEG", "DER", "DEV", "DT", "ETC", "FW", "IJ", "INF", "JJ", "LB", "LC", "M", "MSP", "NN", "NR", "NT", "OD", "ON", "P", "PN", "PU", "SB", "SP", "URL", "VA", "VC", "VE", "VV",])`, where `X` is for pos tag missing
- english v4 : `datasets.ClassLabel(num_classes=49, names=["XX", "``", "$", "''", ",", "-LRB-", "-RRB-", ".", ":", "ADD", "AFX", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NFP", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WP$", "WRB",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`".
- english v12 : `datasets.ClassLabel(num_classes=51, names="english_v12": ["XX", "``", "$", "''", "*", ",", "-LRB-", "-RRB-", ".", ":", "ADD", "AFX", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NFP", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "VERB", "WDT", "WP", "WP$", "WRB",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`".
- **`parse_tree`** (*`Optional[str]`*) : An serialized NLTK Tree representing the parse. It includes POS tags as pre-terminal nodes. When the parse information is missing, the parse will be `None`.
- **`predicate_lemmas`** (*`List[Optional[str]]`*) : The predicate lemma of the words for which we have semantic role information or word sense information. All other indices are `None`.
- **`predicate_framenet_ids`** (*`List[Optional[int]]`*) : The PropBank frameset ID of the lemmas in predicate_lemmas, or `None`.
- **`word_senses`** (*`List[Optional[float]]`*) : The word senses for the words in the sentence, or None. These are floats because the word sense can have values after the decimal, like 1.1.
- **`speaker`** (*`Optional[str]`*) : This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data. When it is not available, it will be `None`.
- **`named_entities`** (*`List[ClassLabel]`*) : The BIO tags for named entities in the sentence.
- tag set : `datasets.ClassLabel(num_classes=37, names=["O", "B-PERSON", "I-PERSON", "B-NORP", "I-NORP", "B-FAC", "I-FAC", "B-ORG", "I-ORG", "B-GPE", "I-GPE", "B-LOC", "I-LOC", "B-PRODUCT", "I-PRODUCT", "B-DATE", "I-DATE", "B-TIME", "I-TIME", "B-PERCENT", "I-PERCENT", "B-MONEY", "I-MONEY", "B-QUANTITY", "I-QUANTITY", "B-ORDINAL", "I-ORDINAL", "B-CARDINAL", "I-CARDINAL", "B-EVENT", "I-EVENT", "B-WORK_OF_ART", "I-WORK_OF_ART", "B-LAW", "I-LAW", "B-LANGUAGE", "I-LANGUAGE",])`
- **`srl_frames`** (*`List[{"word":str, "frames":List[str]}]`*) : A dictionary keyed by the verb in the sentence for the given Propbank frame labels, in a BIO format.
- **`coref spans`** (*`List[List[int]]`*) : The spans for entity mentions involved in coreference resolution within the sentence. Each element is a tuple composed of (cluster_id, start_index, end_index). Indices are inclusive.
### Data Splits
Each dataset (arabic_v4, chinese_v4, english_v4, english_v12) has 3 splits: _train_, _validation_, and _test_
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{pradhan-etal-2013-towards,
title = "Towards Robust Linguistic Analysis using {O}nto{N}otes",
author = {Pradhan, Sameer and
Moschitti, Alessandro and
Xue, Nianwen and
Ng, Hwee Tou and
Bj{\"o}rkelund, Anders and
Uryupina, Olga and
Zhang, Yuchen and
Zhong, Zhi},
booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-3516",
pages = "143--152",
}
```
### Contributions
Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset. |
true |
# 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:** https://turkunlp.org/paraphrase.html
- **Repository:** https://github.com/TurkuNLP/Turku-paraphrase-corpus
- **Paper:** https://aclanthology.org/2021.nodalida-main.29
- **Leaderboard:** Not available
- **Point of Contact:** [Jenna Kanerva, Filip Ginter](mailto:jmnybl@utu.fi,filip.ginter@gmail.com)
### Dataset Summary
The project gathered a large dataset of Finnish paraphrase pairs (over 100,000). The paraphrases are selected and classified manually, so as to minimize lexical overlap, and provide examples that are maximally structurally and lexically different. The objective is to create a dataset which is challenging and better tests the capabilities of natural language understanding. An important feature of the data is that most paraphrase pairs are distributed in their document context. The primary application for the dataset is the development and evaluation of deep language models, and representation learning in general.
Usage:
```
from datasets import load_dataset
dataset = load_dataset('TurkuNLP/turku_paraphrase_corpus', name="plain")
```
where `name` is one of the supported loading options: `plain`, `plain-context`, `classification`, `classification-context`, or `generation`. See Data Fields for more information.
### Supported Tasks and Leaderboards
* Paraphrase classification
* Paraphrase generation
### Languages
Finnish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
The dataset consist of pairs of text passages, where a typical passage is about a sentence long, however, a passage may also be longer or shorter than a sentence. Thus, each example includes two text passages (string), a manually annotated label to indicate the paraphrase type (string), and additional metadata. The dataset includes three different configurations: `plain`, `classification`, and `generation`. The `plain` configuration loads the original data without any additional preprocessing or transformations, while the `classification` configuration directly builds the data in a form suitable for training a paraphrase classifier, where each example is doubled in the data with different directions (text1, text2, label) --> (text2, text1, label) taking care of the label flipping as well if needed (paraphrases with directionality flag < or >). In the `generation` configuration, the examples are preprocessed to be directly suitable for the paraphrase generation task. In here, paraphrases not suitable for generation are discarded (negative, and highly context-dependent paraphrases), and directional paraphrases are provided so that the generation goes from more detailed passage to the more general one in order to prevent model hallucination (i.e. model learning to introduce new information). The rest of the paraphrases are provided in both directions (text1, text2, label) --> (text2, text1, label).
Each pair in the `plain` and `classification` configurations will include fields:
`id`:
Identifier of the paraphrase pair (string)
`gem_id`:
Identifier of the paraphrase pair in the GEM dataset (string)
`goeswith`:
Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data. All examples with the same `goeswith` value (other than `not available`) should be kept together in any train/dev/test split; most users won't need this (string)
`fold`:
0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold, most users won't need this (int)
`text1`:
First paraphrase passage (string)
`text2`:
Second paraphrase passage (string)
`label`:
Manually annotated labels (string)
`binary_label`:
Label turned into binary with values `positive` (paraphrase) and `negative` (not-paraphrase) (string)
`is_rewrite`:
Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool)
Each pair in the `generation` config will include the same fields except `text1` and `text2` are renamed to `input` and `output` in order to indicate the generation direction. Thus the fields are: `id`, `gem_id`, `goeswith`, `fold`, `input`, `output`, `label`, `binary_label`, and `is_rewrite`
**Context**: Most (but not all) of the paraphrase pairs are identified in their document context. By default, these contexts are not included to conserve memory, but can be accessed using the configurations `plain-context` and `classification-context`. These are exactly like `plain` and `classification` with these additional fields:
`context1`:
a dictionary with the fields `doctext` (string), `begin` (int), `end` (int). These mean that the paraphrase in `text1` was extracted from `doctext[begin:end]`. In most cases, `doctext[begin:end]` and `text1` are the exact same string, but occassionally that is not the case when e.g. intervening punctuations or other unrelated texts were "cleaned" from `text1` during annotation. In case the context is not available, `doctext` is an empty string and `beg==end==0`
`context2`:
same as `context1` but for `text2`
### 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 [@jmnybl](https://github.com/jmnybl) and [@fginter](https://github.com/fginter) for adding this dataset. |
true |
# Dataset Card for wisesight_sentiment
## 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/PyThaiNLP/wisesight-sentiment
- **Repository:** https://github.com/PyThaiNLP/wisesight-sentiment
- **Paper:**
- **Leaderboard:** https://www.kaggle.com/c/wisesight-sentiment/
- **Point of Contact:** https://github.com/PyThaiNLP/
### Dataset Summary
Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment label (positive, neutral, negative, question)
- Released to public domain under Creative Commons Zero v1.0 Universal license.
- Labels: {"pos": 0, "neu": 1, "neg": 2, "q": 3}
- Size: 26,737 messages
- Language: Central Thai
- Style: Informal and conversational. With some news headlines and advertisement.
- Time period: Around 2016 to early 2019. With small amount from other period.
- Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs.
- Privacy:
- Only messages that made available to the public on the internet (websites, blogs, social network sites).
- For Facebook, this means the public comments (everyone can see) that made on a public page.
- Private/protected messages and messages in groups, chat, and inbox are not included.
- Alternations and modifications:
- Keep in mind that this corpus does not statistically represent anything in the language register.
- Large amount of messages are not in their original form. Personal data are removed or masked.
- Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact.
(Mis)spellings are kept intact.
- Messages longer than 2,000 characters are removed.
- Long non-Thai messages are removed. Duplicated message (exact match) are removed.
- More characteristics of the data can be explore [this notebook](https://github.com/PyThaiNLP/wisesight-sentiment/blob/master/exploration.ipynb)
### Supported Tasks and Leaderboards
Sentiment analysis / [Kaggle Leaderboard](https://www.kaggle.com/c/wisesight-sentiment/)
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'category': 'pos', 'texts': 'น่าสนนน'}
{'category': 'neu', 'texts': 'ครับ #phithanbkk'}
{'category': 'neg', 'texts': 'ซื้อแต่ผ้าอนามัยแบบเย็นมาค่ะ แบบว่าอีห่ากูนอนไม่ได้'}
{'category': 'q', 'texts': 'มีแอลกอฮอลมั้ยคะ'}
```
### Data Fields
- `texts`: texts
- `category`: sentiment of texts ranging from `pos` (positive; 0), `neu` (neutral; 1), `neg` (negative; 2) and `q` (question; 3)
### Data Splits
| | train | valid | test |
|-----------|-------|-------|-------|
| # samples | 21628 | 2404 | 2671 |
| # neu | 11795 | 1291 | 1453 |
| # neg | 5491 | 637 | 683 |
| # pos | 3866 | 434 | 478 |
| # q | 476 | 42 | 57 |
| avg words | 27.21 | 27.18 | 27.12 |
| avg chars | 89.82 | 89.50 | 90.36 |
## Dataset Creation
### Curation Rationale
Originally, the dataset was conceived for the [In-class Kaggle Competition](https://www.kaggle.com/c/wisesight-sentiment/) at Chulalongkorn university by [Ekapol Chuangsuwanich](https://www.cp.eng.chula.ac.th/en/about/faculty/ekapolc/) (Faculty of Engineering, Chulalongkorn University). It has since become one of the benchmarks for sentiment analysis in Thai.
### Source Data
#### Initial Data Collection and Normalization
- Style: Informal and conversational. With some news headlines and advertisement.
- Time period: Around 2016 to early 2019. With small amount from other period.
- Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs.
- Privacy:
- Only messages that made available to the public on the internet (websites, blogs, social network sites).
- For Facebook, this means the public comments (everyone can see) that made on a public page.
- Private/protected messages and messages in groups, chat, and inbox are not included.
- Usernames and non-public figure names are removed
- Phone numbers are masked (e.g. 088-888-8888, 09-9999-9999, 0-2222-2222)
- If you see any personal data still remain in the set, please tell us - so we can remove them.
- Alternations and modifications:
- Keep in mind that this corpus does not statistically represent anything in the language register.
- Large amount of messages are not in their original form. Personal data are removed or masked.
- Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact.
- (Mis)spellings are kept intact.
- Messages longer than 2,000 characters are removed.
- Long non-Thai messages are removed. Duplicated message (exact match) are removed.
#### Who are the source language producers?
Social media users in Thailand
### Annotations
#### Annotation process
- Sentiment values are assigned by human annotators.
- A human annotator put his/her best effort to assign just one label, out of four, to a message.
- Agreement, enjoyment, and satisfaction are positive. Disagreement, sadness, and disappointment are negative.
- Showing interest in a topic or in a product is counted as positive. In this sense, a question about a particular product could has a positive sentiment value, if it shows the interest in the product.
- Saying that other product or service is better is counted as negative.
- General information or news title tend to be counted as neutral.
#### Who are the annotators?
Outsourced annotators hired by [Wisesight (Thailand) Co., Ltd.](https://github.com/wisesight/)
### Personal and Sensitive Information
- The authors tried to exclude any known personally identifiable information from this data set.
- Usernames and non-public figure names are removed
- Phone numbers are masked (e.g. 088-888-8888, 09-9999-9999, 0-2222-2222)
- If you see any personal data still remain in the set, please tell us - so we can remove them.
## Considerations for Using the Data
### Social Impact of Dataset
- `wisesight_sentiment` is the first and one of the few open datasets for sentiment analysis of social media data in Thai
- There are risks of personal information that escape the anonymization process
### Discussion of Biases
- A message can be ambiguous. When possible, the judgement will be based solely on the text itself.
- In some situation, like when the context is missing, the annotator may have to rely on his/her own world knowledge and just guess.
- In some cases, the human annotator may have an access to the message's context, like an image. These additional information are not included as part of this corpus.
### Other Known Limitations
- The labels are imbalanced; over half of the texts are `neu` (neutral) whereas there are very few `q` (question).
- Misspellings in social media texts make word tokenization process for Thai difficult, thus impacting the model performance
## Additional Information
### Dataset Curators
Thanks [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp) community, [Kitsuchart Pasupa](http://www.it.kmitl.ac.th/~kitsuchart/) (Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang), and [Ekapol Chuangsuwanich](https://www.cp.eng.chula.ac.th/en/about/faculty/ekapolc/) (Faculty of Engineering, Chulalongkorn University) for advice. The original Kaggle competition, using the first version of this corpus, can be found at https://www.kaggle.com/c/wisesight-sentiment/
### Licensing Information
- If applicable, copyright of each message content belongs to the original poster.
- **Annotation data (labels) are released to public domain.**
- [Wisesight (Thailand) Co., Ltd.](https://github.com/wisesight/) helps facilitate the annotation, but does not necessarily agree upon the labels made by the human annotators. This annotation is for research purpose and does not reflect the professional work that Wisesight has been done for its customers.
- The human annotator does not necessarily agree or disagree with the message. Likewise, the label he/she made to the message does not necessarily reflect his/her personal view towards the message.
### Citation Information
Please cite the following if you make use of the dataset:
Arthit Suriyawongkul, Ekapol Chuangsuwanich, Pattarawat Chormai, and Charin Polpanumas. 2019. **PyThaiNLP/wisesight-sentiment: First release.** September.
BibTeX:
```
@software{bact_2019_3457447,
author = {Suriyawongkul, Arthit and
Chuangsuwanich, Ekapol and
Chormai, Pattarawat and
Polpanumas, Charin},
title = {PyThaiNLP/wisesight-sentiment: First release},
month = sep,
year = 2019,
publisher = {Zenodo},
version = {v1.0},
doi = {10.5281/zenodo.3457447},
url = {https://doi.org/10.5281/zenodo.3457447}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. |
false |
# XTREME-S
## Dataset Description
- **Fine-Tuning script:** [research-projects/xtreme-s](https://github.com/huggingface/transformers/tree/master/examples/research_projects/xtreme-s)
- **Paper:** [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752)
- **Leaderboard:** [TODO(PVP)]()
- **FLEURS amount of disk used:** 350 GB
- **Multilingual Librispeech amount of disk used:** 2700 GB
- **Voxpopuli amount of disk used:** 400 GB
- **Covost2 amount of disk used:** 70 GB
- **Minds14 amount of disk used:** 5 GB
- **Total amount of disk used:** ca. 3500 GB
The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval.
***TLDR; XTREME-S is the first speech benchmark that is both diverse, fully accessible, and reproducible. All datasets can be downloaded with a single line of code.
An easy-to-use and flexible fine-tuning script is provided and actively maintained.***
XTREME-S covers speech recognition with Fleurs, Multilingual LibriSpeech (MLS) and VoxPopuli, speech translation with CoVoST-2, speech classification with LangID (Fleurs) and intent classification (MInds-14) and finally speech(-text) retrieval with Fleurs. Each of the tasks covers a subset of the 102 languages included in XTREME-S, from various regions:
- **Western Europe**: *Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh*
- **Eastern Europe**: *Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian*
- **Central-Asia/Middle-East/North-Africa**: *Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek*
- **Sub-Saharan Africa**: *Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu*
- **South-Asia**: *Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu*
- **South-East Asia**: *Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese*
- **CJK languages**: *Cantonese and Mandarin Chinese, Japanese, Korean*
## Design principles
### Diversity
XTREME-S aims for task, domain and language
diversity. Tasks should be diverse and cover several domains to
provide a reliable evaluation of model generalization and
robustness to noisy naturally-occurring speech in different
environments. Languages should be diverse to ensure that
models can adapt to a wide range of linguistic and phonological
phenomena.
### Accessibility
The sub-dataset for each task can be downloaded
with a **single line of code** as shown in [Supported Tasks](#supported-tasks).
Each task is available under a permissive license that allows the use and redistribution
of the data for research purposes. Tasks have been selected based on their usage by
pre-existing multilingual pre-trained models, for simplicity.
### Reproducibility
We produce fully **open-sourced, maintained and easy-to-use** fine-tuning scripts
for each task as shown under [Fine-tuning Example](#fine-tuning-and-evaluation-example).
XTREME-S encourages submissions that leverage publicly available speech and text datasets. Users should detail which data they use.
In general, we encourage settings that can be reproduced by the community, but also encourage the exploration of new frontiers for speech representation learning.
## Fine-tuning and Evaluation Example
We provide a fine-tuning script under [**research-projects/xtreme-s**](https://github.com/huggingface/transformers/tree/master/examples/research_projects/xtreme-s).
The fine-tuning script is written in PyTorch and allows one to fine-tune and evaluate any [Hugging Face model](https://huggingface.co/models) on XTREME-S.
The example script is actively maintained by [@anton-l](https://github.com/anton-l) and [@patrickvonplaten](https://github.com/patrickvonplaten). Feel free
to reach out via issues or pull requests on GitHub if you have any questions.
## Leaderboards
The leaderboard for the XTREME-S benchmark can be found at [this address (TODO(PVP))]().
## Supported Tasks
Note that the suppoprted tasks are focused particularly on linguistic aspect of speech,
while nonlinguistic/paralinguistic aspects of speech relevant to e.g. speech synthesis or voice conversion are **not** evaluated.
<p align="center">
<img src="https://github.com/patrickvonplaten/scientific_images/raw/master/xtreme_s.png" alt="Datasets used in XTREME"/>
</p>
### 1. Speech Recognition (ASR)
We include three speech recognition datasets: FLEURS-ASR, MLS and VoxPopuli (optionally BABEL). Multilingual fine-tuning is used for these three datasets.
#### FLEURS-ASR
*FLEURS-ASR* is the speech version of the FLORES machine translation benchmark, covering 2000 n-way parallel sentences in n=102 languages.
```py
from datasets import load_dataset
fleurs_asr = load_dataset("google/xtreme_s", "fleurs.af_za") # for Afrikaans
# to download all data for multi-lingual fine-tuning uncomment following line
# fleurs_asr = load_dataset("google/xtreme_s", "fleurs.all")
# see structure
print(fleurs_asr)
# load audio sample on the fly
audio_input = fleurs_asr["train"][0]["audio"] # first decoded audio sample
transcription = fleurs_asr["train"][0]["transcription"] # first transcription
# use `audio_input` and `transcription` to fine-tune your model for ASR
# for analyses see language groups
all_language_groups = fleurs_asr["train"].features["lang_group_id"].names
lang_group_id = fleurs_asr["train"][0]["lang_group_id"]
all_language_groups[lang_group_id]
```
#### Multilingual LibriSpeech (MLS)
*MLS* is a large multilingual corpus derived from read audiobooks from LibriVox and consists of 8 languages. For this challenge the training data is limited to 10-hours splits.
```py
from datasets import load_dataset
mls = load_dataset("google/xtreme_s", "mls.pl") # for Polish
# to download all data for multi-lingual fine-tuning uncomment following line
# mls = load_dataset("google/xtreme_s", "mls.all")
# see structure
print(mls)
# load audio sample on the fly
audio_input = mls["train"][0]["audio"] # first decoded audio sample
transcription = mls["train"][0]["transcription"] # first transcription
# use `audio_input` and `transcription` to fine-tune your model for ASR
```
#### VoxPopuli
*VoxPopuli* is a large-scale multilingual speech corpus for representation learning and semi-supervised learning, from which we use the speech recognition dataset. The raw data is collected from 2009-2020 European Parliament event recordings. We acknowledge the European Parliament for creating and sharing these materials.
**VoxPopuli has to download the whole dataset 100GB since languages
are entangled into each other - maybe not worth testing here due to the size**
```py
from datasets import load_dataset
voxpopuli = load_dataset("google/xtreme_s", "voxpopuli.ro") # for Romanian
# to download all data for multi-lingual fine-tuning uncomment following line
# voxpopuli = load_dataset("google/xtreme_s", "voxpopuli.all")
# see structure
print(voxpopuli)
# load audio sample on the fly
audio_input = voxpopuli["train"][0]["audio"] # first decoded audio sample
transcription = voxpopuli["train"][0]["transcription"] # first transcription
# use `audio_input` and `transcription` to fine-tune your model for ASR
```
#### (Optionally) BABEL
*BABEL* from IARPA is a conversational speech recognition dataset in low-resource languages. First, download LDC2016S06, LDC2016S12, LDC2017S08, LDC2017S05 and LDC2016S13. BABEL is the only dataset in our benchmark who is less easily accessible, so you will need to sign in to get access to it on LDC. Although not officially part of the XTREME-S ASR datasets, BABEL is often used for evaluating speech representations on a difficult domain (phone conversations).
```py
from datasets import load_dataset
babel = load_dataset("google/xtreme_s", "babel.as")
```
**The above command is expected to fail with a nice error message,
explaining how to download BABEL**
The following should work:
```py
from datasets import load_dataset
babel = load_dataset("google/xtreme_s", "babel.as", data_dir="/path/to/IARPA_BABEL_OP1_102_LDC2016S06.zip")
# see structure
print(babel)
# load audio sample on the fly
audio_input = babel["train"][0]["audio"] # first decoded audio sample
transcription = babel["train"][0]["transcription"] # first transcription
# use `audio_input` and `transcription` to fine-tune your model for ASR
```
### 2. Speech Translation (ST)
We include the CoVoST-2 dataset for automatic speech translation.
#### CoVoST-2
The *CoVoST-2* benchmark has become a commonly used dataset for evaluating automatic speech translation. It covers language pairs from English into 15 languages, as well as 21 languages into English. We use only the "X->En" direction to evaluate cross-lingual representations. The amount of supervision varies greatly in this setting, from one hour for Japanese->English to 180 hours for French->English. This makes pretraining particularly useful to enable such few-shot learning. We enforce multiligual fine-tuning for simplicity. Results are splitted in high/med/low-resource language pairs as explained in the [paper (TODO(PVP))].
```py
from datasets import load_dataset
covost_2 = load_dataset("google/xtreme_s", "covost2.id.en") # for Indonesian to English
# to download all data for multi-lingual fine-tuning uncomment following line
# covost_2 = load_dataset("google/xtreme_s", "covost2.all")
# see structure
print(covost_2)
# load audio sample on the fly
audio_input = covost_2["train"][0]["audio"] # first decoded audio sample
transcription = covost_2["train"][0]["transcription"] # first transcription
translation = covost_2["train"][0]["translation"] # first translation
# use audio_input and translation to fine-tune your model for AST
```
### 3. Speech Classification
We include two multilingual speech classification datasets: FLEURS-LangID and Minds-14.
#### Language Identification - FLEURS-LangID
LangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all.
```py
from datasets import load_dataset
fleurs_langID = load_dataset("google/xtreme_s", "fleurs.all") # to download all data
# see structure
print(fleurs_langID)
# load audio sample on the fly
audio_input = fleurs_langID["train"][0]["audio"] # first decoded audio sample
language_class = fleurs_langID["train"][0]["lang_id"] # first id class
language = fleurs_langID["train"].features["lang_id"].names[language_class]
# use audio_input and language_class to fine-tune your model for audio classification
```
#### Intent classification - Minds-14
Minds-14 is an intent classification made from e-banking speech datasets in 14 languages, with 14 intent labels. We impose a single multilingual fine-tuning to increase the size of the train and test sets and reduce the variance associated with the small size of the dataset per language.
```py
from datasets import load_dataset
minds_14 = load_dataset("google/xtreme_s", "minds14.fr-FR") # for French
# to download all data for multi-lingual fine-tuning uncomment following line
# minds_14 = load_dataset("google/xtreme_s", "minds14.all")
# see structure
print(minds_14)
# load audio sample on the fly
audio_input = minds_14["train"][0]["audio"] # first decoded audio sample
intent_class = minds_14["train"][0]["intent_class"] # first transcription
intent = minds_14["train"].features["intent_class"].names[intent_class]
# use audio_input and language_class to fine-tune your model for audio classification
```
### 4. (Optionally) Speech Retrieval
We optionally include one speech retrieval dataset: FLEURS-Retrieval as explained in the [FLEURS paper](https://arxiv.org/abs/2205.12446).
#### FLEURS-Retrieval
FLEURS-Retrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use FLEURS-Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English "key" utterance corresponding to the speech translation of "queries" in 15 languages. Results have to be reported on the test sets of FLEURS-Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult.
```py
from datasets import load_dataset
fleurs_retrieval = load_dataset("google/xtreme_s", "fleurs.af_za") # for Afrikaans
# to download all data for multi-lingual fine-tuning uncomment following line
# fleurs_retrieval = load_dataset("google/xtreme_s", "fleurs.all")
# see structure
print(fleurs_retrieval)
# load audio sample on the fly
audio_input = fleurs_retrieval["train"][0]["audio"] # decoded audio sample
text_sample_pos = fleurs_retrieval["train"][0]["transcription"] # positive text sample
text_sample_neg = fleurs_retrieval["train"][1:20]["transcription"] # negative text samples
# use `audio_input`, `text_sample_pos`, and `text_sample_neg` to fine-tune your model for retrieval
```
Users can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech.
## Dataset Structure
The XTREME-S benchmark is composed of the following datasets:
- [FLEURS](https://huggingface.co/datasets/google/fleurs#dataset-structure)
- [Multilingual Librispeech (MLS)](https://huggingface.co/datasets/facebook/multilingual_librispeech#dataset-structure)
Note that for MLS, XTREME-S uses `path` instead of `file` and `transcription` instead of `text`.
- [Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli#dataset-structure)
- [Minds14](https://huggingface.co/datasets/polyai/minds14#dataset-structure)
- [Covost2](https://huggingface.co/datasets/covost2#dataset-structure)
Note that for Covost2, XTREME-S uses `path` instead of `file` and `transcription` instead of `sentence`.
- [BABEL](https://huggingface.co/datasets/ldc/iarpa_babel#dataset-structure)
Please click on the link of the dataset cards to get more information about its dataset structure.
## Dataset Creation
The XTREME-S benchmark is composed of the following datasets:
- [FLEURS](https://huggingface.co/datasets/google/fleurs#dataset-creation)
- [Multilingual Librispeech (MLS)](https://huggingface.co/datasets/facebook/multilingual_librispeech#dataset-creation)
- [Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli#dataset-creation)
- [Minds14](https://huggingface.co/datasets/polyai/minds14#dataset-creation)
- [Covost2](https://huggingface.co/datasets/covost2#dataset-creation)
- [BABEL](https://huggingface.co/datasets/ldc/iarpa_babel#dataset-creation)
Please visit the corresponding dataset cards to get more information about the source data.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos).
### Discussion of Biases
Most datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through XTREME-S should generalize to all languages.
### Other Known Limitations
The benchmark has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on XTREME-S should still correlate well with actual progress made for speech understanding.
## Additional Information
All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/).
### Citation Information
#### XTREME-S
```
@article{conneau2022xtreme,
title={XTREME-S: Evaluating Cross-lingual Speech Representations},
author={Conneau, Alexis and Bapna, Ankur and Zhang, Yu and Ma, Min and von Platen, Patrick and Lozhkov, Anton and Cherry, Colin and Jia, Ye and Rivera, Clara and Kale, Mihir and others},
journal={arXiv preprint arXiv:2203.10752},
year={2022}
}
```
#### MLS
```
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
```
#### VoxPopuli
```
@article{wang2021voxpopuli,
title={Voxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation},
author={Wang, Changhan and Riviere, Morgane and Lee, Ann and Wu, Anne and Talnikar, Chaitanya and Haziza, Daniel and Williamson, Mary and Pino, Juan and Dupoux, Emmanuel},
journal={arXiv preprint arXiv:2101.00390},
year={2021}
}
```
#### CoVoST 2
```
@article{DBLP:journals/corr/abs-2007-10310,
author = {Changhan Wang and
Anne Wu and
Juan Miguel Pino},
title = {CoVoST 2: {A} Massively Multilingual Speech-to-Text Translation Corpus},
journal = {CoRR},
volume = {abs/2007.10310},
year = {2020},
url = {https://arxiv.org/abs/2007.10310},
eprinttype = {arXiv},
eprint = {2007.10310},
timestamp = {Thu, 12 Aug 2021 15:37:06 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2007-10310.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
#### Minds14
```
@article{gerz2021multilingual,
title={Multilingual and cross-lingual intent detection from spoken data},
author={Gerz, Daniela and Su, Pei-Hao and Kusztos, Razvan and Mondal, Avishek and Lis, Micha{\l} and Singhal, Eshan and Mrk{\v{s}}i{\'c}, Nikola and Wen, Tsung-Hsien and Vuli{\'c}, Ivan},
journal={arXiv preprint arXiv:2104.08524},
year={2021}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@anton-l](https://github.com/anton-l), [@aconneau](https://github.com/aconneau) for adding this dataset
|
false |
# School Math 0.25M
## 内容
包含约25万条由[BELLE](https://github.com/LianjiaTech/BELLE)项目生成的中文数学题数据，包含解题过程。
注意：此数据集是由ChatGPT产生的，未经过严格校验，题目或解题过程可能包含错误。使用过程中请注意这一点。
## 样例
```
{
"instruction": "题目：小华手里有一个装满糖果的袋子，共有12个，小明想知道里面有多少颗糖果，于是他问小华：“你手里的糖果袋子里有偶数个糖果吗？”小华回答：“有，而且多于10颗。”请问小华手里的糖果袋子里最少有几颗糖果？",
"input": "",
"output": "\n由题目可知：小华手里的糖果袋子里有偶数个糖果；\n又知道小华手里的糖果袋子里有多于10颗糖果。\n因为糖果数为偶数，多于10颗，所以糖果数最小必须是12。\n所以小华手里的糖果袋子里最少有12颗糖果。"
}
```
### 字段
```
instruction: 指令
input: 输入（本数据集均为空）
output: 输出
```
## 局限性和使用限制
我们要求开发者仅将我们开源的代码、数据、模型及后续衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。
由于数据是由*ChatGPT*生成的，未经严格验证，在事实性和其他方面还存在一些不足。因此，在使用此数据集时，请务必注意甄别。
本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目的开发者不承担任何责任。
***
# School Math 0.25M
## Contents
Includes approx. 250k Chinese school math quizzes and answers generated by BELLE, with steps to solve the quizzes.
Note: this subset was generated by *ChatGPT* and was not strictly verified. The quizzes or answers might contain errors. Please take this in mind when using this subset.
## Sample
```
{
"instruction": "题目：小华手里有一个装满糖果的袋子，共有12个，小明想知道里面有多少颗糖果，于是他问小华：“你手里的糖果袋子里有偶数个糖果吗？”小华回答：“有，而且多于10颗。”请问小华手里的糖果袋子里最少有几颗糖果？",
"input": "",
"output": "\n由题目可知：小华手里的糖果袋子里有偶数个糖果；\n又知道小华手里的糖果袋子里有多于10颗糖果。\n因为糖果数为偶数，多于10颗，所以糖果数最小必须是12。\n所以小华手里的糖果袋子里最少有12颗糖果。"
}
```
### Schema
```
instruction: 指令
input: 输入（本数据集均为空）
output: 输出
```
## Limitation and Usage Limits
We require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.
Since this dataset was generated by *ChatGPT* and was not strictly verified, it still has shortcomings regarding factuality and other aspects. When using this dataset, careful inspection is needed.
This dataset does not represent anyone's ground, interest or thought, and is not related to any kind of claim of any groups. The developers of this project do not assume any responsibility to potential harm inflicted by using this dataset and project. |
false |
# Dataset Card for sberquad
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/sberbank-ai/data-science-journey-2017
- **Paper:** https://arxiv.org/abs/1912.09723
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Sber Question Answering Dataset (SberQuAD) 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.
Russian original analogue presented in Sberbank Data Science Journey 2017.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Russian
## Dataset Structure
### Data Instances
```
{
"context": "Первые упоминания о строении человеческого тела встречаются в Древнем Египте...",
"id": 14754,
"qas": [
{
"id": 60544,
"question": "Где встречаются первые упоминания о строении человеческого тела?",
"answers": [{"answer_start": 60, "text": "в Древнем Египте"}],
}
]
}
```
### Data Fields
- id: a int32 feature
- title: a string feature
- context: a string feature
- question: a string feature
- answers: a dictionary feature containing:
- text: a string feature
- answer_start: a int32 feature
### Data Splits
| name |train |validation|test |
|----------|-----:|---------:|-----|
|plain_text|45328 | 5036 |23936|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@InProceedings{sberquad,
doi = {10.1007/978-3-030-58219-7_1},
author = {Pavel Efimov and
Andrey Chertok and
Leonid Boytsov and
Pavel Braslavski},
title = {SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis},
booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction},
year = {2020},
publisher = {Springer International Publishing},
pages = {3--15}
}
```
### Contributions
Thanks to [@alenusch](https://github.com/Alenush) for adding this dataset. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.